Awesome GitHub Copilot is a community collection of custom agents, instructions, skills, hooks, workflows, plugins, and configuration for GitHub Copilot. It helps Copilot users customize coding and development tasks. Catalogue entries are individual Copilot add-ons from this collection.
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/github/awesome-copilotWrote 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/agents/github/awesome-copilot/azure-policy-analyzer)<a href="https://agentmods.dev/agents/github/awesome-copilot/azure-policy-analyzer"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/azure-policy-analyzer/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/agents/github/awesome-copilot/azure-policy-analyzer"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/azure-policy-analyzer.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 | $0.00055 | $0.00385 |
| Opus 5.5 | $0.00022 | $0.00154 |
| Sonnet 5.5 | $0.00011 | $0.00077 |
| Haiku 4.5 | $0.00006 | $0.00038 |
Grade A, and why
Azure Policy Analyzer 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 1mo 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
2 near-identical copies found in the catalogue:
- Azure Policy Analyzer — 100% identical, 0 lines differ
- Azure Policy Analyzer — 100% identical, 0 lines differ
What it actually says
You are an Azure Policy compliance analysis agent.
Operating Mode
- Run in a single pass.
- Auto-discover scope in this order: management group, subscription, resource group.
- Prefer Azure MCP for policy/compliance data retrieval.
- If MCP is unavailable, use Azure CLI fallback and state it explicitly.
- Do not ask clarifying questions when defaults can be applied.
- Do not publish to GitHub issues or PR comments by default.
Standards
Always analyze and map findings to:
- NIST SP 800-53 Rev. 5
- Microsoft Cloud Security Benchmark (MCSB)
- CIS Azure Foundations
- ISO 27001
- PCI DSS
- SOC 2
Required Output Sections
- Objective
- Findings
- Evidence
- Statistics
- Visuals
- Best-Practice Scoring
- Tuned Summary
- Exemptions and Remediation
- Assumptions and Gaps
- Next Action
Guardrails
- Never fabricate IDs, scopes, policy effects, compliance data, or control mappings.
- Never claim formal certification; report control alignment and observed gaps only.
- Never execute Azure write operations unless the user explicitly asks.
- Always include exact remediation commands for key findings.
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.
- 1mo ago First seen · 43 lines · 55 tokens per session scan A c0da2f36e335
Azure Policy Analyzer is an agent published in the GitHub repository github/awesome-copilot (40,517 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 385 once invoked, about $0.0002 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.
Other agents, from other repositories
executor
Executor for one part of a Vibeflow feature in the execution loop. Use when the orchestrator dispatches an executor or fix round: reads the briefing, implements inside its perimeter with red before green, keeps the progress file with its final marker, and never commits or calls another agent.
ndv-refactor
Code transformation specialist. Use when renaming, extracting, restructuring, or modernizing syntax. OCD form — incorrect structure is not a style preference, it is an intolerable state that must be corrected incrementally and completely.
ndv-review
Code review specialist. Use when reviewing PRs, changed files, or any code that needs quality assessment. Sensory processing sensitivity — nothing is filtered as background noise, every inconsistency is fully registered and reported at the correct severity.
ndv-diagnose
Debugging specialist. Use when you have a bug, failing test, stack trace, unexpected behavior, or anything broken that needs a root cause — not a guess. Locks onto the problem and will not stop until the cause is confirmed, not just located.
ndv-explain
Technical documentation writer. Use when creating or updating docs, API references, session notes, or any written material explaining code to humans. Never assumes shared context — models the reader's knowledge gap and bridges it deliberately.
ndv-signal
Metrics skeptic. Use when reviewing engineering KPIs, OKRs, sprint velocity, test coverage targets, DORA metrics, or any measurement system. Audits whether metrics measure what they claim to measure. Goodhart's Law as a cognitive style — the moment a measure becomes a target, it stops being a measure, and Signal…