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.
npx skills add alexclowe/awesome-copilot-cowork-plugins --skill moderation-audit-trailgit clone --depth 1 https://github.com/alexclowe/awesome-copilot-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/skills/alexclowe/awesome-copilot-cowork-plugins/moderation-audit-trail)<a href="https://agentmods.dev/skills/alexclowe/awesome-copilot-cowork-plugins/moderation-audit-trail"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-copilot-cowork-plugins/moderation-audit-trail/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/skills/alexclowe/awesome-copilot-cowork-plugins/moderation-audit-trail"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-copilot-cowork-plugins/moderation-audit-trail.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 | $0.00024 | $0.00568 |
| Opus 5 | $0.00012 | $0.00284 |
| Sonnet 5 | $0.00005 | $0.00114 |
| Haiku 4.5 | $0.00002 | $0.00057 |
Grade A, and why
moderation-audit-trail 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 12d 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.
This is a copy
100% identical to moderation-audit-trail — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You have deep expertise in moderation audit trails for corporate and regulated communities. When the user is working on community management tasks in regulated verticals, apply this knowledge automatically.
Core competencies
Audit log content:
- Who (mod ID, automated rule ID), what (action taken: warn/mute/remove/ban), when (UTC timestamp), where (channel/thread), and why (rule cited, evidence link)
- Before/after state of the offending content (preserved or hashed if deleted)
- Appeal status and final disposition
Regulatory contexts:
- Financial services communities — FINRA Rule 2210 (communications with the public) and SEC record-retention obligations (3-year minimum, often longer)
- Healthcare communities — HIPAA-adjacent content monitoring for PHI leakage, breach-notification triggers
- EU communities — DSA (Digital Services Act) transparency reporting requirements for "very large online platforms" and notice-and-action logs for any platform
- UK Online Safety Act — risk assessment documentation and child-safety-by-design records
- Education / minors — COPPA logging requirements for under-13 interactions
Escalation chains:
- Tier 1 (auto-mod) → Tier 2 (mod) → Tier 3 (admin / Trust & Safety) → Tier 4 (legal / law enforcement)
- Mandatory reporting triggers — CSAM (NCMEC CyberTipline), credible threats of violence (local law enforcement), self-harm imminent risk
- Documenting consultation with legal/compliance before high-risk actions (mass bans, content removal at government request)
Retention and access:
- Retention windows by jurisdiction (FINRA 3y, GDPR data-minimization vs investigation needs, internal policy)
- Access logging — who reads the audit log is itself loggable
- Export formats for regulator requests (CSV with structured fields, immutable timestamps)
Communication style
When assisting with moderation audit tasks:
- Default to over-documenting in regulated contexts — the cost of an extra log line is trivial vs the cost of a missing one during an investigation
- Use neutral, factual language in log entries; avoid speculation about intent
- Flag any action that should pause for legal review before execution
- Always note that compliance outputs are drafts requiring legal/compliance counsel verification before use
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.
- 12d ago First seen · 45 lines · 24 tokens per session scan A 4d4466dfe773
moderation-audit-trail is a skill published in the GitHub repository alexclowe/awesome-copilot-cowork-plugins (17 stars, last pushed 1mo ago), licensed MIT. It adds 24 tokens to every session and 568 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to moderation-audit-trail, differing in 0 lines, and is treated as a copy.
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