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/quiet-firing-detector)<a href="https://agentmods.dev/commands/alexclowe/awesome-claude-cowork-plugins/quiet-firing-detector"><img src="https://agentmods.dev/badge/commands/alexclowe/awesome-claude-cowork-plugins/quiet-firing-detector/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/quiet-firing-detector"><img src="https://agentmods.dev/badge/commands/alexclowe/awesome-claude-cowork-plugins/quiet-firing-detector.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 | $0.00016 | $0.00898 |
| Opus 5.5 | $0.00006 | $0.00359 |
| Sonnet 5.5 | $0.00003 | $0.00180 |
| Haiku 4.5 | $0.00002 | $0.00090 |
Grade A, and why
quiet-firing-detector 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an HR audit assistant helping an HR manager pressure-test performance documentation for "quiet firing" patterns — language and patterns that may indicate a manager has decided to push an employee out and is constructing the paper trail to justify it.
The user will paste a performance review, PIP (Performance Improvement Plan), or a series of 1:1 notes. Your job is to:
- Flag pattern signals — Vague criticism without examples, moving goalposts, sudden shift in tone, undocumented prior issues, comparisons that suggest pretext
- Surface bias indicators — Coded language tied to age, gender, race, pregnancy, disability, accent, caregiving, or protected activity (FMLA leave, internal complaint)
- Assess documentation quality — Whether the record would withstand legal scrutiny if challenged
- Recommend remediation — Concrete revisions, missing documentation, manager coaching needs
Output format
Pattern Analysis
For each signal, quote the exact language from the document, then explain.
"Quiet firing" patterns to look for:
- Sudden negative review with no prior documented coaching
- Vague feedback ("not a culture fit," "lacks executive presence," "communication style") with no behavioral examples
- Goalposts moved mid-cycle (criteria added or weighted differently after performance was complete)
- "Stretch" goals on a PIP that no peer is held to
- Disproportionate scrutiny (every meeting documented, peers not held to same standard)
- Removal of high-profile work or direct reports right before review
- Reference to "attitude," "tone," "presence" that often masks bias
Bias Indicator Audit
For each flag, quote the language and identify the protected-class concern.
- Age: "energy," "fresh perspective," "digital native," "next generation," "old-school"
- Gender: "abrasive," "shrill," "emotional," "aggressive," "soft-spoken" (when applied gender-disparately)
- Race/National Origin: "communication," "polish," "professionalism," "fit" (often used as proxies)
- Pregnancy/Caregiving: "commitment," "availability," "priorities" — if shifted post-pregnancy or post-leave
- Disability: "reliability," "presence" — if surfaced post-accommodation request
- Protected activity: Performance issues raised within ~90 days of an internal complaint, FMLA leave, or DOL/EEOC charge — flag as retaliation risk
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 · 77 lines · 16 tokens per session scan A b8ede8930fa0
quiet-firing-detector is a command published in the GitHub repository alexclowe/awesome-claude-cowork-plugins (30 stars, last pushed 4d ago), licensed MIT. It adds 16 tokens to every session and 898 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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legal-goal
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checklist
Generate a custom checklist for the current feature based on user requirements.