agentleFS
aadi-labsaadi-labs/agentmailer-plugins/AGENTS.md

agentmailer-plugins

AGENTS.md1 starsChanged 3 months agoMITView on GitHub

AgentMailer is identity and communication infrastructure for AI agents. It is not a workflow engine or an agent workspace. Keep model calls, tool execution, schedules, checkpoints, and business approvals in the calling runtime. - Treat top-level skill directories as the authoring source for skills.sh. - Treat skills/ as a generated client export. Update it with the repository's generation scripts instead of editing exported skills independently. - Treat fern/openapi/openapi.json as a reviewed snapshot of https://api.agentmailer.ai/openapi.json. Run the sync check before changing…

The file

We split this AGENTS.md into the rules it gives an agent

9 rules. About 2 are common, 1 is distinctive, and 6 are very specific to this project.

  • ≈2

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 6

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  • could be automatic: a setting could enforce it
  1. Very specific:

    Treat top-level skill directories as the authoring source for skills.sh.

    line 9
  2. Very specific:

    Treat skills/ as a generated client export.

    line 10
  3. Very specific:

    Treat fern/openapi/openapi.json as a reviewed snapshot of https://api.agentmailer.ai/openapi.json.

    line 12
  4. Very specific:

    Do not hand-edit generated API methods under sdk/.

    line 15
  5. Very specific:

    Keep the portable Agent Plugins manifest at plugin.json and the hosted Streamable HTTP MCP connection at mcp.json standards-conformant.

    line 16
  6. Common:

    Never guess API fields, MCP tools, permissions, or credentials.

    line 21
  7. Common:

    Never print or commit AgentMailer access tokens, API keys, mailbox passwords, approval codes, or webhook signing secrets.

    line 23
  8. Distinctive:

    Use stable idempotency keys for retried writes.

    line 25
  9. Very specific:

    Run pnpm validate after changing manifests, skills, examples, SDK metadata, or client exports.

    line 30

≈ “Common” is an estimate: at least 1 in 5 of the 40,246 AGENTS.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
AGENTS.md

Agent instructions

AgentMailer is identity and communication infrastructure for AI agents. It is not a workflow engine or an agent workspace. Keep model calls, tool execution, schedules, checkpoints, and business approvals in the calling runtime.

Source boundaries

  • Treat top-level skill directories as the authoring source for skills.sh.
  • Treat skills/ as a generated client export. Update it with the repository's generation scripts instead of editing exported skills independently.
  • Treat fern/openapi/openapi.json as a reviewed snapshot of https://api.agentmailer.ai/openapi.json. Run the sync check before changing generated clients.
  • Do not hand-edit generated API methods under sdk/.
  • Keep the portable Agent Plugins manifest at plugin.json and the hosted Streamable HTTP MCP connection at mcp.json standards-conformant.

Safe integration behavior

  • Never guess API fields, MCP tools, permissions, or credentials. Use the published OpenAPI and MCP schemas.
  • Never print or commit AgentMailer access tokens, API keys, mailbox passwords, approval codes, or webhook signing secrets.
  • Use stable idempotency keys for retried writes.
  • Preserve human approval for agent signup and externally consequential sends.

Validation

Run pnpm validate after changing manifests, skills, examples, SDK metadata, or client exports. Run the relevant generated-client checks for SDK or CLI changes.

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Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.