multi-agent-skill
/multi-agent-skill
Build multi-agent teams for real work in this repo. Use when adding a team topology, a harness, or a workspace under agent/teams, or when extending the shared patterns in agent/workflows.
- Author
- bichengwang ↗
- Repository
- multi-agents ↗
- Licence
- MIT
- Last repository change
- 27.10.2026
Assessment and security
- Files in this package
- 2
- Scripts
- 0
- HTML comments
- 0
- URLs in SKILL.md
- 0
No signals detected. Repository changed on 27.10.2026, without references to retired models or APIs.
MIT, open: use and modify while retaining author attribution.
What it does and when to use it
Build multi-agent teams for real work in this repo. Use when adding a team topology, a harness, or a workspace under agent/teams, or when extending the shared patterns in agent/workflows.
Original author description
Build multi-agent teams for real work in this repo. Use when adding a team topology, a harness, or a workspace under agent/teams, or when extending the shared patterns in agent/workflows.
How it works
- core logic view
- rules
- adding a harness
- adding a team topology
- tests
SKILL.md preview
---
name: multi-agent-skill
description: Build multi-agent teams for real work in this repo. Use when adding a team topology, a harness, or a workspace under agent/teams, or when extending the shared patterns in agent/workflows.
---
# multi-agent-skill
This repo runs teams of coding agents and measures whether a team beats a solo agent.
## core logic view
```
CodingTask ──► Team (topology) ──► TeamResult(patch, runs, usage, duration_s, meta)
│
├─ drives Harness : one coding agent run to completion (external CLI or SDK agent)
└─ in a Workspace : isolated checkout at the base commit (local worktree or sandbox)
Topologies reuse agent/workflows:
SequentialWorkflow : A → B → C
RefineLoop : generate ⇄ evaluate (feedback) until accepted; returns the best draft
BestOfN : N × (generate → evaluate) concurrently, keep the best
Router : classify → hand off to one specialist workflow
```
Contracts: `agent/teams/types.py` (see `reference/protocol.md`). Harnesses and teams touch the
repository only through `Workspace.exec`, so one team runs locally and in benchmark sandboxes.
## rules
1. Never change the contracts in `agent/teams/types.py` without updating every implementation.
2. Patterns in `agent/workflows` never call the SDK directly; they call an injected
`runner(agent, input_text) -> final_output`. Tests inject a fake runner.
3. Evaluators used by `RefineLoop` / `BestOfN` return an `agent.workflows.Verdict` subclass
(`score`, `passed`, `feedback`, plus domain fields).
4. Cost honesty: a harness that cannot report or price a call returns `Usage.cost_usd=None`;
never guess prices. Every harness call made by a team appears in `TeamResult.runs`.
5. Reviewers and judges must not mutate a worker's workspace; give them a fork.
6. The run budget (`--max-cost`, `agent/teams/budget.py`) is enforced by `build_team`, which wraps every
harness except the `check` pseudo-harness in `BudgetedHarness`. Topologies never call the budget:
a refused run raises `BudgetExceeded`, which they record like any failed run (`meta["failures"]`,
not in `TeamResult.runs`). Runs already admitted finish, so spend can overshoot; unpriced runs
block further runs unless `allow_unpriced`.
7. `plan` runs may carry the roles `coder`/`reviewer` inside subtasks (`reviewer=H`, a `ReviewTeam`
per subtask in its own fork) and extra `planner` runs (`replans=N`); subtasks are numbered across
rounds and replaced ones get status `replanned`.
## adding a harness
1. `agent/teams/harnesses/<name>.py`: implement `Harness.run` with `workspace.exec` only; parse
the CLI's machine-readable output into `HarnessRun` (final message, session id, usage, cost).
If the CLI reports cumulative usage on resume, subtract the session's previous total.
2. Register it in `agent/teams/spec.py` (`HARNESSES`, `build_harness`, `is_priced`).
3. Map `--permissions safe|bypass` to the CLI's own sandbox / bypass flags.
4. Capture real CLI output as sanitized fixtures in `tests/teams/fixtures/` and test parsing.
## adding a team topology
1. Implement it in `agent/teams/topologies.py` (or its own module, like `plan.py`) on top of an
`agent/workflows` pattern.
2. Add its spec kind and keys to `agent/teams/spec.py`; `Team.name` must be the canonical spec.
3. Test with fake harnesses on a temp git repo (`tests/teams/`): selection, failures, cleanup.
## tests
Offline and deterministic: `tests/workflows/fakes.py` (`FakeAgent`, `fake_runner`,
`scripted_evaluator`) for patterns; fake harnesses and temp git repos for teams. Run
`make test`, then `make check` before a PR.
Files in this package (2)
- SKILL.md 4 KB
- reference/protocol.md 2 KB
Versions
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