Agent skill

Review Multi Agent Orchestration

by sickn33 in sickn33/agentic-awesome-skills

A skill your agent uses when a supervisor, swarm, graph, planner-worker system, or parallel agent workflow needs review for task boundaries, shared state, branch joins, retries, cancellation…

MITAuto-check passedAgent Workflows

Install Review Multi Agent Orchestration

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill review-multi-agent-orchestration -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills review-multi-agent-orchestration --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/review-multi-agent-orchestration .claude/skills/review-multi-agent-orchestration && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
review-multi-agent-orchestration
GitHub stars
47k
Used in
1 other repo
Token cost
~2.6k tokens
SKILL.md length
1,175 words
Files
1
Skills in repo
1,497
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when a supervisor, swarm, graph, planner-worker system, or parallel agent workflow needs review for task boundaries, shared state, branch joins, retries, cancellation…

  • Works in 6 steps: Topology summary — nodes, edges, state… → Invariant table — invariant, enforcement… → Failure-path matrix — trigger, current… → …
  • Planner-worker system
  • SKILL.md covers Overview, When to Use, Capture the Orchestration… and Decide Whether Multi-Agent…, plus 10 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Review Multi Agent Orchestration is an agent skill from sickn33/agentic-awesome-skills. Use when a supervisor, swarm, graph, planner-worker system, or parallel agent workflow needs review for task boundaries, shared state, branch joins, retries, cancellation, context handoffs, budgets, deadlocks, or human escalation before implementation or production rollout.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Multi-agent orchestration and Subagents. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Planner-worker system
  • Parallel agent workflow needs review for task boundaries
  • Context handoffs
  • Human escalation before implementation

Example prompts

  • “/review-multi-agent-orchestration”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Topology summary — nodes, edges, state owner, storage, external effects, and human gates.
  2. Invariant table — invariant, enforcement point, evidence, and gap.
  3. Failure-path matrix — trigger, current behavior, blast radius, and required containment.
  4. Findings — severity, exact design element, failure scenario, and smallest viable correction.
  5. Recommended topology — only the components and policies needed to close findings.
  6. Validation plan — deterministic unit/model tests, concurrency tests, fault injection, replay, and end-to-end evidence.

What it can do on your machine

Read from SKILL.md and the folder at commit b84d35a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Review Multi Agent Orchestration loads about 2.6k tokens when it runs. Until then it costs about 77 tokens; SKILL.md has 1,175 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~77
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit b84d35a, republished under its MIT licence (© sickn33). 1,175 words, ~2,551 tokens.

Download SKILL.mdSave it as .claude/skills/review-multi-agent-orchestration/SKILL.md (or your agent's skills folder).
name
review-multi-agent-orchestration
description
Use when a supervisor, swarm, graph, planner-worker system, or parallel agent workflow needs review for task boundaries, shared state, branch joins, retries, cancellation, context handoffs, budgets, deadlocks, or human escalation before implementation or production rollout.
risk
safe
source
self
date_added
2026-08-19

Review Multi-Agent Orchestration

Overview

Review an orchestration as a distributed state machine, not as a list of agent roles. The goal is to prove that every task has one owner, every state transition has one authority, and every terminal outcome is reachable without duplicate effects, lost work, or unbounded loops.

This skill reviews a design or implementation. Do not launch workers, mutate queues, cancel runs, change production configuration, or deploy fixes unless the user separately requests implementation.

When to Use

  • Reviewing supervisor/worker, planner/executor, debate, swarm, graph, or hierarchical Agent designs.
  • Introducing parallel branches, subagents, MCP tools, durable execution, memory, checkpoints, or human-in-the-loop gates.
  • Diagnosing duplicate work, stale context, deadlocks, livelocks, branch races, runaway retries, or ambiguous ownership.
  • Deciding whether a complex task should be parallel, sequential, delegated, or kept in one agent.

Do not use it for a single independent tool call or a simple pipeline with no concurrency, shared state, retry, or delegation boundary.

Capture the Orchestration Contract

Request or derive:

  • business goal, success criteria, and non-goals;
  • task graph with stable task IDs and dependency edges;
  • agent roles, capabilities, permissions, tools, and sandbox boundaries;
  • state schema, source of truth, ownership, versioning, and persistence;
  • message envelopes and artifact handoff contracts;
  • dispatch, join, retry, timeout, cancellation, compensation, and escalation policies;
  • token, cost, concurrency, wall-clock, and external-effect budgets;
  • terminal states and evidence required to enter them.

Mark each field as declared, inferred, or missing. Never invent framework behavior from role names such as "supervisor" or "validator."

Decide Whether Multi-Agent Execution Is Justified

Multi-agent execution is justified when tasks have independently verifiable outputs and can be isolated by files, artifacts, permissions, or read-only scopes. Keep work sequential when one branch consumes another's evolving output, all workers must edit the same state, or coordination cost exceeds the expected parallel gain.

Score each candidate task:

DimensionParallel-safe evidence
DependencyInputs are frozen before dispatch
OwnershipOne writer owns each artifact or state partition
VerificationOutput has a local acceptance contract
ContextHandoff fits a bounded message or immutable artifact
Side effectsEffects are absent, isolated, or idempotent
FailureFailure can be contained without corrupting siblings

If any dimension is unresolved, recommend serialization or an explicit coordination mechanism rather than optimistic concurrency.

Model the State Machine

Represent task state explicitly:

text
pending -> ready -> leased -> running -> succeeded
                         |        |-> retry_wait -> ready
                         |        |-> needs_human
                         |        |-> failed
                         |        |-> cancelled
                         |-> lease_expired -> ready

For every transition record:

  • authorized actor;
  • compare-and-set precondition or expected state version;
  • persisted fields and artifact references;
  • emitted event and deduplication key;
  • budget consumed;
  • timeout or lease behavior;
  • compensation or recovery path.

Reject designs where workers overwrite the whole shared state object or where "done" is a free-form message rather than a validated transition.

Review Task and State Ownership

Each task needs one active lease owner, a fencing token or monotonically increasing attempt, and a stable idempotency key for external effects. A retry may repeat computation, but it must not repeat a committed effect.

Use one of these state patterns deliberately:

  • Single-writer coordinator: workers return proposals or artifacts; only the coordinator mutates canonical state.
  • Partitioned state: each worker owns a disjoint namespace; a joiner writes the aggregate.
  • Event log with reducers: workers append immutable events; deterministic reducers derive state.

Flag shared checkout edits, last-write-wins JSON blobs, mutable global memory, and unversioned summaries as collision risks.

Review Dispatch and Handoffs

A dispatch envelope should bind:

json
{
  "run_id": "run-7",
  "task_id": "backend-3",
  "attempt": 2,
  "parent_task_id": "migration-1",
  "input_artifacts": [{"uri": "artifact://schema", "digest": "sha256:..."}],
  "expected_output": "backend-contract-v1",
  "deadline": "RFC3339 timestamp",
  "budgets": {"tokens": 20000, "tool_calls": 40},
  "permissions": ["repo:backend:write", "tests:run"],
  "idempotency_key": "run-7:backend-3",
  "trace_parent": "trace-12"
}

Handoffs should pass the minimum sufficient context plus immutable artifact references. Verify that summaries preserve decisions, assumptions, unresolved questions, source citations, and version identity. Do not rely on shared conversational context as durable state.

Review Joins and Completion

Name the join rule for every fan-out:

  • all_required: continue only when every required branch succeeds;
  • quorum(k): continue after k valid results and cancel or ignore the rest by policy;
  • first_valid: continue after the first result that passes an acceptance predicate;
  • best_effort: collect until deadline and report missing branches;
  • manual_select: a human chooses among complete candidates.

first_finished is not first_valid. Define how late results, duplicate completions, branch cancellation, partial failure, and incompatible artifacts are handled. The joiner must validate artifact versions before moving the parent task to a terminal state.

Show full SKILL.md (510 more words)Show less

Review Failure Semantics

Check these paths explicitly:

FailureRequired policy
Worker crashLease expiry, checkpoint boundary, reassignment
TimeoutDeadline owner, cancellation propagation, late-result handling
Transient tool errorRetry classifier, cap, backoff, same idempotency key
Permanent errorFail/skip/escalate decision and downstream propagation
Corrupt outputSchema and semantic rejection without state advancement
Coordinator restartDurable queue/state recovery and fencing of stale workers
Human timeoutSafe default and bounded escalation
Compensation failureExplicit manual-recovery state

Look for retry storms, nested retry multiplication, orphaned workers, circular waits, approval deadlocks, and loops whose only exit is a model judgment. Require a deterministic step, time, or budget bound.

Review Memory and Reflection Loops

Separate:

  • task state required for correctness;
  • episodic run history;
  • reusable semantic memory;
  • scratch reasoning and reflection.

Correctness state must be durable and versioned; it must not depend on vector similarity or a model-generated summary. Memory writes need provenance, tenant/run scope, retention, conflict policy, and a rule for stale or poisoned entries.

Reflection loops need a measurable delta predicate, maximum iterations, budget decrement, and terminal action: accept, revise, escalate, or fail. "Reflect until good" is an unbounded loop.

Review Observability and Evidence

Require stable run_id, task_id, attempt, agent_id, state_version, trace_parent, and artifact digests across logs. The evidence should reconstruct dispatch, tool calls, state transitions, retries, joins, cancellations, approvals, and terminal verdicts without relying on agent narration.

Do not equate rich traces with correctness. Each terminal state still needs an acceptance predicate and an authoritative witness.

Produce the Review

Return:

  1. Topology summary — nodes, edges, state owner, storage, external effects, and human gates.
  2. Invariant table — invariant, enforcement point, evidence, and gap.
  3. Failure-path matrix — trigger, current behavior, blast radius, and required containment.
  4. Findings — severity, exact design element, failure scenario, and smallest viable correction.
  5. Recommended topology — only the components and policies needed to close findings.
  6. Validation plan — deterministic unit/model tests, concurrency tests, fault injection, replay, and end-to-end evidence.

Core invariants to include:

  • at most one active owner per task attempt;
  • monotonic state version and terminal-state immutability;
  • no committed effect executes more than once;
  • parent completion implies its declared join predicate;
  • cancellation reaches every owned child or records an orphan;
  • every loop and retry consumes a bounded budget;
  • a human-assisted outcome is not reported as autonomous success.

Common Mistakes

  • Adding agents for roles that do not own distinct outputs.
  • Sharing one writable checkout or mutable state file across parallel workers.
  • Using a supervisor's prose summary as the canonical state.
  • Retrying the whole graph when only one idempotent task failed.
  • Advancing on the first completion without validating it.
  • Letting child and parent retries multiply without a global cap.
  • Mixing durable task state with long-term vector memory.
  • Measuring throughput while ignoring coordination overhead and failure amplification.

Limitations

  • A static review cannot prove runtime scheduling, provider isolation, or exactly-once external effects; validate those claims in a harness.
  • Framework names do not establish durability or failure semantics. Inspect the configured runtime contract.
  • Recommendations should match the system's actual risk and scale; do not add queues, consensus, or databases when a single writer and immutable artifacts are sufficient.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/review-multi-agent-orchestration of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit b84d35a

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Harness Agent Team Designerrevfactory/harness9.1k—~4.5kAutomated safety check: PassApache-2.0
ClawTeam Multi-Agent Swarmwin4r/ClawTeam-OpenClaw1.5k1 repos~2.9kAutomated safety check: PassMIT
Sub-Agent Delegationcodewhale-hq/Codewhale41k—~790Automated safety check: PassMIT

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Categories

Questions about Review Multi Agent Orchestration

What does Review Multi Agent Orchestration do?

A skill your agent uses when a supervisor, swarm, graph, planner-worker system, or parallel agent workflow needs review for task boundaries, shared state, branch joins, retries, cancellation…. Review Multi Agent Orchestration is an agent skill from sickn33/agentic-awesome-skills. Use when a supervisor, swarm, graph, planner-worker system, or parallel agent workflow needs review for task boundaries, shared state, branch joins, retries, cancellation, context handoffs, budgets, deadlocks, or human escalation before implementation or production rollout.

When should I use Review Multi Agent Orchestration?

Review Multi Agent Orchestration fits situations like: planner-worker system; parallel agent workflow needs review for task boundaries; context handoffs; human escalation before implementation.

How do I install Review Multi Agent Orchestration in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill review-multi-agent-orchestration -a claude-code`. Or copy the skill folder (skills/review-multi-agent-orchestration in sickn33/agentic-awesome-skills) into .claude/skills/review-multi-agent-orchestration in your project. Claude Code loads it when a task matches its description.

How do I install Review Multi Agent Orchestration in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill review-multi-agent-orchestration -a codex`. Or copy the skill folder (skills/review-multi-agent-orchestration in sickn33/agentic-awesome-skills) into .agents/skills/review-multi-agent-orchestration in your project. Codex loads it when a task matches its description.

Can I use Review Multi Agent Orchestration in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill review-multi-agent-orchestration -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-multi-agent-orchestration, .gemini/skills/review-multi-agent-orchestration, .github/skills/review-multi-agent-orchestration and .opencode/skills/review-multi-agent-orchestration in your project.

What does Review Multi Agent Orchestration need to run?

SKILL.md names no scripts, command-line tools or credentials: Review Multi Agent Orchestration is instructions for the agent only.

Does Review Multi Agent Orchestration access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Review Multi Agent Orchestration safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Review Multi Agent Orchestration use?

Review Multi Agent Orchestration is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Review Multi Agent Orchestration use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Review Multi Agent Orchestration?

Skills that share tags, products or a category with Review Multi Agent Orchestration: Paseo Advisor Second Opinion (getpaseo/paseo, 20k stars), O2 Review Loop (openobserve/openobserve, 22k stars), Harness Agent Team Designer (revfactory/harness, 9.1k stars) and ClawTeam Multi-Agent Swarm (win4r/ClawTeam-OpenClaw, 1.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Multi Agent Orchestration?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,405 GitHub stars. The repository holds 1,497 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.