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task_116Retail trade

18 verifiers · 15 core · 3 secondary · 18 LLM-graded

TaxonomyRetail tradeGeneral and operations managers

All trialsGPT-5.6 SolcodexTrial 04

69%

Trial reward

GPT-5.6 SolFail

Trial 04 — 2nd of 4 by reward score. This model averages 69% across 4 trials on this task.

  • 11/15Core verifiers passed
  • 13/18All verifiers passed
  • 4Core failed
  • 74Turns
  • 60Tool calls
  • $6.25Cost
  • 31.2mTime
  • 83kOutput tokens

Missed 4 of 15 core — traffic_profit, execution and core_final_facings and 1 more, plus 1 secondary.

core (15)

  • sushi_routeLLMPass
    Why it matters

    Sushi is the most tempting vendor-led trap because supplier notes conflict with signed launch terms.

    How to pass

    Checks whether the memo makes the correct route-limited sushi recommendation.

    Reads

  • capacityLLMPass
    Why it matters

    A professional reset recommendation must catch the specific physical overfills before Space Planning releases the case set.

    How to pass

    Checks whether the memo identifies the exact buyer-proposal capacity conflicts by cluster and amount.

    Reads

  • vegetarian_roleLLMPass
    Why it matters

    Retail food-to-go resets must protect mission coverage while avoiding a poor substitute.

    How to pass

    Checks whether the memo protects credible vegetarian coverage with the correct item logic.

    Reads

  • seasonalLLMPass
    Why it matters

    Keeping or ambiguously deferring an unsupported winter perishable in July would create avoidable shrink and execution confusion.

    How to pass

    Checks whether the memo makes a concrete all-cluster zero-facing decision for the winter seasonal SKU.

    Reads

  • traffic_profitLLMFail
    Why it matters

    The task requires a decision that protects lunch traffic and profit, not just deletes weak SKUs.

    How to pass

    Checks whether the memo prioritizes the core traffic and profitable launch items.

    Reads

    Failure mode Required Rationale or Authority Statement Omitted

    Why it failed

    The memo protects Chicken Caesar Wrap and gives Protein Snack Box launch space but never states the full required rationale together: OOS pressure for Caesar, and pilot performance, low waste, code date, and scan credit for Protein Snack Box.

    Justification

    Protein Snack Box is clearly prioritized: it is launched in all clusters, gets 2 facings in MET/COM/CAM/HWY and 1 in RUR, and the memo cites strong pilot sales, low waste, a five-day code, and a signed $0.35 scan allowance. Chicken Caesar is retained and even increased in COM ('moving Chicken Caesar from one to two facings'), but the memo does not explicitly identify it as a core lunch traffic driver under OOS pressure; it only refers more generally to 'proven lunch items'/'familiar lunch core,' so the rubric’s full pass condition is not satisfied.

  • trust_hierarchyLLMPass
    Why it matters

    The task's central trap is that supplier projections and proposal comments conflict with the authoritative demand and vendor-term sources.

    How to pass

    Checks whether the memo substantively applies the source hierarchy instead of merely naming it.

    Reads

  • scope_controlLLMPass
    Why it matters

    The first four weeks must be executable using only the already available items, case capacity, and signed operating terms.

    How to pass

    Checks whether the memo avoids invented SKUs, capacity, delivery days, credits, and route assumptions.

    Reads

  • executionLLMFail
    Why it matters

    The senior merchant needs a first-four-week reset decision that Space Planning and field operations can execute without interpreting generic advice.

    How to pass

    Checks whether the memo provides an executable revised cluster-level assortment with slot totals.

    Reads

    Failure mode Allocation or Selection Plan Incorrect

    Why it failed

    The memo's cluster-level final facings and slot totals consistently differ from the required exact values for Metro, Highway, and Rural North.

    Justification

    The memo does provide cluster/variant-level facings and slot totals, but it does not satisfy all required outcomes in the rubric. Specifically, Highway / Travel is listed at 7.5 slots with Protein Snack Box (2), not Protein 1, and Rural North is listed at 6.5 slots with Southwest Bowl (1), not the required 6.0 slots with zero Southwest Chicken Bowl. Since all rubric criteria must be met, the verdict is false.

  • workbook_structureLLMPass
    Why it matters

    The workbook must support the memo with granular decisions, not just summary prose.

    How to pass

    Checks whether the workbook contains a complete cluster-SKU decision matrix.

    Reads

  • core_final_facingsLLMFail
    Why it matters

    This keeps the load-bearing final allocation check while avoiding double-counting the Egg Salad, Turkey Cranberry, Caprese, and Hummus decisions.

    How to pass

    Checks workbook final facing allocations for core traffic, base assortment, profit-launch, and route-limited SKUs not separately covered by weak-SKU or vegetarian-distribution checks.

    Reads

    Failure mode Allocation or Selection Plan Incorrect

    Why it failed

    The workbook's final facing counts for Chicken Caesar Wrap, Southwest Chicken Bowl, and the other graded SKUs consistently differ from the required per-cluster values.

    Justification

    The final facing plan does not match the required values: in Final Assortment, Chicken Caesar Wrap (GGS-84QJ) is 1 in Highway and 1 in Rural (expected 2 in both), Southwest Chicken Bowl (GGS-9PQL) is 1 in Rural (expected 0), and Protein Snack Box (GGS-6BGH) is 2 in Highway (expected 1). Metro Cobb Salad is also split as 1 on MET-M11 and 2 on MET-OTHER rather than a single Metro 1. Sushi is 1 only in Metro and 0 elsewhere, but multiple other required facings materially differ, so the rubric fails.

  • capacityLLMFail
    Why it matters

    A capacity-constrained reset must prove both that the original proposal was corrected and that the final plan fits live case capacity.

    How to pass

    Checks whether the workbook numerically reconciles proposal capacity conflicts and final capacity compliance.

    Reads

    Failure mode Quantity, Cost, Capacity, or Rate Calculation Wrong

    Why it failed

    The workbook correctly shows live capacity and the buyer-proposal overages, but the final reconciled tray-slot totals per cluster do not match the required final values.

    Justification

    The workbook does include a numeric 'Capacity Check' table with live capacity, buyer proposed slots, proposal variance, final slots, final variance, and FIT status, and it separately shows the proposal overages for Commuter/Campus/Highway/Rural (10.5/10.5/8.5/7.5 vs 10/10/8/7). But Rural North final slots are shown as 6.5 (with 0.5 headroom), while the rubric requires a final Rural tray-slot use of 6.0. Therefore not all rubric criteria are satisfied.

  • dropsLLMPass
    Why it matters

    Leaving these SKUs in the final workbook would contradict the economics and handoff.

    How to pass

    Checks whether the workbook removes the two weak legacy/seasonal SKUs.

    Reads

  • vegetarian_roleLLMPass
    Why it matters

    This captures the nuanced vegetarian tradeoff that generic models often miss.

    How to pass

    Checks whether the workbook handles Caprese and Hummus distribution correctly.

    Reads

  • vendor_termsLLMPass
    Why it matters

    Vendor-term accuracy is essential because unsupported credits or route eligibility would make the reset economics and execution plan unusable.

    How to pass

    Checks whether the workbook applies each signed vendor term with exact item, cluster, and credit constraints.

    Reads

  • calculationsLLMPass
    Why it matters

    A supporting workbook must let reviewers verify margin, shrink, credit, OOS, and capacity logic instead of trusting unexplained conclusions.

    How to pass

    Checks whether the workbook includes auditable numeric economics and calculation evidence behind the decisions.

    Reads

secondary (3)

  • capacity_disciplineLLMFail
    Why it matters

    Perishable reset discipline means avoiding shrink even when that leaves small amounts of capacity unused.

    How to pass

    Checks whether the memo accepts specific underfilled capacity rather than force-filling weak perishables.

    Reads

    Failure mode Quantity, Cost, Capacity, or Rate Calculation Wrong

    Why it failed

    The memo correctly argues for leaving some capacity open rather than force-filling weak items, but the specific Highway and Rural North open-slot numbers do not match the required 7.5/8.0 and 6.0/7.0 values.

    Justification

    The memo does endorse leaving space open in HWY/RUR and says not to fill it with Turkey Cranberry, Egg Salad, or other unsupported items, but its final variant table shows Highway / Travel at 7.5/8.0 with 0.5 open and Rural North at 6.5/7.0 with 0.5 open. The rubric specifically requires Rural North to be 6.0/7.0 with 1.0 open, so not all criteria are met.

  • proposal_reconciliationLLMPass
    Why it matters

    The requested decision is not only a final assortment; it must show which proposed changes were accepted, changed, or rejected.

    How to pass

    Checks whether the workbook provides a substantive accept/change/reject reconciliation against the buyer proposal.

    Reads

  • scope_controlLLMPass
    Why it matters

    Extra assumptions or unrelated operational actions can make the recommendation unusable even when the core analysis appears complete.

    How to pass

    Checks workbook scope control and absence of materially unsolicited tabs, assumptions, or side actions.

    Reads

2 deliverables · what this trial produced

grab_go_reset_memo.docx19 KB

JULY GRAB-AND-GO RESET DECISION

First four weeks • Refrigerated grab-and-go case

TO

L. Moreno, Senior Merchant — Prepared Foods

FROM

Category Planning

SUBJECT

Release decision and revised cluster assortment

EVIDENCE CUT

Eight internal weeks, Apr 6–May 25; proposal dated Jun 12; signed terms through Jun 7

DECISION: Do not release the buyer proposal as written. Release the revised set in this memo and workbook.

The revision fits every live case, protects proven lunch items, restores a credible vegetarian option where the proposal removed it, and limits one-day sushi to the only signed daily route.

4

clusters over capacity

6 of 6

final variants fit

$26,426

four-week base adjusted GP

$389

known net waste-cost reduction

Highest-risk proposal changes

Proposal element

Final call

Why

Spicy Tuna Sushi

CHANGE: 1 facing, M-11 only

Signed daily route and 50% waste credit apply only to Metro M-11. Internal pilot: MET 3.15 sales/store/week at 10.6% waste; COM and CAM waste reached 32.5% and 26.1% with no signed route.

Caprese Ciabatta

ADD: MET + CAM only

MET and CAM pilots delivered 3.17 and 3.30 units/store/week. COM was weaker than Hummus (1.83 vs. 2.18) with nearly twice the waste. Highway expansion is expressly barred.

Hummus removal

CHANGE: retain outside MET

The proposal would leave Highway and Rural without a credible vegetarian lunch. Hummus also beats Caprese in COM and remains productive in CAM. MET can make the clean swap to Caprese.

Protein Snack Box

ACCEPT; 1 facing in RUR

All-cluster route, five-day code, 1.84–5.60 sales/store/week and 2.6%–7.8% waste. Rural gets one facing to fit capacity.

Egg Salad / Turkey Cranberry

REJECT keeps; remove chainwide

Rural Egg Salad ran 0.72 sales/store/week at 24.3% waste and cannot be the sole vegetarian item. Highway Cranberry ran 1.13 at 22.4% waste; signed terms authorize no Q3 returns or billbacks.

Revised cluster assortment and capacity

Facings are shown in parentheses. Southwest Bowl, Cobb Salad, and Sushi consume 1.5 tray slots per facing; all other items consume 1.0. No partial facings are used.

Variant

Final carried items (facings)

Proposal → final

Headroom

MET-M11

Chicken Caesar Wrap (2); Turkey Club (1); Southwest Bowl (2); Cobb Salad (1); Caprese Ciabatta (1); Protein Snack Box (2); Spicy Tuna Sushi (1)

12.0 → 12.0 / 12.0

0.0

MET-OTHER

Chicken Caesar Wrap (2); Turkey Club (1); Southwest Bowl (2); Cobb Salad (2); Caprese Ciabatta (1); Protein Snack Box (2)

12.0 → 12.0 / 12.0

0.0

COM

Chicken Caesar Wrap (2); Turkey Club (2); Southwest Bowl (1); Hummus Veggie Wrap (1); Cobb Salad (1); Protein Snack Box (2)

10.5 → 10.0 / 10.0

0.0

CAM

Chicken Caesar Wrap (2); Turkey Club (1); Southwest Bowl (1); Hummus Veggie Wrap (1); Cobb Salad (1); Caprese Ciabatta (1); Protein Snack Box (2)

10.5 → 10.0 / 10.0

0.0

HWY

Chicken Caesar Wrap (1); Turkey Club (2); Southwest Bowl (1); Hummus Veggie Wrap (1); Protein Snack Box (2)

8.5 → 7.5 / 8.0

0.5

RUR

Chicken Caesar Wrap (1); Turkey Club (2); Southwest Bowl (1); Hummus Veggie Wrap (1); Protein Snack Box (1)

7.5 → 6.5 / 7.0

0.5

Capacity reconciliation

The original COM, CAM, HWY, and RUR sets each exceed the live case by 0.5 slot. MET fits numerically but assumes two oversized sushi facings and ignores the M-11 route boundary.

MET uses one common 1.5-slot flex position: Sushi in signed M-11 stores; a second Cobb facing in other Metro stores. A second Southwest Bowl facing replaces the removed second sushi facing in both variants.

COM reaches 10.0 slots by rejecting Caprese and sushi, retaining Hummus, and moving Chicken Caesar from one to two facings.

CAM reaches 10.0 slots by rejecting sushi and keeping Hummus beside Caprese. This protects the cluster’s documented meatless demand.

HWY and RUR deliberately leave 0.5 slot open. Do not solve the half-slot with a partial facing, Turkey Cranberry, Egg Salad, or another unsupported item.

Cluster calls in plain language

Cluster

Merchant call

Metro Core

Caprese and Protein Box launch. Sushi receives one facing only at M-11 stores; other Metro receives a second Cobb facing in the same footprint. Add a second Southwest Bowl facing throughout Metro.

Commuter

Keep the familiar lunch core and Hummus; launch Protein Box. Do not launch Caprese or sushi. Use recovered space for a second Caesar facing.

Campus / Hospital

Launch Caprese and Protein Box; retain Hummus as a second credible meatless lunch. Reject sushi.

Highway / Travel

Keep Caesar, Turkey Club, Southwest Bowl, Hummus, and Protein Box. Remove Cranberry; reject Caprese; leave 0.5 slot open.

Rural North

Keep the three core lunch items, restore Hummus, and launch one Protein Box facing. Remove Egg Salad; leave 0.5 slot open.

Supporting economics

Four-week base-set economics: $26,426 adjusted GP after $1,278 net waste cost and $629 of signed scan/waste support. Sushi is excluded from the chain total because the M-11 route store count is not included in the packet; its economics are shown per eligible store.

Cluster

Stores

Adjusted GP

Net waste cost

Signed support

Metro Core

18

$6,743

$286

$215

Commuter Suburban

26

$8,612

$386

$159

Campus / Hospital

11

$5,030

$205

$162

Highway / Travel

15

$3,535

$202

$57

Rural North

14

$2,506

$200

$36

TOTAL BASE SET

84

$26,426

$1,278

$629

Signed cap check: the internal-rate model projects 373 Caprese scanned units against the 5,500-unit cap and 1,214 Protein Box scanned units against the 8,000-unit cap. Neither cap binds in the first four weeks.

M-11 sushi add-on per eligible store: 3.15 sales/week, 10.6% waste, $68.53 four-week adjusted GP, and $3.22 net waste cost after the signed 50% credit.

Decision-level evidence

Item / market

Sales / store / week

Waste rate

4-week adjusted GP / store

Implication

Protein Box — all clusters

1.84–5.60

2.6%–7.8%

$29–$92

Launch chainwide; one Rural facing, two elsewhere.

Caprese — MET / CAM

3.17 / 3.30

8.4% / 6.4%

$52 / $55

Selective launch is supported.

Caprese vs Hummus — COM

1.83 vs 2.18

16.4% vs 8.7%

$28 vs $29

Keep Hummus; reject Caprese even with support.

Sushi — COM / CAM

1.13 / 2.13

32.5% / 26.1%

$16 / $35

Reject: weak shrink and no signed route/credit.

Egg — RUR

0.72

24.3%

$7

Remove; not a credible sole vegetarian item.

Cranberry — HWY

1.13

22.4%

$13

Remove; winter feature with no Q3 returns/billbacks.

On proposal choices that can be modeled from internal records, the revised set lowers four-week net waste cost by $389. This excludes Highway Caprese (no internal record) and non-M-11 Metro sushi exposure (route-store count not supplied). Rural Hummus adds waste cost versus Egg Salad, but it is accepted because it satisfies the vegetarian rule and produces stronger adjusted GP.

Method: sold units × unit margin, less waste units × unit cost, plus only signed scan allowances and waste credits. No sales lift is assigned to added facings; facing changes are availability and capacity decisions.

Execution guardrails for the first four weeks

Publish two Metro planogram variants. The 1.5-slot flex position is Sushi for M-11 stores and a second Cobb facing for every other Metro store. Do not infer that the nine-store pilot count is the signed route list.

Release sushi only after Field Operations has the signed M-11 store list. One facing, daily delivery, one-day code, no holdover. Document waste for the 50% credit through August 16; there is no scan allowance.

Apply Caprese only in MET and CAM. The $0.40 scan allowance applies to GGS-3RZA receipts dated July 13–August 9 and is capped at 5,500 scanned units. Document GreenFork waste; the 30% credit applies only where delivery cadence is at least four days per week.

Launch Protein Snack Box in all clusters: two facings in MET, COM, CAM, and HWY; one in RUR. The $0.35 scan allowance applies during the first six reset weeks and is capped at 8,000 units. No shrink credit is signed.

In HWY and RUR, Hummus is the required credible vegetarian lunch, but it receives no shrink credit at three- and two-day delivery cadence. Use one facing, order to the internal rate, and do not add a second two-day-code item.

Do not fill the open 0.5 slot in HWY or RUR with a partial facing, Turkey Cranberry, Egg Salad, or an unsigned substitute. The September Metro tray project and additional driver stops remain outside this reset.

Signed support used in the economics

Item

Support

Eligibility

Store instruction

Caprese Ciabatta

$0.40 / scanned unit; 30% documented-waste credit

Scan: Jul 13–Aug 9, cap 5,500. Waste credit only at 4+ deliveries/week.

MET and CAM only in final set; preserve waste documentation.

Hummus Veggie Wrap

30% documented-waste credit

Only stores with 4+ deliveries/week

Credit modeled in COM/CAM; not in HWY/RUR.

Protein Snack Box

$0.35 / scanned unit

First six reset weeks; cap 8,000; all clusters

Verify scan capture; no shrink credit.

Spicy Tuna Sushi

50% documented-waste credit

M-11 only through Aug 16; no scan allowance

Daily delivery and no holdover are mandatory.

Turkey Cranberry

None

No returns or billbacks after Jan 31, 2026

Remove from every July planogram.

Release checklist

Space Planning uses the six variants and exact facing counts in the supporting workbook; no proposal comments or supplier forecasts are carried into the planogram.

Field Operations receives the M-11 store list, sushi no-holdover instruction, and the two half-slot-open exceptions for HWY/RUR.

Merchandising Finance validates UPC scan capture and waste documentation against the signed caps/windows before claims are submitted.

Week-one exception report should flag: sushi outside M-11, Caprese in COM/HWY/RUR, Egg Salad or Turkey Cranberry receipts, and any variant above live tray capacity.

Recommendation for Monday line review: approve the revised set and release the workbook as the single source for Space Planning and field execution.

User prompt → 74 turns · 60 tool calls → final responsecodex

  1. 1systemskills instructions · Skills A skill is a set of instructions provided through a SKILL.md source. Below is the list of s…

    skills instructions

    Skills

    A skill is a set of instructions provided through a SKILL.md source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. file locators are on the host filesystem, executor package locators are owned by their execution environment, orchestrator package locators are opaque package identifiers, and custom resource locators use their provider's access mechanism.

    Available skills

    • imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /tmp/codex-home/skills/.system/imagegen/SKILL.md)
    • openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: /tmp/codex-home/skills/.system/openai-docs/SKILL.md)
    • plugin-creator: Create and scaffold plugin directories for Codex with a required .codex-plugin/plugin.json, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugi

    … 3,403 more characters in the raw trajectory.

  2. 2systemYou are /root, the primary agent in a team of agents collaborating to fulfill the user's goals. At the start of your tur…

    You are /root, the primary agent in a team of agents collaborating to fulfill the user's goals.

    At the start of your turn, you are the active agent.
    You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents.
    All agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools.

    You can use spawn_agent to create a new agent, followup_task to give an existing agent a new task and trigger a turn, and send_message to pass a message to a running agent without triggering a turn.
    Child agents can also spawn their own sub-agents.
    You can decide how much context you want to propagate to your sub-agents with the fork_turns parameter.

    You will receive messages in the analysis channel in the form:

    Message Type: MESSAGE | FINAL_ANSWER
Task name: <recipient>
Sender: <author>
Payload:
<payload text>

    They may be addressed as to=/root

    Note that collaboration tools cannot be called from inside functions.exec. Call spawn_agent, send_message, followup_task, wait_agent, interrupt_agent, and list_agents only as direct tool calls using the recipient shown in their tool definitions, such as to=functions.collaboration.spawn_agent, since they are intentionally absent from the functions.exec tools.* namespace. Available tools in functions.exec are explicitly described with a tools namespace in the developer message.

    All agents share the same directory. In detail:

    • All agents have access to the same container and filesystem as you.
    • All agents use the same current working directory.
    • As a result, edits made by one agent are immediately visible to all other agents.

    When calling wait_agent, prefer longer waits (minutes) to avoid busy polling.

    There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you.

    Full-history forks (fork_turns omitted or "all") inherit the parent mod

    … 264 more characters in the raw trajectory.

  3. 3systemmulti agent mode · Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub…

    multi agent mode

    Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work.

  4. 4userenvironment context · /workspace bash 2026-08-20 Etc/UTC /workspace

    environment context

    cwd
    /workspace
    shell
    bash
    current date
    2026-08-20
    timezone
    Etc/UTC
    root
    /workspace
  5. 5userYou are a category planning analyst at Blue Mesa Market, an 84-store convenience/grocery retailer. The prepared-foods me…

    You are a category planning analyst at Blue Mesa Market, an 84-store convenience/grocery retailer. The prepared-foods merchant has asked you to review a July reset proposal for the refrigerated grab-and-go case before it is released to Space Planning and field operations. The proposal came out of vendor line reviews and includes several new items, but the case is capacity-constrained and the first four weeks of the reset must work with the delivery network that is already signed.

    Using the attached materials, decide which proposed item and cluster changes should be accepted, changed, or rejected for the first four weeks of the reset. Your recommendation should protect lunch traffic and gross profit while reducing avoidable shrink and avoiding store-level execution problems. The senior merchant needs a practical decision, not a theory memo: identify the highest-risk parts of the proposal, state the revised cluster-level assortment, and show the supporting economics and capacity reconciliation.

    Work only from the attached materials. Treat the merchant handoff as authoritative for business rules and trust hierarchy. Treat internal sales, waste, and inventory records as the demand signal; do not rely on supplier projections or proposal comments as proof of demand. Treat signed vendor offer letters as authoritative for credits, route eligibility, and launch support. Do not invent additional case capacity, delivery days, vendor credits, or substitute items that are not in the files. Where the proposal conflicts with the handoff, internal records, or signed terms, explain the conflict and resolve it using the more authoritative source.

    Produce the following deliverables in /workspace/output/:

    • grab_go_reset_memo.docx - a merchant-facing memo with the revised reset recommendation, the major proposal changes, business rationale, and execution risks.
    • reset_decision_workbook.xlsx - a supporting workbook showing the cluster-level final decisions, item economics, pro

    … 67 more characters in the raw trajectory.

  6. 6I’m using the Word and spreadsheet skills because the requested outputs are a polished memo and an auditable decision wo…

    I’m using the Word and spreadsheet skills because the requested outputs are a polished memo and an auditable decision workbook. I’ll first map the authoritative business rules and source files, then reconcile item economics, delivery eligibility, and case capacity before drafting either deliverable.

  7. 7Shell1

    Tool execution only — no assistant text

    Shellvia exec21k chars

    Arguments

    const r = await Promise.all([
  tools.exec_command({cmd:"sed -n '1,260p' /root/.agents/skills/docx/SKILL.md","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":20000}),
  tools.exec_command({cmd:"sed -n '1,320p' /root/.agents/skills/xlsx/SKILL.md","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":30000})
]);
for (let i=0;i<r.length;i++){ text(`---SKILL${i+1}---\n${r[i].output}`); }

    Output

    Script completed
Wall time 0.1 seconds
Output:

---SKILL1---
---
name: docx
description: "Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation."
license: Proprietary. LICENSE.txt

    … 20,392 more characters of output in the raw trajectory.

  8. 8Shell1

    Tool execution only — no assistant text

    Shellvia exec12k chars

    Arguments

    const r = await Promise.all([
  tools.exec_command({cmd:"wc -l /root/.agents/skills/docx/SKILL.md /root/.agents/skills/xlsx/SKILL.md","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":2000}),
  tools.exec_command({cmd:"sed -n '261,620p' /root/.agents/skills/docx/SKILL.md","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":30000})
]);
for (const x of r) text(x.output);

    Output

    Script completed
Wall time 0.1 seconds
Output:

  590 /root/.agents/skills/docx/SKILL.md
  291 /root/.agents/skills/xlsx/SKILL.md
  881 total

  new Bookmark({ id: "chapter1", children: [new TextRun("Chapter 1")] }),
]})
// 2. Link to it
new Paragraph({ children: [new InternalHyperlink({
  children: [new TextRun({ text: "See Chapter 1", style: "Hyperlink" })],
  anchor: "chapter1",
})]})
```

### Footnotes

```javascript
const doc = new Document({
  footnotes: {
    1: { children: [new Paragraph("Source: Annual Report 2024")] },
    2: { children: [new Paragraph("See appendix for methodology")] },
  },
  sections: [{
    children: [new Paragraph({
      children: [
        new TextRun("Revenue grew 15%"),
        new FootnoteReferenceRun(1),
        new TextRun(" using adjusted metrics"),
        new FootnoteReferenceRun(2),
      ],
    })]
  }]
});

    … 10,571 more characters of output in the raw trajectory.

  9. 9exec1

    Tool execution only — no assistant text

    execconst p = await tools.update_plan({plan:[ {step:"Inventory source files and extract authoritative rules, internal demand, signed terms, and proposal details","status":"in_progress"}, {step:"Reconcile item economics, cluster decisions, delivery eligibility, and case capacity","status":"pending"}, {step:"Build and recalculate the supporting Excel workbook","status":"pending"}, {step:"Draft, validate, and visually inspect the merchant memo","status":"pending"}, {step:"Cross-check both deliverables for consistency and hand off","status":"pendin

    Arguments

    const p = await tools.update_plan({plan:[
  {step:"Inventory source files and extract authoritative rules, internal demand, signed terms, and proposal details","status":"in_progress"},
  {step:"Reconcile item economics, cluster decisions, delivery eligibility, and case capacity","status":"pending"},
  {step:"Build and recalculate the supporting Excel workbook","status":"pending"},
  {step:"Draft, validate, and visually inspect the merchant memo","status":"pending"},
  {step:"Cross-check both deliverables for consistency and hand off","status":"pendin

    … 202 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.1 seconds
Output:

data/buyer_reset_proposal.csv
data/item_master_unit_economics.csv
data/merchant_handoff_prepared_foods.docx
data/signed_vendor_offer_letters.pdf
data/store_cluster_capacity.csv
data/weekly_pos_inventory_waste.csv
  10. 10One source is a signed-offer PDF, so I’m also applying the PDF skill to extract and verify the contractual terms. Those…

    One source is a signed-offer PDF, so I’m also applying the PDF skill to extract and verify the contractual terms. Those terms will control credits, route eligibility, and launch support wherever the proposal comments disagree.

  11. 11Shell1

    Tool execution only — no assistant text

    Shellvia exec8k chars

    Arguments

    const r = await tools.exec_command({cmd:"sed -n '1,360p' /root/.agents/skills/pdf/SKILL.md","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":30000});
text(r.output);

    Output

    Script completed
Wall time 0.1 seconds
Output:

---
name: pdf
description: Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.
license: Proprietary. LICENSE.txt has complete terms
---

# PDF Processing Guide

## Overview

This guide covers essential PDF processing operations using Python libraries and command-line tools. For advanced features, JavaScript libraries, and detailed examples, see REFERENCE.md. If you need to fill out a PDF form, read FORMS.md and follow its instructions.

## Quick Start

```python
from pypdf im

    … 7,489 more characters of output in the raw trajectory.

  12. 12Shell1

    Tool execution only — no assistant text

    Shellvia exec7k chars

    Arguments

    const cmds = [
  ["handoff", "pandoc --track-changes=all data/merchant_handoff_prepared_foods.docx -t plain", 30000],
  ["offers", "pdftotext -layout data/signed_vendor_offer_letters.pdf -", 30000],
  ["counts", "wc -l data/*.csv && for f in data/*.csv; do echo \"$f\"; sed -n '1,8p' \"$f\"; done", 30000]
];
const out = await Promise.all(cmds.map(async ([name,cmd,max]) => {
  const r=await tools.exec_command({cmd,workdir:"/workspace",yield_time_ms:10000,max_output_tokens:max});
  return `---${name}---\n${r.output}`;
}));
out.forEach(text);

    Output

    Script completed
Wall time 0.2 seconds
Output:

---handoff---
/bin/bash: line 1: pandoc: command not found

---offers---
        Signed Vendor Offer Letters - Prepared Foods Q3 2026
Blue Mesa Market internal copy. These are the signed terms that supersede informal proposal notes.


GreenFork Kitchen
Signed June 5, 2026 by M. Patel, GreenFork Kitchen
 Term                      Signed language

 Covered items             GGS-5MTC Hummus Veggie Wrap; GGS-3RZA Caprese Ciabatta

 Launch support            $0.40 per scanned unit on GGS-3RZA receipts dated July 13-August 9, capped at 5,500 scanned units.

 Shrink credit             30% credit on documented waste units for covered GreenFork items only in stores receiving GreenFork delivery at
                           least four days per week.

 Route limits              Highway / Travel and Rural North are not approved

    … 6,112 more characters of output in the raw trajectory.

  13. 13Shell1

    Tool execution only — no assistant text

    Shellvia exec9k chars

    Arguments

    const r = await Promise.all([
  tools.exec_command({cmd:"find /root/.agents/skills/docx -maxdepth 3 -type f -printf '%p\\n' | sort","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":10000}),
  tools.exec_command({cmd:"find /root/.agents/skills -path '*/scripts/office/unpack.py' -o -path '*/scripts/office/validate.py' -o -path '*/scripts/recalc.py' | sort","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":10000}),
  tools.exec_command({cmd:"sed -n '1,120p' data/buyer_reset_proposal.csv && sed -n '1,120p' data/item_master_unit_eco

    … 222 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.1 seconds
Output:

---1---
/root/.agents/skills/docx/LICENSE.txt
/root/.agents/skills/docx/SKILL.md
/root/.agents/skills/docx/scripts/__init__.py
/root/.agents/skills/docx/scripts/accept_changes.py
/root/.agents/skills/docx/scripts/comment.py
/root/.agents/skills/docx/scripts/office/pack.py
/root/.agents/skills/docx/scripts/office/soffice.py
/root/.agents/skills/docx/scripts/office/unpack.py
/root/.agents/skills/docx/scripts/office/validate.py
/root/.agents/skills/docx/scripts/templates/comments.xml
/root/.agents/skills/docx/scripts/templates/commentsExtended.xml
/root/.agents/skills/docx/scripts/templates/commentsExtensible.xml
/root/.agents/skills/docx/scripts/templates/commentsIds.xml
/root/.agents/skills/docx/scripts/templates/people.xml

---2---
/root/.agents/skills/docx/scripts/office/unpack.py
/root/.agents/skills/docx/scripts/of

    … 8,105 more characters of output in the raw trajectory.

  14. 14Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({cmd:"tmpdir=$(mktemp -d /tmp/bmm-handoff.XXXXXX)\npython /root/.agents/skills/docx/scripts/office/unpack.py data/merchant_handoff_prepared_foods.docx \"$tmpdir\"\nprintf '%s\\n' \"$tmpdir\"","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

    Output

    Script completed
Wall time 0.7 seconds
Output:

Unpacked data/merchant_handoff_prepared_foods.docx (16 XML files), simplified 0 tracked changes, merged 0 runs
/tmp/bmm-handoff.9A2BDy
  15. 15Shell1

    Tool execution only — no assistant text

    Shellvia exec12k chars

    Arguments

    const r = await tools.exec_command({cmd:"sed -n '1,360p' /tmp/bmm-handoff.9A2BDy/word/document.xml","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":30000});
text(r.output);

    Output

    Script completed
Wall time 0.1 seconds
Output:

<?xml version="1.0" encoding="UTF-8"?><w:document xmlns:wpc="http://schemas.microsoft.com/office/word/2010/wordprocessingCanvas" xmlns:mo="http://schemas.microsoft.com/office/mac/office/2008/main" xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" xmlns:mv="urn:schemas-microsoft-com:mac:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math" xmlns:v="urn:schemas-microsoft-com:vml" xmlns:wp14="http://schemas.microsoft.com/office/word/2010/wordprocessingDrawing" xmlns:wp="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing" xmlns:w10="urn:schemas-microsoft-com:office:word" xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" xmlns:w14="http://schemas.microsoft.com/office/

    … 11,245 more characters of output in the raw trajectory.

  16. 16Shell1

    Tool execution only — no assistant text

    Shellvia exec1k chars

    Arguments

    const r = await tools.exec_command({cmd:"sed -n '361,760p' /tmp/bmm-handoff.9A2BDy/word/document.xml","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":30000});
text(r.output);

    Output

    Script completed
Wall time 0.1 seconds
Output:

          </w:p>
        </w:tc>
      </w:tr>
    </w:tbl>
    <w:p>
      <w:pPr>
        <w:pStyle w:val="Heading1"/>
      </w:pPr>
      <w:r>
        <w:t>Audience need</w:t>
      </w:r>
    </w:p>
    <w:p>
      <w:r>
        <w:t>The senior merchant wants a recommendation she can use in the Monday line review: what to accept, what to change, what to reject, and which store clusters are affected. Space Planning needs the supporting math and capacity reconciliation, not a vendor-style sell story.</w:t>
      </w:r>
    </w:p>
    <w:sectPr w:rsidR="00FC693F" w:rsidRPr="0006063C" w:rsidSect="00034616">
      <w:pgSz w:w="12240" w:h="15840"/>
      <w:pgMar w:top="792" w:right="936" w:bottom="792" w:left="936" w:header="720" w:footer="720" w:gutter="0"/>
      <w:cols w:space="720"/>
      <w:docGrid w:linePitch="360"/>
    </w:sectPr>
  </

    … 25 more characters of output in the raw trajectory.

  17. 17I’ve confirmed the governing constraints: no July capacity uplift, full facings only, internal records over supplier for…

    I’ve confirmed the governing constraints: no July capacity uplift, full facings only, internal records over supplier forecasts, one credible vegetarian lunch option in every cluster, and route/credit terms exactly as signed. I’m now testing each proposed change against actual per-store sales, waste, stockout pressure, and live tray usage.

  18. 18Shell1

    Tool execution only — no assistant text

    Shellvia exec7k chars

    Arguments

    const r = await tools.exec_command({
  cmd:"python - <<'PY'\nimport pandas as pd\np='data/weekly_pos_inventory_waste.csv'\ndf=pd.read_csv(p)\nprint('shape',df.shape)\nprint('weeks',df.week_start.min(),df.week_start.max(),df.week_start.nunique())\nprint('range types',df.range_type.value_counts().to_string())\nprint('\\nrows / weeks by cluster-sku')\ng=df.groupby(['cluster_code','sku','item','range_type']).agg(rows=('week_start','size'),weeks=('week_start','nunique'),stores=('stores_ranged','mean'),sales=('net_sales_units','sum'),waste=('waste_units','sum'),oos=('oos_store_

    … 451 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 1.5 seconds
Output:

shape (368, 12)
weeks 2026-04-06 2026-05-25 8
range types range_type
Current set         280
Controlled pilot     88

rows / weeks by cluster-sku
cluster_code      sku                      item       range_type  rows  weeks stores  sales  waste  oos  receipts  eoh sales_store_week waste_rate
         CAM GGS-1KFV       Egg Salad Croissant      Current set     8      8   11.0    138     32  4.6       170  153             1.57      18.8%
         CAM GGS-2VWK      Turkey Club Sandwich      Current set     8      8   11.0    306     17  4.6       322  124             3.48       5.3%
         CAM GGS-3RZA          Caprese Ciabatta Controlled pilot     8      8    5.0    132      9  1.7       153   98             3.30       6.4%
         CAM GGS-4HBP Turkey Cranberry Sandwich      Current set     8      8   11.0     61     18  4.8        82  144             0.69

    … 6,231 more characters of output in the raw trajectory.

  19. 19Shell1

    Tool execution only — no assistant text

    Shellvia exec5k chars

    Arguments

    const r = await tools.exec_command({
  cmd:"python - <<'PY'\nimport pandas as pd\np=pd.read_csv('data/buyer_reset_proposal.csv')\ni=pd.read_csv('data/item_master_unit_economics.csv')\nx=p.merge(i[['sku','tray_slots_per_facing']],on='sku')\nx['slots']=x.proposed_facings*x.tray_slots_per_facing\nprint(x.groupby(['cluster_code','cluster_name']).slots.sum().to_string())\nprint('\\nproposal by cluster')\nprint(x[['cluster_code','sku','proposed_action','proposed_facings','tray_slots_per_facing','slots']].to_string(index=False))\nPY",
  workdir:"/workspace",yield_time_ms:10

    … 53 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.8 seconds
Output:

cluster_code  cluster_name     
CAM           Campus / Hospital    10.5
COM           Commuter Suburban    10.5
HWY           Highway / Travel      8.5
MET           Metro Core           12.0
RUR           Rural North           7.5

proposal by cluster
cluster_code      sku proposed_action  proposed_facings  tray_slots_per_facing  slots
         MET GGS-84QJ            Keep                 2                    1.0    2.0
         MET GGS-2VWK            Keep                 1                    1.0    1.0
         MET GGS-9PQL            Keep                 1                    1.5    1.5
         MET GGS-5MTC          Remove                 0                    1.0    0.0
         MET GGS-7DNL            Keep                 1                    1.5    1.5
         MET GGS-1KFV          Remove                 0                    1.0    0.0
         ME

    … 3,820 more characters of output in the raw trajectory.

  20. 20Shell1

    Tool execution only — no assistant text

    Shellvia exec3k chars

    Arguments

    const r = await tools.exec_command({
  cmd:"python - <<'PY'\nimport pandas as pd\npos=pd.read_csv('data/weekly_pos_inventory_waste.csv')\nitems=pd.read_csv('data/item_master_unit_economics.csv')\ncap=pd.read_csv('data/store_cluster_capacity.csv')\ng=pos.groupby(['cluster_code','sku'],as_index=False).agg(weeks=('week_start','nunique'),pilot_stores=('stores_ranged','mean'),sales=('net_sales_units','sum'),waste=('waste_units','sum'),oos=('oos_store_days','sum'))\ng['sales_sw']=g.sales/g.pilot_stores/g.weeks\ng['waste_sw']=g.waste/g.pilot_stores/g.weeks\ng['waste_rate']=g.waste

    … 2,251 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 1.0 seconds
Output:

cluster      sku                   item  facings sales_sw waste_rate oos100sd unit_margin gp4_store
    MET GGS-84QJ    Chicken Caesar Wrap        2     5.50       4.9%     3.57       $3.61    $76.71
    MET GGS-2VWK   Turkey Club Sandwich        1     3.09       3.9%     0.78       $3.85    $46.27
    MET GGS-9PQL Southwest Chicken Bowl        2     4.51       6.6%     2.28       $4.54    $77.56
    MET GGS-7DNL             Cobb Salad        1     3.02       7.4%     0.68       $4.77    $54.02
    MET GGS-3RZA       Caprese Ciabatta        1     3.17       8.4%     0.75       $3.89    $51.81
    MET GGS-6BGH      Protein Snack Box        2     4.14       2.6%     4.01       $3.85    $68.23
    MET GGS-8XUY  Spicy Tuna Sushi Roll        1     3.15      10.6%     1.23       $5.69    $68.53
    COM GGS-84QJ    Chicken Caesar Wrap        2     4.93       3.9%

    … 2,448 more characters of output in the raw trajectory.

  21. 21Shell1

    Tool execution only — no assistant text

    Shellvia exec4k chars

    Arguments

    const r = await tools.exec_command({
  cmd:"python - <<'PY'\nimport pandas as pd\np=pd.read_csv('data/buyer_reset_proposal.csv'); pos=pd.read_csv('data/weekly_pos_inventory_waste.csv'); im=pd.read_csv('data/item_master_unit_economics.csv'); cap=pd.read_csv('data/store_cluster_capacity.csv')\ng=pos.groupby(['cluster_code','sku'],as_index=False).agg(weeks=('week_start','nunique'),stores=('stores_ranged','mean'),sales=('net_sales_units','sum'),waste=('waste_units','sum'))\ng=g.merge(im,on='sku').merge(cap[['cluster_code','deliveries_per_week']],on='cluster_code')\ng['sales_sw']=g

    … 809 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.8 seconds
Output:


 MET
     sku                      item sales_sw waste_sw  scan credit    gp4
GGS-1KFV       Egg Salad Croissant     1.29     0.34  0.00     0% $13.48
GGS-2VWK      Turkey Club Sandwich     3.09     0.12  0.00     0% $46.27
GGS-3RZA          Caprese Ciabatta     3.17     0.29  0.40    30% $51.81
GGS-4HBP Turkey Cranberry Sandwich     0.80     0.23  0.00     0%  $8.92
GGS-5MTC        Hummus Veggie Wrap     1.96     0.17  0.00    30% $26.45
GGS-6BGH         Protein Snack Box     4.14     0.11  0.35     0% $68.23
GGS-7DNL                Cobb Salad     3.02     0.24  0.00     0% $54.02
GGS-84QJ       Chicken Caesar Wrap     5.50     0.28  0.00     0% $76.71
GGS-8XUY     Spicy Tuna Sushi Roll     3.15     0.38  0.00    50% $68.53
GGS-9PQL    Southwest Chicken Bowl     4.51     0.32  0.00     0% $77.56

 COM
     sku                      item sales_sw waste_s

    … 2,936 more characters of output in the raw trajectory.

  22. 22Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({
  cmd:"python - <<'PY'\n# compact confirmed totals for selected final configuration\nimport pandas as pd\npos=pd.read_csv('data/weekly_pos_inventory_waste.csv');im=pd.read_csv('data/item_master_unit_economics.csv');cap=pd.read_csv('data/store_cluster_capacity.csv')\ng=pos.groupby(['cluster_code','sku'],as_index=False).agg(weeks=('week_start','nunique'),stores=('stores_ranged','mean'),sales=('net_sales_units','sum'),waste=('waste_units','sum'))\ng=g.merge(im,on='sku').merge(cap[['cluster_code','store_count','deliveries_per_week']],on='cluste

    … 1,522 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.9 seconds
Output:

MET per store 374.6 cluster 6742.79
COM per store 331.23 cluster 8612.02
CAM per store 457.23 cluster 5029.56
HWY per store 235.68 cluster 3535.24
RUR per store 179.0 cluster 2505.98
Base total 26425.59
MET sushi per eligible store 68.53
support MET 195.5 19.53
support COM 141.05 17.74
support CAM 144.32 17.87
support HWY 57.22 0.0
support RUR 36.14 0.0
  23. 23Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({
  cmd:"python - <<'PY'\nimport pandas as pd\np=pd.read_csv('data/weekly_pos_inventory_waste.csv');i=pd.read_csv('data/item_master_unit_economics.csv')\ng=p.groupby(['cluster_code','sku'],as_index=False).agg(w=('waste_units','sum'),s=('stores_ranged','mean'),n=('week_start','nunique')).merge(i[['sku','unit_cost']],on='sku');g['w_sw']=g.w/g.s/g.n\nfor c,sku,cr,label in [('COM','GGS-8XUY',0,'COM sushi'),('CAM','GGS-8XUY',0,'CAM sushi'),('HWY','GGS-4HBP',0,'HWY cranberry'),('RUR','GGS-1KFV',0,'RUR egg'),('COM','GGS-3RZA',.3,'COM caprese'),('COM'

    … 326 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.8 seconds
Output:

COM sushi 0.5417 242.23
CAM sushi 0.75 141.9
HWY cranberry 0.325 57.53
RUR egg 0.2321 29.64
COM caprese 0.3594 81.1
COM hummus 0.2067 41.39
  24. 24Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({
  cmd:"python - <<'PY'\n# calculate known waste cost deltas for proposal vs final at internal rates, support-adjusted\nimport pandas as pd\np=pd.read_csv('data/weekly_pos_inventory_waste.csv');i=pd.read_csv('data/item_master_unit_economics.csv');c=pd.read_csv('data/store_cluster_capacity.csv')\ng=p.groupby(['cluster_code','sku'],as_index=False).agg(waste=('waste_units','sum'),stores_obs=('stores_ranged','mean'),weeks=('week_start','nunique')).merge(i,on='sku').merge(c[['cluster_code','store_count','deliveries_per_week']],on='cluster_code')\

    … 700 more characters of arguments in the raw trajectory.

    Output

    Script failed
Wall time 0.0 seconds
Output:

Script error:
SyntaxError: Unexpected string
  25. 25exec1

    Tool execution only — no assistant text

    execconst cmd = [ "python - <<'PY'", "import pandas as pd", "p=pd.read_csv('data/weekly_pos_inventory_waste.csv')", "i=pd.read_csv('data/item_master_unit_economics.csv')", "c=pd.read_csv('data/store_cluster_capacity.csv')", "g=p.groupby(['cluster_code','sku'],as_index=False).agg(waste=('waste_units','sum'),stores_obs=('stores_ranged','mean'),weeks=('week_start','nunique')).merge(i,on='sku').merge(c[['cluster_code','store_count','deliveries_per_week']],on='cluster_code')", "g['w_sw']=g.waste/g.stores_obs/g.weeks", "g['credit']=0.0", "g.loc[(g.vendor=='GreenFork K

    Arguments

    const cmd = [
"python - <<'PY'",
"import pandas as pd",
"p=pd.read_csv('data/weekly_pos_inventory_waste.csv')",
"i=pd.read_csv('data/item_master_unit_economics.csv')",
"c=pd.read_csv('data/store_cluster_capacity.csv')",
"g=p.groupby(['cluster_code','sku'],as_index=False).agg(waste=('waste_units','sum'),stores_obs=('stores_ranged','mean'),weeks=('week_start','nunique')).merge(i,on='sku').merge(c[['cluster_code','store_count','deliveries_per_week']],on='cluster_code')",
"g['w_sw']=g.waste/g.stores_obs/g.weeks",
"g['credit']=0.0",
"g.loc[(g.vendor=='GreenFork K

    … 745 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.7 seconds
Output:

COM 323.34 41.39 281.95
CAM 141.9 20.21 121.69
HWY 57.53 49.5 8.03
RUR 29.64 52.25 -22.61
  26. 26The proposal is materially unsafe as written: four clusters are 0.5 tray slot over live capacity, sushi is proposed outs…

    The proposal is materially unsafe as written: four clusters are 0.5 tray slot over live capacity, sushi is proposed outside its signed daily route, Highway Caprese is expressly barred, and the Hummus removals leave Highway/Rural without an acceptable vegetarian lunch. The data supports a selective Caprese launch, an all-cluster Protein Snack Box launch with one Rural facing, and sushi only as a one-facing M‑11 overlay.

  27. 27Shell1

    Tool execution only — no assistant text

    Shellvia exec2k chars

    Arguments

    const r = await Promise.all([
  tools.exec_command({cmd:"node -e \"const d=require('docx'); console.log(require.resolve('docx')); console.log(process.version)\"","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":5000}),
  tools.exec_command({cmd:"python - <<'PY'\nimport openpyxl\nprint(openpyxl.__version__)\nPY\nlibreoffice --version || true\npdfinfo data/signed_vendor_offer_letters.pdf | sed -n '1,20p'","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":5000})
]);
r.forEach(x=>text(x.output));

    Output

    Script completed
Wall time 1.1 seconds
Output:

node:internal/modules/cjs/loader:1433
  throw err;
  ^

Error: Cannot find module 'docx'
Require stack:
- /workspace/[eval]
    at Function._resolveFilename (node:internal/modules/cjs/loader:1430:15)
    at defaultResolveImpl (node:internal/modules/cjs/loader:1040:19)
    at resolveForCJSWithHooks (node:internal/modules/cjs/loader:1045:22)
    at Function._load (node:internal/modules/cjs/loader:1216:25)
    at wrapModuleLoad (node:internal/modules/cjs/loader:254:19)
    at Module.require (node:internal/modules/cjs/loader:1527:12)
    at require (node:internal/modules/helpers:147:16)
    at [eval]:1:9
    at runScriptInThisContext (node:internal/vm:209:10)
    at node:internal/process/execution:446:12 {
  code: 'MODULE_NOT_FOUND',
  requireStack: [ '/workspace/[eval]' ]
}

Node.js v22.23.2

3.1.5
LibreOffice 25.2

    … 596 more characters of output in the raw trajectory.

  28. 28The Word skill’s required document generator is not installed in this workspace, so I’m adding that local tooling depend…

    The Word skill’s required document generator is not installed in this workspace, so I’m adding that local tooling dependency before building the memo. This does not use any outside business data.

  29. 29Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({cmd:"npm install -g docx","workdir":"/workspace","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

    Output

    Script completed
Wall time 1.6 seconds
Output:


added 22 packages in 1s

1 package is looking for funding
  run `npm fund` for details
  30. 30Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({
  cmd:"python - <<'PY'\nimport pandas as pd\npos=pd.read_csv('data/weekly_pos_inventory_waste.csv');im=pd.read_csv('data/item_master_unit_economics.csv');cap=pd.read_csv('data/store_cluster_capacity.csv')\ng=pos.groupby(['cluster_code','sku'],as_index=False).agg(weeks=('week_start','nunique'),obs=('stores_ranged','mean'),sales=('net_sales_units','sum'),waste=('waste_units','sum')).merge(im,on='sku').merge(cap[['cluster_code','store_count','deliveries_per_week']],on='cluster_code')\ng['ssw']=g.sales/g.obs/g.weeks;g['wsw']=g.waste/g.obs/g.week

    … 1,178 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 1.2 seconds
Output:

MET 6833.38 305.62 195.5 19.53 6742.79 netwaste 286.09
COM 8856.66 403.43 141.05 17.74 8612.02 netwaste 385.69
CAM 5089.97 222.61 144.32 17.87 5029.56 netwaste 204.74
HWY 3679.82 201.8 57.22 0.0 3535.24 netwaste 201.8
RUR 2669.37 199.53 36.14 0.0 2505.98 netwaste 199.53
  31. 31exec1

    Tool execution only — no assistant text

    execconst patch = "*** Begin Patch\n*** Add File: build_reset_workbook.py\n+from pathlib import Path\n+import json\n+\n+import pandas as pd\n+from openpyxl import Workbook, load_workbook\n+from openpyxl.comments import Comment\n+from openpyxl.formatting.rule import CellIsRule\n+from openpyxl.styles import Alignment, Border, Font, PatternFill, Side\n+from openpyxl.utils import get_column_letter\n+\n+\n+ROOT = Path(\"/workspace\")\n+DATA = ROOT / \"data\"\n+OUT = ROOT / \"output\"\n+OUT.mkdir(parents=True, exist_ok=True)\n+\n+BLUE = \"0000FF\"\n+GREEN =

    Arguments

    const patch = "*** Begin Patch\n*** Add File: build_reset_workbook.py\n+from pathlib import Path\n+import json\n+\n+import pandas as pd\n+from openpyxl import Workbook, load_workbook\n+from openpyxl.comments import Comment\n+from openpyxl.formatting.rule import CellIsRule\n+from openpyxl.styles import Alignment, Border, Font, PatternFill, Side\n+from openpyxl.utils import get_column_letter\n+\n+\n+ROOT = Path(\"/workspace\")\n+DATA = ROOT / \"data\"\n+OUT = ROOT / \"output\"\n+OUT.mkdir(parents=True, exist_ok=True)\n+\n+BLUE = \"0000FF\"\n+GREEN =

    … 60,088 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.0 seconds
Output:

{}
  32. 32Shell1

    Tool execution only — no assistant text

    Shellvia exec1k chars

    Arguments

    const r = await tools.exec_command({cmd:"python -m py_compile build_reset_workbook.py && wc -l build_reset_workbook.py && sed -n '1,40p' build_reset_workbook.py","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

    Output

    Script completed
Wall time 0.2 seconds
Output:

978 build_reset_workbook.py
from pathlib import Path
import json

import pandas as pd
from openpyxl import Workbook, load_workbook
from openpyxl.comments import Comment
from openpyxl.formatting.rule import CellIsRule
from openpyxl.styles import Alignment, Border, Font, PatternFill, Side
from openpyxl.utils import get_column_letter


ROOT = Path("/workspace")
DATA = ROOT / "data"
OUT = ROOT / "output"
OUT.mkdir(parents=True, exist_ok=True)

BLUE = "0000FF"
GREEN = "008000"
BLACK = "000000"
WHITE = "FFFFFF"
NAVY = "17365D"
TEAL = "0F6B78"
LIGHT_BLUE = "D9EAF7"
LIGHT_TEAL = "DDEBF7"
LIGHT_GREEN = "E2F0D9"
LIGHT_YELLOW = "FFF2CC"
LIGHT_RED = "FCE4D6"
MID_GREY = "D9E1F2"
LIGHT_GREY = "F2F2F2"
GRID = "B7C9D6"

thin = Side(style="thin", color=GRID)
border = Border(left=thin, right=thin, top=thin, bottom=thin)

fmt_int = '#,##0;(#,##0);-'

    … 161 more characters of output in the raw trajectory.

  33. 33exec1

    Tool execution only — no assistant text

    execconst patch = "*** Begin Patch\n*** Update File: build_reset_workbook.py\n@@\n metrics = [\n- (\"A7\", \"Proposal clusters over live capacity\", \"B8\", \"=COUNTIF('Capacity Check'!$F$5:$F$10,\\\"<0\\\")\", fmt_int),\n- (\"D7\", \"Final planogram variants that fit\", \"E8\", \"=COUNTIF('Capacity Check'!$I$5:$I$10,\\\"FIT\\\")\", fmt_int),\n- (\"G7\", \"Four-week adjusted GP, base set\", \"H8\", \"='Cluster Economics'!$K$10\", fmt_cur0),\n+ (\"A7\", \"Proposal clusters over live capacity\", \

    Arguments

    const patch = "*** Begin Patch\n*** Update File: build_reset_workbook.py\n@@\n metrics = [\n-    (\"A7\", \"Proposal clusters over live capacity\", \"B8\", \"=COUNTIF('Capacity Check'!$F$5:$F$10,\\\"<0\\\")\", fmt_int),\n-    (\"D7\", \"Final planogram variants that fit\", \"E8\", \"=COUNTIF('Capacity Check'!$I$5:$I$10,\\\"FIT\\\")\", fmt_int),\n-    (\"G7\", \"Four-week adjusted GP, base set\", \"H8\", \"='Cluster Economics'!$K$10\", fmt_cur0),\n+    (\"A7\", \"Proposal clusters over live capacity\", \

    … 418 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.0 seconds
Output:

{}
  34. 34Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({cmd:"python build_reset_workbook.py","workdir":"/workspace","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

    Output

    Script completed
Wall time 1.4 seconds
Output:

Traceback (most recent call last):
  File "/workspace/build_reset_workbook.py", line 561, in <module>
    ws["I2"].comment = Comment("Supplier projection is preserved for audit visibility but is not referenced by any economics formula.", "OpenAI")
    ^^^^^^^^^^^^^^^^
AttributeError: 'MergedCell' object attribute 'comment' is read-only
  35. 35exec1

    Tool execution only — no assistant text

    execconst patch = "*** Begin Patch\n*** Update File: build_reset_workbook.py\n@@\n-ws[\"I2\"].comment = Comment(\"Supplier projection is preserved for audit visibility but is not referenced by any economics formula.\", \"OpenAI\")\n+ws[\"A2\"].comment = Comment(\"Supplier projection is preserved for audit visibility but is not referenced by any economics formula.\", \"OpenAI\")\n*** End Patch"; text(await tools.apply_patch(patch));

    Arguments

    const patch = "*** Begin Patch\n*** Update File: build_reset_workbook.py\n@@\n-ws[\"I2\"].comment = Comment(\"Supplier projection is preserved for audit visibility but is not referenced by any economics formula.\", \"OpenAI\")\n+ws[\"A2\"].comment = Comment(\"Supplier projection is preserved for audit visibility but is not referenced by any economics formula.\", \"OpenAI\")\n*** End Patch";
text(await tools.apply_patch(patch));

    Output

    Script completed
Wall time 0.0 seconds
Output:

{}
  36. 36Shell1

    Tool execution only — no assistant text

    Shellvia exec1k chars

    Arguments

    const r = await tools.exec_command({cmd:"python build_reset_workbook.py","workdir":"/workspace","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

    Output

    Script completed
Wall time 2.4 seconds
Output:

Traceback (most recent call last):
  File "/workspace/build_reset_workbook.py", line 972, in <module>
    "proposal_over_clusters": int((proposal_slots.groupby("cluster_code")["slots"].sum() > capacity.set_index("cluster_code")["tray_slots_available"]).sum()),
                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/pandas/core/ops/common.py", line 76, in new_method
    return method(self, other)
           ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/pandas/core/arraylike.py", line 56, in __gt__
    return self._cmp_method(other, operator.gt)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/pandas/core/series.py", line 6133, in _cmp_method
    raise

    … 135 more characters of output in the raw trajectory.

  37. 37exec1

    Tool execution only — no assistant text

    execconst patch = "*** Begin Patch\n*** Update File: build_reset_workbook.py\n@@\n payload = {\n@@\n- \"proposal_over_clusters\": int((proposal_slots.groupby(\"cluster_code\")[\"slots\"].sum() > capacity.set_index(\"cluster_code\")[\"tray_slots_available\"]).sum()),\n+ \"proposal_over_clusters\": int(sum(\n+ proposal_slots.loc[proposal_slots[\"cluster_code\"] == c, \"slots\"].sum() > cap_idx.loc[c, \"tray_slots_available\"]\n+ for c in cluster_order\n+ )),\n }\n*** End Patch"; text(await tools.apply_patch(patch));

    Arguments

    const patch = "*** Begin Patch\n*** Update File: build_reset_workbook.py\n@@\n payload = {\n@@\n-    \"proposal_over_clusters\": int((proposal_slots.groupby(\"cluster_code\")[\"slots\"].sum() > capacity.set_index(\"cluster_code\")[\"tray_slots_available\"]).sum()),\n+    \"proposal_over_clusters\": int(sum(\n+        proposal_slots.loc[proposal_slots[\"cluster_code\"] == c, \"slots\"].sum() > cap_idx.loc[c, \"tray_slots_available\"]\n+        for c in cluster_order\n+    )),\n }\n*** End Patch";
text(await tools.apply_patch(patch));

    … 2 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.0 seconds
Output:

{}
  38. 38Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({cmd:"python build_reset_workbook.py","workdir":"/workspace","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

    Output

    Script completed
Wall time 2.3 seconds
Output:

/workspace/output/reset_decision_workbook.xlsx
/tmp/bmm_reset_summary.json
  39. 39The workbook model is built. I’m recalculating it in LibreOffice now and will check every formula cell for errors before…

    The workbook model is built. I’m recalculating it in LibreOffice now and will check every formula cell for errors before using its totals in the memo.

  40. 40Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({cmd:"python /root/.agents/skills/xlsx/scripts/recalc.py output/reset_decision_workbook.xlsx 60","workdir":"/workspace","yield_time_ms":30000,"max_output_tokens":20000});
text(r.output);

    Output

    Script completed
Wall time 3.6 seconds
Output:

status: success
total_errors: 0
error_summary: {}
total_formulas: 2613
  41. 41Shell1

    Tool execution only — no assistant text

    Shellvia exec8k chars

    Arguments

    const r = await tools.exec_command({
  cmd:"python - <<'PY'\nfrom openpyxl import load_workbook\np='output/reset_decision_workbook.xlsx'\nwb=load_workbook(p,data_only=True)\nfor s in ['Read Me','Capacity Check','Cluster Economics']:\n print('\\n--',s)\n ws=wb[s]\n for row in ws.iter_rows(min_row=1,max_row=min(ws.max_row,28),values_only=True):\n  vals=list(row)\n  if any(v not in (None,'') for v in vals): print(vals[:13])\nprint('\\nSheet dimensions')\nfor ws in wb.worksheets: print(ws.title,ws.max_row,ws.max_column)\nPY",
  workdir:"/workspace",yield_time_ms:100

    … 52 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.8 seconds
Output:


-- Read Me
['Grab-and-Go Reset Decision Workbook', None, None, None, None, None, None, None, None, None]
['Q3 2026 July reset | First four weeks | Supplier projections are displayed only for reconciliation and are excluded from all economics.', None, None, None, None, None, None, None, None, None]
['RELEASE THE REVISED SET — DO NOT RELEASE THE BUYER PROPOSAL AS WRITTEN', None, None, None, None, None, None, None, None, None]
['Proposal clusters over live capacity', None, None, 'Final planogram variants that fit', None, None, 'Four-week adjusted GP, base set', None, None, 'Modeled net waste-cost reduction*']
[4, None, None, 6, None, None, 26425.5855, None, None, 389.052083333333]
[None, None, None, None, None, None, None, None, None, '*Observed-item choices only; excludes Highway Caprese and unknown non-M11 sushi exposure.']
['Final release map', None, None, Non

    … 7,361 more characters of output in the raw trajectory.

  42. 42Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({
  cmd:"python - <<'PY'\nfrom openpyxl import load_workbook\nwb=load_workbook('output/reset_decision_workbook.xlsx',data_only=True)\nws=wb['Item Economics']\nfor r in range(5,55):\n if (ws.cell(r,1).value,ws.cell(r,3).value) in [('COM','GGS-3RZA'),('COM','GGS-5MTC'),('COM','GGS-8XUY'),('CAM','GGS-8XUY'),('MET','GGS-8XUY'),('HWY','GGS-3RZA'),('RUR','GGS-6BGH')]:\n  print(r,[ws.cell(r,c).value for c in [1,3,5,6,7,11,13,20,21,24,25,26,27,28,29]])\nprint('\\nDecision counts')\nws=wb['Proposal Reconciliation']\nfrom collections import Count

    … 297 more characters of arguments in the raw trajectory.

    Output

    Script failed
Wall time 0.0 seconds
Output:

Script error:
SyntaxError: Unexpected string
  43. 43exec1

    Tool execution only — no assistant text

    execconst cmd = [ "python - <<'PY'", "from openpyxl import load_workbook", "from collections import Counter", "wb=load_workbook('output/reset_decision_workbook.xlsx',data_only=True)", "ws=wb['Item Economics']", "keys={('COM','GGS-3RZA'),('COM','GGS-5MTC'),('COM','GGS-8XUY'),('CAM','GGS-8XUY'),('MET','GGS-8XUY'),('HWY','GGS-3RZA'),('RUR','GGS-6BGH')}", "for r in range(5,55):", " if (ws.cell(r,1).value,ws.cell(r,3).value) in keys:", " print(r,[ws.cell(r,c).value for c in [1,3,5,6,7,11,13,20,21,24,25,26,27,28,29]])", "print('Decision counts')", "ws=4k chars

    Arguments

    const cmd = [
"python - <<'PY'",
"from openpyxl import load_workbook",
"from collections import Counter",
"wb=load_workbook('output/reset_decision_workbook.xlsx',data_only=True)",
"ws=wb['Item Economics']",
"keys={('COM','GGS-3RZA'),('COM','GGS-5MTC'),('COM','GGS-8XUY'),('CAM','GGS-8XUY'),('MET','GGS-8XUY'),('HWY','GGS-3RZA'),('RUR','GGS-6BGH')}",
"for r in range(5,55):",
"    if (ws.cell(r,1).value,ws.cell(r,3).value) in keys:",
"        print(r,[ws.cell(r,c).value for c in [1,3,5,6,7,11,13,20,21,24,25,26,27,28,29]])",
"print('Decision counts')",
"ws=

    … 402 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.7 seconds
Output:

14 ['MET', 'GGS-8XUY', 'Controlled pilot', 9, 8, 3.15277777777778, 0.106299212598425, 0, 0.5, 17.1330555555556, 68.5322222222222, 3.225, 'M-11 stores only; daily delivery required', 'M-11 add-on', '1 M-11 / 0 other']
18 ['COM', 'GGS-5MTC', 'Current set', 26, 8, 2.18269230769231, 0.0865191146881288, 0, 0.3, 7.32877403846154, 29.3150961538462, 1.59182692307692, 'Existing GreenFork item', 'Base carried', '1']
22 ['COM', 'GGS-3RZA', 'Controlled pilot', 8, 8, 1.828125, 0.164285714285714, 0.4, 0.3, 7.0628125, 28.25125, 3.119375, 'Eligible cluster; GreenFork route', 'Not carried', '0']
24 ['COM', 'GGS-8XUY', 'Controlled pilot', 6, 8, 1.125, 0.325, 0, 0, 4.07208333333333, 16.2883333333333, 9.31666666666667, 'Not eligible; samples are not a signed route', 'Not carried', '0']
34 ['CAM', 'GGS-8XUY', 'Controlled pilot', 4, 8, 2.125, 0.260869565217391, 0, 0, 8.86625, 35.465, 12

    … 2,979 more characters of output in the raw trajectory.

  44. 44exec1

    Tool execution only — no assistant text

    execconst r = await tools.update_plan({explanation:"Source hierarchy, terms, internal demand, final assortment, economics, and tray reconciliation are complete. The workbook has recalculated with 2,613 formulas and zero errors; visual QA and memo production remain.",plan:[ {step:"Inventory source files and extract authoritative rules, internal demand, signed terms, and proposal details","status":"completed"}, {step:"Reconcile item economics, cluster decisions, delivery eligibility, and case capacity","status":"completed"}, {step:"Build and recalculate the supporti

    Arguments

    const r = await tools.update_plan({explanation:"Source hierarchy, terms, internal demand, final assortment, economics, and tray reconciliation are complete. The workbook has recalculated with 2,613 formulas and zero errors; visual QA and memo production remain.",plan:[
  {step:"Inventory source files and extract authoritative rules, internal demand, signed terms, and proposal details","status":"completed"},
  {step:"Reconcile item economics, cluster decisions, delivery eligibility, and case capacity","status":"completed"},
  {step:"Build and recalculate the supporti

    … 260 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.0 seconds
Output:

{}
  45. 45Shell1

    Tool execution only — no assistant text

    Shellvia exec6k chars

    Arguments

    const r = await tools.exec_command({cmd:"sed -n '1,240p' /tmp/bmm_reset_summary.json","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":20000});
text(r.output);

    Output

    Script completed
Wall time 0.1 seconds
Output:

variants:
  -
    variant: MET-M11
    cluster: MET
    scope: Only stores on the signed M-11 daily route
    items:
      GGS-84QJ: 2
      GGS-2VWK: 1
      GGS-9PQL: 2
      GGS-7DNL: 1
      GGS-3RZA: 1
      GGS-6BGH: 2
      GGS-8XUY: 1
  -
    variant: MET-OTHER
    cluster: MET
    scope: Metro stores not on the signed M-11 route
    items:
      GGS-84QJ: 2
      GGS-2VWK: 1
      GGS-9PQL: 2
      GGS-7DNL: 2
      GGS-3RZA: 1
      GGS-6BGH: 2
  -
    variant: COM
    cluster: COM
    scope: All 26 Commuter Suburban stores
    items:
      GGS-84QJ: 2
      GGS-2VWK: 2
      GGS-9PQL: 1
      GGS-5MTC: 1

    … 5,138 more characters of output in the raw trajectory.

  46. 46Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({cmd:"npm root -g","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":1000});
text(r.output);

    Output

    Script completed
Wall time 0.2 seconds
Output:

/root/.nvm/versions/node/v22.23.2/lib/node_modules
  47. 47Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({cmd:"node -e \"const p=require('/root/.nvm/versions/node/v22.23.2/lib/node_modules/docx/package.json'); console.log(p.version)\"","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":1000});
text(r.output);

    Output

    Script completed
Wall time 0.1 seconds
Output:

9.7.1
  48. 48exec1

    Tool execution only — no assistant text

    execconst patch = "*** Begin Patch\n*** Add File: build_reset_memo.js\n+const fs = require(\"fs\");\n+const {\n+ AlignmentType, BorderStyle, Document, Footer, Header, HeadingLevel,\n+ LevelFormat, LineRuleType, PageBreak, PageNumber, Packer, Paragraph,\n+ ShadingType, TabStopPosition, TabStopType, Table, TableCell, TableRow,\n+ TextRun, VerticalAlign, WidthType,\n+} = require(\"/root/.nvm/versions/node/v22.23.2/lib/node_modules/docx\");\n+\n+const summary = JSON.parse(fs.readFileSync(\"/tmp/bmm_reset_summary.json\", \"utf8\"));\n+const OUT = \"/workspace

    Arguments

    const patch = "*** Begin Patch\n*** Add File: build_reset_memo.js\n+const fs = require(\"fs\");\n+const {\n+  AlignmentType, BorderStyle, Document, Footer, Header, HeadingLevel,\n+  LevelFormat, LineRuleType, PageBreak, PageNumber, Packer, Paragraph,\n+  ShadingType, TabStopPosition, TabStopType, Table, TableCell, TableRow,\n+  TextRun, VerticalAlign, WidthType,\n+} = require(\"/root/.nvm/versions/node/v22.23.2/lib/node_modules/docx\");\n+\n+const summary = JSON.parse(fs.readFileSync(\"/tmp/bmm_reset_summary.json\", \"utf8\"));\n+const OUT = \"/workspace

    … 30,851 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.0 seconds
Output:

{}
  49. 49Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({cmd:"node --check build_reset_memo.js","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

    Output

    Script completed
Wall time 0.1 seconds
Output:
  50. 50exec1

    Tool execution only — no assistant text

    execconst patch = "*** Begin Patch\n*** Update File: build_reset_memo.js\n@@\n- cell([run(\"4\", { size: 28, bold: true, color: C.teal }), run(\"\\nclusters over capacity\", { size: 16, color: C.muted })], 2592, { fill: C.lightBlue, alignment: AlignmentType.CENTER, children: [para([run(\"4\", { size: 28, bold: true, color: C.teal })], { alignment: AlignmentType.CENTER, spacing: { after: 20 } }), para(\"clusters over capacity\", { alignment: AlignmentType.CENTER, run: { size: 16, color: C.muted }, spacing: { after: 0 } })] }),\n+ cell([], 2592, { fill: C.light

    Arguments

    const patch = "*** Begin Patch\n*** Update File: build_reset_memo.js\n@@\n-    cell([run(\"4\", { size: 28, bold: true, color: C.teal }), run(\"\\nclusters over capacity\", { size: 16, color: C.muted })], 2592, { fill: C.lightBlue, alignment: AlignmentType.CENTER, children: [para([run(\"4\", { size: 28, bold: true, color: C.teal })], { alignment: AlignmentType.CENTER, spacing: { after: 20 } }), para(\"clusters over capacity\", { alignment: AlignmentType.CENTER, run: { size: 16, color: C.muted }, spacing: { after: 0 } })] }),\n+    cell([], 2592, { fill: C.light

    … 374 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.0 seconds
Output:

{}
  51. 51Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({cmd:"node build_reset_memo.js","workdir":"/workspace","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

    Output

    Script completed
Wall time 0.3 seconds
Output:

/workspace/output/grab_go_reset_memo.docx
  52. 52Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({cmd:"python /root/.agents/skills/docx/scripts/office/validate.py output/grab_go_reset_memo.docx","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

    Output

    Script completed
Wall time 1.3 seconds
Output:


Paragraphs: 0 → 207 (+207)
All validations PASSED!
  53. 53Both files now contain the same final assortment and economics. I’m rendering the memo and key workbook tabs for visual…

    Both files now contain the same final assortment and economics. I’m rendering the memo and key workbook tabs for visual QA—checking table fit, page breaks, headers, and whether the operational instructions remain readable at print scale.

  54. 54Shell1

    Tool execution only — no assistant text

    Shellvia exec5k chars

    Arguments

    const r = await tools.exec_command({cmd:"mkdir -p /tmp/bmm_memo_render\npython /root/.agents/skills/docx/scripts/office/soffice.py --headless --convert-to pdf --outdir /tmp/bmm_memo_render output/grab_go_reset_memo.docx\npdfinfo /tmp/bmm_memo_render/grab_go_reset_memo.pdf | sed -n '1,20p'\npdftotext -layout /tmp/bmm_memo_render/grab_go_reset_memo.pdf /tmp/bmm_memo_render/grab_go_reset_memo.txt\nwc -l /tmp/bmm_memo_render/grab_go_reset_memo.txt\nsed -n '1,80p' /tmp/bmm_memo_render/grab_go_reset_memo.txt","workdir":"/workspace","yield_time_ms":30000,"max_output_tokens":2

    … 28 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 1.3 seconds
Output:

Warning: failed to launch javaldx - java may not function correctly
convert /workspace/output/grab_go_reset_memo.docx as a Writer document -> /tmp/bmm_memo_render/grab_go_reset_memo.pdf using filter : writer_pdf_Export
Title:           July Grab-and-Go Reset Decision
Author:          Blue Mesa Market Category Planning
Creator:         Writer
Producer:        LibreOffice 25.2.3.2 (X86_64) / LibreOffice Community
CreationDate:    Thu Aug 20 01:37:35 2026 UTC
Custom Metadata: no
Metadata Stream: yes
Tagged:          yes
UserProperties:  no
Suspects:        no
Form:            none
JavaScript:      no
Pages:           4
Encrypted:       no
Page size:       612 x 792 pts (letter)
Page rot:        0
File size:       141009 bytes
Optimized:       no
PDF version:     1.7
265 /tmp/bmm_memo_render/grab_go_reset_memo.txt
BLUE MESA MARKET

    … 4,469 more characters of output in the raw trajectory.

  55. 55Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({cmd:"pdftoppm -png -r 130 /tmp/bmm_memo_render/grab_go_reset_memo.pdf /tmp/bmm_memo_render/page >/dev/null 2>&1\nls -lh /tmp/bmm_memo_render/page-*.png","workdir":"/workspace","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

    Output

    Script completed
Wall time 1.3 seconds
Output:

-rw-r--r-- 1 root root 257K Aug 20 01:37 /tmp/bmm_memo_render/page-1.png
-rw-r--r-- 1 root root 324K Aug 20 01:37 /tmp/bmm_memo_render/page-2.png
-rw-r--r-- 1 root root 256K Aug 20 01:37 /tmp/bmm_memo_render/page-3.png
-rw-r--r-- 1 root root 318K Aug 20 01:37 /tmp/bmm_memo_render/page-4.png
  56. 56exec1

    Tool execution only — no assistant text

    execconst results = await Promise.all([1,2,3,4].map(n => tools.view_image({path:`/tmp/bmm_memo_render/page-${n}.png`,detail:"original"}))); for (const x of results) image(x.image_url);1.6M chars

    Arguments

    const results = await Promise.all([1,2,3,4].map(n => tools.view_image({path:`/tmp/bmm_memo_render/page-${n}.png`,detail:"original"})));
for (const x of results) image(x.image_url);

    Output

    Script completed
Wall time 0.1 seconds
Output:

    … 1,575,514 more characters of output in the raw trajectory.

  57. 57exec1

    Tool execution only — no assistant text

    execconst patch = "*** Begin Patch\n*** Update File: build_reset_memo.js\n@@\n- [cell(\"Hummus removal\", 1900, { fill: C.grey, bold: true }), cell(\"CHANGE: retain COM/CAM/HWY/RUR\", 2100, { fill: C.paleYellow, bold: true }), cell(\"The proposal would leave Highway and Rural without a credible vegetarian lunch. Hummus also beats Caprese in COM and remains productive in CAM. MET can make the clean swap to Caprese.\", CONTENT - 4000)],\n+ [cell(\"Hummus removal\", 1900, { fill: C.grey, bold: true }), cell(\"CHANGE: retain outside MET\", 2100, { fill: C.paleYello

    Arguments

    const patch = "*** Begin Patch\n*** Update File: build_reset_memo.js\n@@\n-  [cell(\"Hummus removal\", 1900, { fill: C.grey, bold: true }), cell(\"CHANGE: retain COM/CAM/HWY/RUR\", 2100, { fill: C.paleYellow, bold: true }), cell(\"The proposal would leave Highway and Rural without a credible vegetarian lunch. Hummus also beats Caprese in COM and remains productive in CAM. MET can make the clean swap to Caprese.\", CONTENT - 4000)],\n+  [cell(\"Hummus removal\", 1900, { fill: C.grey, bold: true }), cell(\"CHANGE: retain outside MET\", 2100, { fill: C.paleYello

    … 863 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.0 seconds
Output:

{}
  58. 58Shell1

    Tool execution only — no assistant text

    Shellvia exec3k chars

    Arguments

    const r = await tools.exec_command({cmd:"node build_reset_memo.js\npython /root/.agents/skills/docx/scripts/office/validate.py output/grab_go_reset_memo.docx\npython /root/.agents/skills/docx/scripts/office/soffice.py --headless --convert-to pdf --outdir /tmp/bmm_memo_render output/grab_go_reset_memo.docx\npdfinfo /tmp/bmm_memo_render/grab_go_reset_memo.pdf | rg '^Pages|^Page size'\npdftotext -layout /tmp/bmm_memo_render/grab_go_reset_memo.pdf - | sed -n '32,70p'","workdir":"/workspace","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

    Output

    Script completed
Wall time 2.7 seconds
Output:

/workspace/output/grab_go_reset_memo.docx

Paragraphs: 0 → 207 (+207)
All validations PASSED!
Warning: failed to launch javaldx - java may not function correctly
convert /workspace/output/grab_go_reset_memo.docx as a Writer document -> /tmp/bmm_memo_render/grab_go_reset_memo.pdf using filter : writer_pdf_Export
Overwriting: /tmp/bmm_memo_render/grab_go_reset_memo.pdf
Pages:           4
Page size:       612 x 792 pts (letter)
  Proposal element               Final call                                           Why

                                                     Signed daily route and 50% waste credit apply only to Metro M-11.
                         CHANGE: 1 facing,
 Spicy Tuna Sushi                                    Internal pilot: MET 3.15 sales/store/week at 10.6% waste; COM and
                         M-11 only                   CAM waste rea

    … 2,138 more characters of output in the raw trajectory.

  59. 59Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({
  cmd:"python - <<'PY'\nfrom openpyxl import load_workbook\nfrom pathlib import Path\nsrc=Path('output/reset_decision_workbook.xlsx')\nout=Path('/tmp/bmm_workbook_render');out.mkdir(exist_ok=True)\nareas={'Read Me':'A1:J36','Capacity Check':'A1:J10','Cluster Economics':'A1:M27','Final Assortment':'A1:P40','Proposal Reconciliation':'A1:R18'}\nfor keep,area in areas.items():\n    wb=load_workbook(src)\n    for ws in list(wb.worksheets):\n        if ws.title!=keep: wb.remove(ws)\n    ws=wb[keep]; ws.print_area=area; ws.page_setup.fitToWid

    … 536 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 5.6 seconds
Output:

Warning: failed to launch javaldx - java may not function correctly
Warning: failed to launch javaldx - java may not function correctly
Warning: failed to launch javaldx - java may not function correctly
Warning: failed to launch javaldx - java may not function correctly
Warning: failed to launch javaldx - java may not function correctly
Capacity_Check.pdf 1
Cluster_Economics.pdf 1
Final_Assortment.pdf 1
Proposal_Reconciliation.pdf 1
Read_Me.pdf 1
  60. 60Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({cmd:"for f in /tmp/bmm_workbook_render/*.pdf; do base=${f%.pdf}; pdftoppm -f 1 -singlefile -png -r 150 \"$f\" \"$base\" >/dev/null 2>&1; done\nls -lh /tmp/bmm_workbook_render/*.png","workdir":"/workspace","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

    Output

    Script completed
Wall time 1.4 seconds
Output:

-rw-r--r-- 1 root root  87K Aug 20 01:38 /tmp/bmm_workbook_render/Capacity_Check.png
-rw-r--r-- 1 root root 159K Aug 20 01:38 /tmp/bmm_workbook_render/Cluster_Economics.png
-rw-r--r-- 1 root root 158K Aug 20 01:38 /tmp/bmm_workbook_render/Final_Assortment.png
-rw-r--r-- 1 root root 130K Aug 20 01:38 /tmp/bmm_workbook_render/Proposal_Reconciliation.png
-rw-r--r-- 1 root root 207K Aug 20 01:38 /tmp/bmm_workbook_render/Read_Me.png
  61. 61exec1

    Tool execution only — no assistant text

    execconst files=["Read_Me","Capacity_Check","Cluster_Economics","Final_Assortment","Proposal_Reconciliation"]; const rs=await Promise.all(files.map(f=>tools.view_image({path:`/tmp/bmm_workbook_render/${f}.png`,detail:"original"}))); rs.forEach(x=>image(x.image_url));1.0M chars

    Arguments

    const files=["Read_Me","Capacity_Check","Cluster_Economics","Final_Assortment","Proposal_Reconciliation"];
const rs=await Promise.all(files.map(f=>tools.view_image({path:`/tmp/bmm_workbook_render/${f}.png`,detail:"original"})));
rs.forEach(x=>image(x.image_url));

    Output

    Script completed
Wall time 0.0 seconds
Output:

    … 1,006,876 more characters of output in the raw trajectory.

  62. 62Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({
  cmd:"python - <<'PY'\nfrom openpyxl import load_workbook\nfrom pathlib import Path\nsrc=Path('output/reset_decision_workbook.xlsx')\nout=Path('/tmp/bmm_workbook_render2');out.mkdir(exist_ok=True)\nareas={'Read Me':'A1:J36','Capacity Check':'A1:J10','Cluster Economics':'A1:M27','Final Assortment':'A1:P40','Proposal Reconciliation':'A1:R18'}\nfor keep,area in areas.items():\n    wb=load_workbook(src)\n    for ws in wb.worksheets:\n        ws.sheet_state='visible' if ws.title==keep else 'hidden'\n    ws=wb[keep]; ws.print_area=area; ws.

    … 783 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 20.0 seconds
Output:

Warning: failed to launch javaldx - java may not function correctly
Warning: failed to launch javaldx - java may not function correctly
Warning: failed to launch javaldx - java may not function correctly
Warning: failed to launch javaldx - java may not function correctly
Warning: failed to launch javaldx - java may not function correctly
Capacity_Check.pdf 1
Cluster_Economics.pdf 1
Final_Assortment.pdf 1
Proposal_Reconciliation.pdf 1
Read_Me.pdf 1
  63. 63exec1

    Tool execution only — no assistant text

    execconst files=["Read_Me","Capacity_Check","Cluster_Economics","Final_Assortment","Proposal_Reconciliation"]; const rs=await Promise.all(files.map(f=>tools.view_image({path:`/tmp/bmm_workbook_render2/${f}.png`,detail:"original"}))); rs.forEach(x=>image(x.image_url));1.2M chars

    Arguments

    const files=["Read_Me","Capacity_Check","Cluster_Economics","Final_Assortment","Proposal_Reconciliation"];
const rs=await Promise.all(files.map(f=>tools.view_image({path:`/tmp/bmm_workbook_render2/${f}.png`,detail:"original"})));
rs.forEach(x=>image(x.image_url));

    Output

    Script completed
Wall time 0.1 seconds
Output:

    … 1,192,560 more characters of output in the raw trajectory.

  64. 64The support amounts are below the signed caps, but I’m adding an explicit cap-usage check to the workbook so Finance and…

    The support amounts are below the signed caps, but I’m adding an explicit cap-usage check to the workbook so Finance and the merchant can see that the modeled credits are not overstated.

  65. 65exec1

    Tool execution only — no assistant text

    execconst patch = "*** Begin Patch\n*** Update File: build_reset_workbook.py\n@@\n impact_total = 27\n@@\n for col in [4, 5, 6]:\n ws.cell(impact_total, col).number_format = fmt_cur\n+\n+section_row(ws, 30, \"Signed scan-support cap check\", 13)\n+header_row(ws, 31, [\"Item\", \"SKU\", \"Projected 4-Week Scanned Units\", \"Signed Unit Cap\", \"Cap Utilization\", \"Status\", \"Allowance / Unit\", \"Projected Scan Support\", \"\", \"\", \"\", \"\", \"\"])\n+support_checks = [\n+ (\"Caprese Ciabatta\", \"GGS-3R

    Arguments

    const patch = "*** Begin Patch\n*** Update File: build_reset_workbook.py\n@@\n impact_total = 27\n@@\n for col in [4, 5, 6]:\n     ws.cell(impact_total, col).number_format = fmt_cur\n+\n+section_row(ws, 30, \"Signed scan-support cap check\", 13)\n+header_row(ws, 31, [\"Item\", \"SKU\", \"Projected 4-Week Scanned Units\", \"Signed Unit Cap\", \"Cap Utilization\", \"Status\", \"Allowance / Unit\", \"Projected Scan Support\", \"\", \"\", \"\", \"\", \"\"])\n+support_checks = [\n+    (\"Caprese Ciabatta\", \"GGS-3R

    … 2,685 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.0 seconds
Output:

{}
  66. 66exec1

    Tool execution only — no assistant text

    execconst patch = "*** Begin Patch\n*** Update File: build_reset_memo.js\n@@\n const capByVariant = Object.fromEntries(summary.capacity.map((x) => [x.variant, x]));\n const econByCluster = Object.fromEntries(summary.cluster_economics.map((x) => [x.cluster, x]));\n+const supportCapBySku = Object.fromEntries(summary.support_cap_checks.map((x) => [x.sku, x]));\n@@\n children.push(table(econWidths, econRows, { header: true }));\n \n children.push(\n+ para(`Signed cap check: the internal-rate model projects ${Math.round(supportCapBySku[\"GGS-3RZA\"].projected_units).toLocaleSt

    Arguments

    const patch = "*** Begin Patch\n*** Update File: build_reset_memo.js\n@@\n const capByVariant = Object.fromEntries(summary.capacity.map((x) => [x.variant, x]));\n const econByCluster = Object.fromEntries(summary.cluster_economics.map((x) => [x.cluster, x]));\n+const supportCapBySku = Object.fromEntries(summary.support_cap_checks.map((x) => [x.sku, x]));\n@@\n children.push(table(econWidths, econRows, { header: true }));\n \n children.push(\n+  para(`Signed cap check: the internal-rate model projects ${Math.round(supportCapBySku[\"GGS-3RZA\"].projected_units).toLocaleSt

    … 874 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.0 seconds
Output:

{}
  67. 67Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({cmd:"python -m py_compile build_reset_workbook.py\npython build_reset_workbook.py\npython /root/.agents/skills/xlsx/scripts/recalc.py output/reset_decision_workbook.xlsx 60\nnode --check build_reset_memo.js\nnode build_reset_memo.js\npython /root/.agents/skills/docx/scripts/office/validate.py output/grab_go_reset_memo.docx","workdir":"/workspace","yield_time_ms":30000,"max_output_tokens":20000});
text(r.output);

    Output

    Script completed
Wall time 6.5 seconds
Output:

/workspace/output/reset_decision_workbook.xlsx
/tmp/bmm_reset_summary.json
{
  "status": "success",
  "total_errors": 0,
  "error_summary": {},
  "total_formulas": 2625
}
/workspace/output/grab_go_reset_memo.docx

Paragraphs: 0 → 208 (+208)
All validations PASSED!
  68. 68Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({cmd:"python /root/.agents/skills/docx/scripts/office/soffice.py --headless --convert-to pdf --outdir /tmp/bmm_memo_render output/grab_go_reset_memo.docx >/dev/null\npdfinfo /tmp/bmm_memo_render/grab_go_reset_memo.pdf | rg '^Pages|^Page size'\npdftotext -layout /tmp/bmm_memo_render/grab_go_reset_memo.pdf - | rg -n 'Signed cap check|M-11 sushi add-on|Decision-level evidence|Page 3'","workdir":"/workspace","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

    Output

    Script completed
Wall time 1.1 seconds
Output:

Warning: failed to launch javaldx - java may not function correctly
Pages:           4
Page size:       612 x 792 pts (letter)
158:Signed cap check: the internal-rate model projects 373 Caprese scanned units against the 5,500-unit cap and 1,214 Protein
160:M-11 sushi add-on per eligible store: 3.15 sales/week, 10.6% waste, $68.53 four-week adjusted GP, and
163:Decision-level evidence
204:Internal merchant decision support • Q3 2026                                                             Page 3
  69. 69Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({cmd:"pdftoppm -f 3 -singlefile -png -r 130 /tmp/bmm_memo_render/grab_go_reset_memo.pdf /tmp/bmm_memo_render/page3 >/dev/null 2>&1","workdir":"/workspace","yield_time_ms":30000,"max_output_tokens":1000});
text(r.output);

    Output

    Script completed
Wall time 0.4 seconds
Output:
  70. 70exec1

    Tool execution only — no assistant text

    execconst r=await tools.view_image({path:"/tmp/bmm_memo_render/page3.png",detail:"original"}); image(r.image_url);377k chars

    Arguments

    const r=await tools.view_image({path:"/tmp/bmm_memo_render/page3.png",detail:"original"}); image(r.image_url);

    Output

    Script completed
Wall time 0.0 seconds
Output:

    … 375,516 more characters of output in the raw trajectory.

  71. 71Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const cmd = [
"python - <<'PY'",
"from openpyxl import load_workbook",
"wb=load_workbook('output/reset_decision_workbook.xlsx',data_only=True)",
"ws=wb['Cluster Economics']",
"for r in range(30,34):",
"    print(r,[ws.cell(r,c).value for c in range(1,9)])",
"PY"
].join("\n");
const r=await tools.exec_command({cmd,workdir:"/workspace",yield_time_ms:10000,max_output_tokens:5000});
text(r.output);

    Output

    Script completed
Wall time 0.8 seconds
Output:

30 ['Signed scan-support cap check', None, None, None, None, None, None, None]
31 ['Item', 'SKU', 'Projected 4-Week Scanned Units', 'Signed Unit Cap', 'Cap Utilization', 'Status', 'Allowance / Unit', 'Projected Scan Support']
32 ['Caprese Ciabatta', 'GGS-3RZA', 373.2, 5500, 0.0678545454545455, 'BELOW CAP', 0.4, 149.28]
33 ['Protein Snack Box', 'GGS-6BGH', 1214.15, 8000, 0.15176875, 'BELOW CAP', 0.35, 424.9525]
  72. 72Shell1

    Tool execution only — no assistant text

    Shellvia exec

    Arguments

    const r = await tools.exec_command({cmd:"ls -lh output\nfind output -maxdepth 1 -type f -printf '%f\\n' | sort\nsha256sum output/grab_go_reset_memo.docx output/reset_decision_workbook.xlsx","workdir":"/workspace","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

    Output

    Script completed
Wall time 0.1 seconds
Output:

total 124K
-rw-r--r-- 1 root root  19K Aug 20 01:39 grab_go_reset_memo.docx
-rw-r--r-- 1 root root 102K Aug 20 01:39 reset_decision_workbook.xlsx
grab_go_reset_memo.docx
reset_decision_workbook.xlsx
84c2133ecaa06d4fb6602d6ea86805db75f421f8736b7064bf6057fef8d6c085  output/grab_go_reset_memo.docx
253f0ad574c83acec5afd321bfce72ee2569c988071e8d68173eebe4a725fe46  output/reset_decision_workbook.xlsx
  73. 73exec1

    Tool execution only — no assistant text

    execconst r = await tools.update_plan({explanation:"Both deliverables are complete and reconciled. The workbook recalculated 2,625 formulas with zero errors; the memo passed DOCX validation and a four-page visual inspection. Final assortment, capacity, support caps, and economics match across files.",plan:[ {step:"Inventory source files and extract authoritative rules, internal demand, signed terms, and proposal details","status":"completed"}, {step:"Reconcile item economics, cluster decisions, delivery eligibility, and case capacity","status":"completed"}, {step:

    Arguments

    const r = await tools.update_plan({explanation:"Both deliverables are complete and reconciled. The workbook recalculated 2,625 formulas with zero errors; the memo passed DOCX validation and a four-page visual inspection. Final assortment, capacity, support caps, and economics match across files.",plan:[
  {step:"Inventory source files and extract authoritative rules, internal demand, signed terms, and proposal details","status":"completed"},
  {step:"Reconcile item economics, cluster decisions, delivery eligibility, and case capacity","status":"completed"},
  {step:

    … 295 more characters of arguments in the raw trajectory.

    Output

    Script completed
Wall time 0.0 seconds
Output:

{}
  74. 74Completed. The revised six-variant assortment fits live capacity and follows signed route/support terms. - [Merchant mem…

    Completed. The revised six-variant assortment fits live capacity and follows signed route/support terms.

    • Merchant memo
    • Decision workbook

    Workbook validation found zero formula errors; the memo passed document and visual QA.

3 failure modes across 5 verifier failures

  1. 1Allocation or Selection Plan IncorrectView on Failure Modes ↗Reasoning and Computation2 failures

    Description

    After eligibility is established, does the artifact assign a scarce resource, facing, shelf position, assortment item, or appointment slot to the wrong recipient or produce the wrong required final set?

    Failure evidence · per affected verifier

    • The memo's cluster-level final facings and slot totals consistently differ from the required exact values for Metro, Highway, and Rural North.

    • The workbook's final facing counts for Chicken Caesar Wrap, Southwest Chicken Bowl, and the other graded SKUs consistently differ from the required per-cluster values.

  2. 2Quantity, Cost, Capacity, or Rate Calculation WrongView on Failure Modes ↗Reasoning and Computation2 failures

    Description

    With the correct source rows and eligibility decisions available, is a required order quantity, excess, cost, credit, capacity, production-mix value, threshold flag, or weighted rate calculated incorrectly?

    Failure evidence · per affected verifier

    • The memo correctly argues for leaving some capacity open rather than force-filling weak items, but the specific Highway and Rural North open-slot numbers do not match the required 7.5/8.0 and 6.0/7.0 values.

    • The workbook correctly shows live capacity and the buyer-proposal overages, but the final reconciled tray-slot totals per cluster do not match the required final values.

  3. 3Required Rationale or Authority Statement OmittedView on Failure Modes ↗Presentation and Communication1 failure

    Description

    Is the underlying decision correct or substantially present, but the memo fails to state a required rule hierarchy, source-governance principle, item-specific reason, signed-term basis, or operating condition?

    Failure evidence · per affected verifier

    • The memo protects Chicken Caesar Wrap and gives Protein Snack Box launch space but never states the full required rationale together: OOS pressure for Caesar, and pilot performance, low waste, code date, and scan credit for Protein Snack Box.