Install
openclaw skills install @tinadu-ai/multi-agent-governorGovern any task that uses or considers multiple agents, subagents, delegation, parallel work, or speed-first execution. Use whenever the user says "多派 Agent", "并行", "省时间", "不用省 token", "many agents", or asks several agents to work or review simultaneously. Convert the request into bounded parallel lanes, route mechanical work to tools/scripts, issue minimal context capsules, enforce agent and QA limits, protect artifact freshness, wait for long-running sessions correctly, and report actual time and token usage.
openclaw skills install @tinadu-ai/multi-agent-governorTreat parallelism as an execution budget, not an agent-count target. Optimize for elapsed time and correctness while preventing duplicated work, stale reviews, full-history forks, and unbounded QA loops.
fork_context: false when the platform supports it.User phrases such as "多多多派 Agent" or "不用省 token" raise urgency and evidence coverage. They do not override these limits.
Before spawning, classify every work item:
| Work item | Execute with |
|---|---|
| Independent expert judgment or bounded writing | Agent |
| File reads, searches, API calls, shell commands | Approved parallel/batch tool calls |
| Counting, hashing, schema checks, formulas, overflow, string scans | Deterministic script |
| Critical path, conflict resolution, final edits | Main agent |
Do not use agents merely to parallelize I/O. If fewer than 2 independent reasoning lanes exist, keep the task local or use only 1 sidecar agent even when the user asks for many.
Read references/orchestration-policy.md when decomposition or model routing is non-obvious.
Identify:
Create a manifest:
python3 scripts/init_run.py \
--objective "<objective>" \
--source /absolute/path/to/input \
--mode fast-deep \
--deadline-minutes 90
Use --main-thread-id when the current platform exposes a stable thread id. Store temporary run state under /tmp/multi-agent-runs/; do not place it beside user deliverables.
Create one capsule per independent lane:
python3 scripts/add_capsule.py \
--manifest /tmp/multi-agent-runs/<run-id>/run-manifest.json \
--lane-id evidence-qa \
--role explorer \
--objective "Verify financing and source claims" \
--input /absolute/path/to/workbook.xlsx \
--deliverable "Structured findings only" \
--acceptance "Every finding includes file/sheet/range evidence" \
--exclusion "Do not edit source files"
Pass the capsule path and necessary file paths to the agent. Do not paste the long conversation, entire file contents, intended conclusion, or another agent's diagnosis. See references/agent-contracts.md.
scripts/update_run.py agent-start.When a command returns a session id, register it and poll that exact session until exit_code is present. Never rerun a command merely because the first call returned no output.
The main agent reviews findings, resolves conflicts, and writes the integrated artifact once. Agents must not concurrently edit the same deliverable.
Register the integrated build and hashes:
python3 scripts/update_run.py build \
--manifest /tmp/multi-agent-runs/<run-id>/run-manifest.json \
--artifact /absolute/path/to/output
Run format, schema, test, count, hash, formula, overflow, link, and prohibited-language checks before asking agents to review. Domain skills supply their own gates.
Then verify build freshness:
python3 scripts/check_freshness.py \
--manifest /tmp/multi-agent-runs/<run-id>/run-manifest.json \
--include-sources
Use no more than 2 non-overlapping QA lenses, such as:
Every QA capsule must include the current build_id and artifact hashes. Do not edit artifacts while QA is running. Discard a result if freshness fails. Fix verified blockers in one batch. Do not spawn another broad review wave for cosmetic suggestions.
Read references/quality-gates.md for severity and stop rules.
python3 scripts/collect_usage.py \
--manifest /tmp/multi-agent-runs/<run-id>/run-manifest.json \
--write
Stop when all deterministic acceptance tests pass and no blocking QA finding remains. Do not continue because another reviewer might produce a different preference.
Escalate to the user before exceeding 5 agents, starting a second QA wave, changing frozen source files, or resolving a genuine product/business judgment that cannot be inferred safely.