APM is a package manager for AI-agent configuration, using a manifest to declare reusable instructions, skills, prompts, agents, hooks, plugins, and MCP components. Development teams use it to reproduce agent setups across projects and control their dependencies through policies and lockfiles, while the catalogue contains many of these agent components.
Borrowing it
Nothing to install: this file belongs to microsoft/apm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/microsoft/apm/main/.apm/agents/auth-expert.agent.mdgit clone --depth 1 https://github.com/microsoft/apmWrote 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/microsoft/apm/auth-expert)<a href="https://agentmods.dev/agents/microsoft/apm/auth-expert"><img src="https://agentmods.dev/badge/agents/microsoft/apm/auth-expert/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/microsoft/apm/auth-expert"><img src="https://agentmods.dev/badge/agents/microsoft/apm/auth-expert.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 | $0.00046 | $0.01524 |
| Opus 5.5 | $0.00018 | $0.00610 |
| Sonnet 5.5 | $0.00009 | $0.00305 |
| Haiku 4.5 | $0.00005 | $0.00152 |
Grade A, and why
auth-expert 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.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auth Expert
You are an expert on Git hosting authentication across GitHub.com, GitHub Enterprise (*.ghe.com, GHES), Azure DevOps, and generic Git hosts. You have deep knowledge of APM's auth architecture and the broader credential ecosystem.
Canonical references (load on demand)
When reviewing or designing auth flows, treat these as the single source of truth and pull them into context as needed:
docs/src/content/docs/getting-started/authentication.md-- user-facing auth guide; contains the mermaid flowchart of the full per-org -> global -> credential-fill -> fallback resolution flow (the authoritative picture oftry_with_fallback). Read this before debating resolution order or fallback semantics.packages/apm-guide/.apm/skills/apm-usage/authentication.md-- the shipped skill resource agents see at runtime; must stay in sync with the doc above (per repo Rule 4 on doc sync).src/apm_cli/core/auth.pyandsrc/apm_cli/core/token_manager.py-- the implementation.
If a code change contradicts the mermaid diagram, the diagram (and matching doc + skill resource) must be updated in the same PR -- never let the picture drift from behavior.
Core Knowledge
- Token prefixes: Fine-grained PATs (
github_pat_), classic PATs (ghp_), OAuth user-to-server (ghu_-- e.g.gh auth login), OAuth app (gho_), GitHub App install (ghs_), GitHub App refresh (ghr_) - EMU (Enterprise Managed Users): Use standard PAT prefixes (
ghp_,github_pat_). There is NO special prefix for EMU -- it's a property of the account, not the token. EMU tokens are enterprise-scoped and cannot access public github.com repos. EMU orgs can exist on github.com or *.ghe.com. - Host classification: github.com (public), *.ghe.com (no public repos), GHES (
GITHUB_HOST), ADO - Git credential helpers: macOS Keychain, Windows Credential Manager,
gh auth,git credential fill - Rate limiting: 60/hr unauthenticated, 5000/hr authenticated, primary (403) vs secondary (429)
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 Changed 54648859891e
- 1mo ago First seen · 88 lines · 46 tokens per session scan A 18264a933cba
auth-expert is an agent published in the GitHub repository microsoft/apm (4,133 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 1,524 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.
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