multi-agent-orchestration
Skill1 starsChanged 17 days agoMITView on GitHub
Design and evaluate supervisor, router, handoff, and parallel-worker agent patterns with clear ownership, bounded delegation, structured messages, and conflict resolution.
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 14 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 9
Use multiple agents only when specialization, parallelism, or isolation creates measurable value.
- Distinctive: line 14
Choose orchestration: Use a router for classification, a supervisor for dynamic delegation, explicit handoffs for control transfer, or …
- Distinctive: line 25
Keep sequential dependencies explicit.
- Distinctive: line 26
Use a reviewer agent only when it has clear criteria and can inspect the actual artifact/evidence.
- Distinctive: line 27
Avoid majority voting among agents that share the same model, prompt, or source bias as a substitute for verification.
“Common” is measured: kept by at least 6 in 10 of the 14 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 Orchestration
Goal
Use multiple agents only when specialization, parallelism, or isolation creates measurable value. Make delegation bounded, auditable, and recoverable.
Workflow
- Prove the need. Compare a single-agent or deterministic baseline with the proposed multi-agent design.
- Define roles. Each agent needs a narrow responsibility, input contract, output contract, tool allowlist, and stop condition.
- Choose orchestration. Use a router for classification, a supervisor for dynamic delegation, explicit handoffs for control transfer, or parallel workers for independent tasks.
- Bound delegation. Set maximum depth, number of agents, wall time, token/cost budget, retries, and tool calls. Prevent recursive spawning loops.
- Structure messages. Include task objective, relevant context, constraints, evidence references, expected output schema, and completion criteria. Avoid passing entire conversation histories by default.
- Isolate privileges. Give each agent only the data and tools required. Never assume an agent's identity or assertions establish authorization.
- Handle parallel results. Define merge rules, deduplication, conflict detection, source precedence, and what happens when workers disagree or fail.
- Verify outputs. Validate specialist results against source evidence and task requirements before taking action or presenting conclusions.
- Observe the delegation graph. Trace parent/child calls, costs, failures, retries, and decisions.
- Evaluate. Measure end-to-end success, latency, cost, duplicated work, disagreement rate, and policy violations against the baseline.
Coordination patterns
- Parallelize independent, bounded research or analysis.
- Keep sequential dependencies explicit.
- Use a reviewer agent only when it has clear criteria and can inspect the actual artifact/evidence.
- Avoid majority voting among agents that share the same model, prompt, or source bias as a substitute for verification.
Failure handling
Define behavior for worker timeout, malformed output, partial results, contradictory claims, supervisor failure, cancellation, and budget exhaustion. Preserve completed work when safe, but do not repeat external side effects blindly.
Deliverable
Provide a delegation diagram, role contracts, state transitions, budget limits, permissions, merge/conflict policy, trace fields, and comparative evaluation.
References
- Microsoft multi-agent design lessons: https://github.com/microsoft/ai-agents-for-beginners/tree/main/08-multi-agent
- LangGraph subgraphs: https://docs.langchain.com/oss/python/langgraph/use-subgraphs
- OpenAI Agents SDK handoffs: https://openai.github.io/openai-agents-python/handoffs/
Other versions17 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 17
- mizchi/skills★ 369same name
- DevelopersGlobal/ai-agent-skills★ 216same name
- roedyrustam/vibes-plug★ 75same name
- VoDaiLocz/kilo-kit-mcp★ 28same name
- composio-community/opencode-skills★ 14same name
- DavidTeju/shared-skills★ 9same name
- mnzralee/claude-multi-agent-architecture★ 8same name
- uvwt/agentdock-skills★ 6same name
- javalenciacai/QASkills★ 5same name
- thejordanleopold/claude-code-skills-distilled★ 4same name
7 more not listed
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.
Used alongsideProjects with it also use refactoring, observability and 3 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
pproencaSkillrefactoring
Code refactoring best practices based on Martin Fowler's catalog and Clean Code principles. This skill should be used when refactoring existing code, improving code structure, reducing complexity, eliminating code smells, or reviewing code for maintainability. Triggers on tasks involving extract method, rename, decompose conditional, reduce coupling, or improve readability.
Also in 3 projects that use this
OpenAISkillobservability
Vercel Observability expert guidance — Drains (logs, traces, speed insights, web analytics), Web Analytics, Speed Insights, runtime logs, custom events, OpenTelemetry integration, and monitoring dashboards. Use when instrumenting, debugging, or optimizing application performance and user experience on Vercel.
Also in 3 projects that use this
MicrosoftSkillapi-design
Guide for designing and documenting RESTful APIs. Use when asked to design an API, create endpoints, or document an API.
Also in 4 projects that use this
lodetomasiSkilltdd-workflow
Test-Driven Development workflow with red-green-refactor pattern and best practices
Also in 3 projects that use this
Microsoft AzureSkillgit-workflow
Squad branching model: dev-first workflow with insiders preview channel
Also in 3 projects that use this
In this repository28 other files it gives its agents.
- agent-architecture-designSkill
- agent-evaluationSkill
- agent-harness-engineeringSkill
- agentic-workflow-testingSkill
- agent-memory-designSkill
- browser-computer-useSkill
- durable-agent-workflowsSkill
- human-in-the-loopSkill
- mcp-developmentSkill
- mcp-server-developmentSkill
- tool-calling-designSkill
- advanced-retrieval-rerankingSkill
- context-engineeringSkill
- embedding-model-engineeringSkill
- llm-evaluationSkill
- llm-observabilitySkill
- model-serving-inferenceSkill
- multimodal-ai-pipelinesSkill
- prompt-engineeringSkill
- rag-engineeringSkill
- rag-pipeline-engineeringSkill
- structured-outputsSkill
- agentic-securitySkill
- security-reviewSkill
- code-reviewSkill
- debuggingSkill
- test-engineeringSkill
- example-skillSkill
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.

