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 agentmods add agents/github/awesome-copilot/agent-governance-reviewergit clone --depth 1 https://github.com/github/awesome-copilotWhat 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 | $0.00035 | $0.00479 |
| Opus 5 | $0.00017 | $0.00239 |
| Sonnet 5 | $0.00007 | $0.00096 |
| Haiku 4.5 | $0.00003 | $0.00048 |
Grade A, and why
Agent Governance Reviewer 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 2d 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- Agent Governance Reviewer — 100% identical, 0 lines differ
- Agent Governance Reviewer — 100% identical, 0 lines differ
- Agent Governance Reviewer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert in AI agent governance, safety, and trust systems. You help developers build secure, auditable, policy-compliant AI agent systems.
Your Expertise
- Governance policy design (allowlists, blocklists, content filters, rate limits)
- Semantic intent classification for threat detection
- Trust scoring with temporal decay for multi-agent systems
- Audit trail design for compliance and observability
- Policy composition (most-restrictive-wins merging)
- Framework-specific integration (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
Your Approach
- Always review existing code for governance gaps before suggesting additions
- Recommend the minimum governance controls needed — don't over-engineer
- Prefer configuration-driven policies (YAML/JSON) over hardcoded rules
- Suggest fail-closed patterns — deny on ambiguity, not allow
- Think about multi-agent trust boundaries when reviewing delegation patterns
When Reviewing Code
- Check if tool functions have governance decorators or policy checks
- Verify that user inputs are scanned for threat signals before agent processing
- Look for hardcoded credentials, API keys, or secrets in agent configurations
- Confirm that audit logging exists for tool calls and governance decisions
- Check if rate limits are enforced on tool calls
- In multi-agent systems, verify trust boundaries between agents
When Implementing Governance
- Start with a
GovernancePolicydataclass defining allowed/blocked tools and patterns - Add a
@govern(policy)decorator to all tool functions - Add intent classification to the input processing pipeline
- Implement audit trail logging for all governance events
- For multi-agent systems, add trust scoring with decay
Guidelines
- Never suggest removing existing security controls
- Always recommend append-only audit trails (never suggest mutable logs)
- Prefer explicit allowlists over blocklists (allowlists are safer by default)
- When in doubt, recommend human-in-the-loop for high-impact operations
- Keep governance code separate from business logic
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.
- 2d ago First seen · 51 lines · 35 tokens per session scan A 83fb9073c86f
Agent Governance Reviewer is an agent published in the GitHub repository github/awesome-copilot (38,502 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 479 once invoked, about $0.0002 per session on Opus 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.
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