agentleFS
3338902669-ops3338902669-ops/agent-orchestra/agent-team-os/SKILL.md

agent-team-os

Skill93 starsChanged 41 days agoApache-2.0View on GitHub

Run a team of AI agents (or one agent in several role sessions) as one governed unit — layered long-term memory, hub-coordinated governance with evidence anchors and human approval gates, an identity-based agent mailbox with receipts, and an event-driven doorbell that wakes seats on new mail without polling. Use when setting up or operating a multi-agent team, when agents must share durable memory and verifiable hand-offs, or when a single agent should work in the same pattern across multiple sessions. Do not use for adversarial or multi-tenant environments, or for any flow where a single message by itself authorizes payment, deployment, or credential release — on the mailbox's CLI path the sender is self-declared.

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 2 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Every seat has a fixed mail identity.

    line 30
  2. Distinctive:

    No polling anywhere. A watcher on the mailbox storage fires on writes, consumes durable per-recipient delivery events through persistent …

    line 37
  3. Very specific:

    Every failure is reported as a named error with a non-zero exit code.

    line 91

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill 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
SKILL.md

Agent Team OS

A complete operating pattern for a small team of AI agents coordinated by one hub, designed so that work is remembered, verifiable, and never silently lost — and so that a human stays in charge of anything irreversible.

The four layers

  1. Layered memory — what the team knows. A curated long-term memory file, daily logs, person/group profiles, a temporal knowledge graph, shared rule fragments distributed to every seat, and periodic digests. Each layer has strict rules for what belongs in it. → references/memory-model.md
  2. Governance — how the team is allowed to act. One coordinator is the human's single entry point. A roster assigns one owner (single writer) per resource. Cross-agent relays must carry evidence anchors (path / hash / raw output); no artifact = no conclusion. The verifier is never the implementer. Passing a review gate is not delivery: outward or irreversible actions (send, publish, delete, deploy, pay, change settings) need per-item human approval. → references/governance.md
  3. Identity mailbox — how seats communicate durably. Every seat has a fixed mail identity. Messages carry ids, threads, delivery receipts, and acknowledgements; landed mail is never lost (store-and-forward). File reservations stop two seats editing the same file. Non-MCP seats use a thin helper script. → references/comms-mail.md, template: templates/scripts/mail-helper.ps1
  4. Event-driven doorbell — how seats hear about new mail in real time. No polling anywhere. A watcher on the mailbox storage fires on writes, consumes durable per-recipient delivery events through persistent cursors, and dispatches each recipient's native wake (CLI round, bridge task, task-card CLI, GUI paste, coordinator marker file). → references/doorbell-event-driven.md, template: templates/scripts/doorbell.ps1

Modes

  • Multi-agent mode — several different agents hold the seats.
  • Single-agent mode — one agent runs several sessions, each session plays one seat with the same roster, mailbox, memory layers, and governance. The doorbell simplifies to a session-level trigger. Everything else is identical, so scaling from one agent to many replaces seats, not the system. → references/single-agent-mode.md

Seats are connected through adapters (MCP-native, CLI, GUI desktop, API, file-only); the compatibility matrix defines what each type needs and how it degrades. → references/adapters-compatibility.md

When to use this skill

  • You run (or are setting up) more than one agent — or one agent in several role sessions — and they must coordinate over days, not minutes.
  • Work keeps getting lost between sessions, or hand-offs can't be verified.
  • You need a human approval gate between "an agent says it's done" and "something happened in the real world".

When NOT to use it

  • A single one-shot agent task with no follow-up: the overhead buys nothing.
  • Adversarial settings where seats must not trust each other: the mailbox's CLI path treats sender identity as self-declared (see the known limitation in references/comms-mail.md).

Getting started

  1. Cold-start from zero communication: references/bootstrap-from-zero.md (each phase has an acceptance check; do not skip phases).
  2. Or take the 30-minute path: QUICKSTART.md.
  3. Instantiate the templates: templates/ (placeholder list: templates/PLACEHOLDERS.md).

Verification

Five suites — hygiene, compatibility, usability, error behaviour, reproducibility — are implemented by scripts/check.py and specified in references/verification.md:

python3 scripts/check.py              # all suites
python3 scripts/check.py --category COMPAT   # one suite
python3 scripts/check.py --self-test  # poisoned-fixture negative test

Every failure is reported as a named error with a non-zero exit code; reporting failures honestly is part of the system, not an exception to it (references/error-handling.md).

Download now (ZIP)

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.