multi-agent-collaboration
Skill2 starsChanged 13 days agoMITView on GitHub
Coordinate multiple agents on implementation or research with isolated ownership, reproducible handoffs, independent checks, and evidence-based conflict resolution. Use when parallel or peer-agent work is requested or already in progress.
6 other projects use a version of this.
Adopted by
Most recently taken up by
- TianMingKanae/engineering-multi-ai-skillstheir own version, first seen Oct 20, 2026
- lzhs1995/multi-agent-collaborationtheir own version, first seen Oct 18, 2026
- mattstruble/skillstheir own version, first seen Jul 22, 2026
- varunk130/ai-workflow-playbookstheir own version, first seen May 22, 2026
- hajekim/agentic-design-patterns-extensiontheir own version, first seen Apr 1, 2026
- hajekim/agentic-design-patterns-skillstheir own version, first seen Apr 1, 2026
Other versions6 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 6
- TianMingKanae/engineering-multi-ai-skills★ 6same name
- hajekim/agentic-design-patterns-skills★ 4same name
- mattstruble/skills★ 2same name
- varunk130/ai-workflow-playbooks★ 2same name
- hajekim/agentic-design-patterns-extension★ 1same name
- lzhs1995/multi-agent-collaboration★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository11 other files it gives its agents.
- AGENTS.mdAGENTS.md
- aeoSkill
- agent-worktree-orchestrationSkill
- critical-adversarial-reviewSkill
- evidence-driven-debuggingSkill
- gauntlet-loopSkill
- git-quality-hooksSkill
- quality-gatesSkill
- source-backed-researchSkill
- spec-to-planSkill
- validated-coding-loopSkill
The file
We split this skill into the rules it gives an agent
5 rules. 0 are common, 5 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 6 edited versions we read keep these.
5
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 12
Read the continuation record and outstanding messages before assigning work.
- Distinctive: line 22
Use separate worktrees for concurrent code changes.
- Distinctive: line 28
Use built-in messaging first.
- Distinctive: line 38
When a verified result contradicts an earlier analysis, revise the analysis and record what changed.
- Distinctive: line 44
Before changing agent, branch, or context, update the project-owned continuation record with objective, current revision, completed checks, …
“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Multi-agent collaboration
Produce a result another agent can reproduce and continue. Use the host's agent, task, or messaging tools when available. A shared document or file queue is sufficient when separate sessions need a mailbox.
Choose a useful split
Read the continuation record and outstanding messages before assigning work. Identify the objective, acceptance evidence, allowed changes, shared dependencies, and coordinator.
Split by independent questions, hypotheses, or owned files. A literature check and an implementation experiment can proceed together; two agents rewriting the same module need isolation or a sequential handoff. Use one agent when coordination would cost more than it resolves.
Each assignment needs:
- A concrete question or deliverable and the minimum source context.
- Ownership: writable paths or worktree, read-only dependencies, and actions outside scope.
- Acceptance evidence and the artifact the next agent will receive.
- A stopping condition or budget when the task has one.
Use separate worktrees for concurrent code changes. Agents sharing a filesystem must have non-overlapping writes. The coordinator owns integration and reruns acceptance checks after combining changes.
Exchange reproducible evidence
For substantial handoffs, use the message contract. Include the exact revision, configuration, input or fixture identity, command, result, and artifact path. Label observations, interpretations, and unresolved claims separately.
Use built-in messaging first. If a mailbox is necessary, record a stable message ID and persist the cursor only after processing the message. Keep messages bounded and point to large artifacts. Do not put credentials or full private datasets in messages.
Worker conclusions remain hypotheses until supported by sufficient evidence. Reproduce consequential claims independently, especially where a different environment, validator, or metric could change the decision.
Resolve conflicts
Find the smallest test that distinguishes the explanations. Compare inputs, revisions, defaults, units, measurement windows, validation modes, and external reference versions.
A timeout or missing artifact is unknown. Agreement between agents is supporting evidence only when their methods and inputs justify it. Do not turn repeated opinions into a measured result.
When a verified result contradicts an earlier analysis, revise the analysis and record what changed. Share failed hypotheses and their reopening conditions to avoid duplicate retries.
Converge and preserve
Select a candidate using the acceptance criteria. Integrate it in one owned lane, verify the combined result, and record rejected alternatives and remaining uncertainty.
Before changing agent, branch, or context, update the project-owned continuation record with objective, current revision, completed checks, artifacts, blockers, and the next action. Idle user time alone is not a stopping condition.
Task authorization does not imply permission to publish, deploy, merge, contact others, or spend outside an agreed budget.
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
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.

