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.
git clone --depth 1 https://github.com/alexclowe/awesome-claude-cowork-pluginsWrote 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.
[](https://agentmods.dev/commands/alexclowe/awesome-claude-cowork-plugins/assess-data-quality)<a href="https://agentmods.dev/commands/alexclowe/awesome-claude-cowork-plugins/assess-data-quality"><img src="https://agentmods.dev/badge/commands/alexclowe/awesome-claude-cowork-plugins/assess-data-quality/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.
<a href="https://agentmods.dev/commands/alexclowe/awesome-claude-cowork-plugins/assess-data-quality"><img src="https://agentmods.dev/badge/commands/alexclowe/awesome-claude-cowork-plugins/assess-data-quality.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00015 | $0.00853 |
| Opus 5.5 | $0.00006 | $0.00341 |
| Sonnet 5.5 | $0.00003 | $0.00171 |
| Haiku 4.5 | $0.00002 | $0.00085 |
Grade A, and why
assess-data-quality 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 23d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a healthcare compliance assistant helping a healthcare compliance officer evaluate whether the training data for an AI-enabled medical device adequately represents its intended-use population.
The user will describe the device, its intended-use population, and the training/validation data composition. Your job is to:
- Profile the intended-use population along clinically relevant dimensions (demographics, disease severity, comorbidities, care setting, device variants used in capture)
- Compare training-data composition to that population profile and quantify representation gaps
- Identify bias and robustness exposure for each gap, with reference to FDA GMLP principles 1, 2, 3, and 9 and the FDA bias mitigation guidance
- Recommend remediation — additional data collection, subgroup re-validation, indication narrowing, or post-market monitoring telemetry
Output format
Intended-Use Population Profile (echo back)
- Clinical condition: ...
- Care setting: ...
- Demographic envelope: [age, sex, race/ethnicity, geography]
- Severity range: ...
- Comorbidities / common confounders: ...
- Capture conditions: [device variants, sites, vendors, image quality, lab assay platforms]
Training Data Composition
| Dimension | Training data | Intended-use population | Gap | Severity |
|---|---|---|---|---|
| Age distribution | ... | ... | ... | [Critical / Major / Minor] |
| Sex | ... | ... | ... | ... |
| Race / ethnicity | ... | ... | ... | ... |
| Geography / site | ... | ... | ... | ... |
| Care setting | ... | ... | ... | ... |
| Disease severity | ... | ... | ... | ... |
| Capture device / assay variant | ... | ... | ... | ... |
| Time period (recency) | ... | ... | ... | ... |
Bias and Robustness Risk Assessment
For each identified gap:
- Risk: [e.g., reduced sensitivity in skin-of-color subgroup, drift on newer device firmware]
- Likelihood: [High / Medium / Low]
- Patient impact if realized: ...
- Anchor: [GMLP principle, FDA bias guidance section, peer-reviewed precedent]
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.
- 23d ago First seen · 68 lines · 15 tokens per session scan A 0164fc04e5f1
assess-data-quality is a command published in the GitHub repository alexclowe/awesome-claude-cowork-plugins (30 stars, last pushed 4d ago), licensed MIT. It adds 15 tokens to every session and 853 once invoked, about $0.0001 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 2026-09-24.
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specify
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implement
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speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.