weak-agent-test

weak-agent-test is a skill for Claude Code from kklimuk/docx-cli. It costs 218 tokens per session (6,656 once invoked), scanned A, original, MIT.

An adversarial test harness for docx-cli, a command-line tool for reading and editing Microsoft Word documents. It runs document tasks, renders the results in Word, grades them, and combines the findings into an improvement report.

In plain words
What is it for?
Use it to test document-editing workflows across scenarios such as form filling, invoice changes, and logo replacement.
Why use it?
It shows where weaker AI agents fail, how correct their documents are, and how many tokens or tool calls the work requires.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: reads .claude/ paths; mentions subagents; mentions Claude Code.

Part of the docx-cli plugin — 4 skills, 1 agent shipped together

Good fit Use it to test document-editing workflows across scenarios such as form filling, invoice changes, and logo replacement.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kklimuk/docx-cli/weak-agent-test
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add kklimuk/docx-cli --skill weak-agent-test
Clone the repo
git clone --depth 1 https://github.com/kklimuk/docx-cli

Made for: Claude Code.

Or install docx-cli, the plugin that ships this one along with the rest of its 4 skills, 1 agent.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for weak-agent-test

README.md
[![agentmods](https://agentmods.dev/badge/skills/kklimuk/docx-cli/weak-agent-test/github.svg)](https://agentmods.dev/skills/kklimuk/docx-cli/weak-agent-test)
Your own site
<a href="https://agentmods.dev/skills/kklimuk/docx-cli/weak-agent-test"><img src="https://agentmods.dev/badge/skills/kklimuk/docx-cli/weak-agent-test/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for weak-agent-test

Your own site · 80×15
<a href="https://agentmods.dev/skills/kklimuk/docx-cli/weak-agent-test"><img src="https://agentmods.dev/badge/skills/kklimuk/docx-cli/weak-agent-test.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 218 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,656 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00218 $0.06656
Opus 5.5 $0.00087 $0.02662
Sonnet 5.5 $0.00044 $0.01331
Haiku 4.5 $0.00022 $0.00666

Measured 19d ago against content hash 7136e184c44f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

weak-agent-test scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 19d ago.

The scan reads SKILL.md. This mod also ships 6 executable files (scripts/collect-exercises.ts, scripts/exercise-metrics.ts, scripts/parse-local-ledger.ts, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/weak-agent-test/SKILL.md · 420 lines

How it starts

The opening of the file, as written. The whole thing — 420 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Adversarial review — weak-agent harness for docx-cli

This harness answers one question: can weak agents actually use docx-cli to get real work done, and what should we fix first? It runs the weak-agent-test workflow (.claude/workflows/weak-agent-test.js), which fans out one weak exercise agent per scenario (Haiku by default — swappable to Sonnet via args.model), renders every output with Microsoft Word, grades each against ground-truth criteria with an opus judge, and has opus synthesize a prioritized improvement report. Exercise agents do NOT self-report tool counts — every tool-economy and token number is measured after the run (agents under-count their own calls ~2×, so self-reports were dropped): from the agent transcripts for the Claude arms, from each scenario's exercise.json ledger for the local arm. Both roll up into the same Run-metrics table (tokens, wall-clock, tool split, correctness) via exercise-metrics.ts.

The test corpus is bundled with this skill under scenarios/, one folder per scenario, named after its key (scenarios/mnda/, scenarios/invoice/, …). Each scenario folder is self-describing and holds everything that scenario needs:

  • task.md — the AGENT-FACING request, written as a human delegating the work: the goal, the data, the intent — and no tool vocabulary (no docx commands, locators, or OOXML terms), because discovering which features deliver the outcome is part of what's measured,
  • criteria.md — the JUDGE-ONLY grading rubric (the precise, tool-specific checks). The stage step withholds it from the agent's run workspace, and the judge reads it from the pristine source — the agent never sees the answer key,
  • the fixture .docx to work on (edit scenarios only; authoring scenarios create their output fresh),
  • assets/ — any additional inputs (data files, images; empty for most edit scenarios).

The workflow's SCENARIOS manifest holds only the per-scenario routing metadata (key, bucket label, edit/author kind, the doc filename); whether a baseline gets rendered is DERIVED from the kind (every edit scenario has a pristine source, so it gets one — see hasBaseline()), not a stored field. The actual request/criteria/fixture/assets all live in the folder. The skill is therefore self-contained and travels with its test corpus. To change what a scenario tests, edit the files in its folder. (Heavy, ephemeral run outputs — edited docx, renders, reviews, the report — are dumped to ./tmp/docx-weak-agent-test/<ts>/, never into the repo.)

Staging is ONE code path for every backend: scripts/stage-scenario.ts copies a scenario folder, strips the judge-only criteria.md, and verifies the inputs landed. The workflow's Stage agent runs it per scenario; the local corpus runner imports it.

Each run produces, under the timestamped run dir, one result folder per scenario (named after its key) plus the run-level report and metrics:

<RUN_DIR>/
  REPORT.md            ← synthesized report + the measured run-metrics section, both
                          written by your post-run exercise-metrics.ts --append-report
  exercise-metrics.md  ← measured per-exercise-agent tokens/time/tool split
  exercise-metrics.json
  <key>/               ← one per scenario; the worked-on copy lives here
    task.md  assets/   ← (criteria.md is withheld from this copy — judge-only)
    scratch/           ← the agent's PRIVATE scratch dir (batch JSONL, drafts). The
                          session scratchpad is shared by every concurrent agent, so
                          same-named files there collide — see "Isolation breaches"
    <doc>.docx         ← the edited/authored document
    renders/output/    ← the OUTPUT: Word-rendered page PNGs + read.md (markdown read view)
    renders/baseline/  ← the pristine "before": page PNGs + read.md (every EDIT scenario;
                          absent only for the authored eliot-journal — no source to diff)
    review.md          ← the judge's saved review for this task (written in-run)
    verdict.json       ← the judge's structured verdict incl. taskSuccess (written in-run
                          by the judge — the correctness source the metrics pass reads)
    metrics.json       ← this task's measured tokens/time/tool split + correctness
                          (written by your post-run metrics pass)

Read the full file on GitHub · 420 lines

Files

What ships with it

32 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 19d ago Changed · +36 lines 7136e184c44f
  2. 2mo ago First seen · 384 lines · 218 tokens per session scan A 3bdf77be4ca5

Subscribe to this mod's changes

weak-agent-test is a skill published in the GitHub repository kklimuk/docx-cli (235 stars, last pushed 2d ago), licensed MIT. It adds 218 tokens to every session and 6,656 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.