multi-agent-orchestration

multi-agent-orchestration is a skill for Codex from seb1n/awesome-ai-agent-skills. It costs 71 tokens per session (1,089 once invoked), scanned A, original, MIT.

A method for coordinating several software agents on one larger objective. It divides work into connected tasks with assigned owners, dependencies, handoff rules, shared-state limits, and review points.

In plain words
What is it for?
Planning parallel research or implementation, setting up worker-and-reviewer workflows, controlling shared files, recovering failed work, and combining results.
Why use it?
It reduces duplicated work, conflicting edits, unclear handoffs, and stalled tasks when multiple agents contribute. It also defines how failures and disagreements are handled.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Planning parallel research or implementation, setting up worker-and-reviewer workflows, controlling shared files, recovering failed work, and combining results.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/seb1n/awesome-ai-agent-skills/multi-agent-orchestration
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add seb1n/awesome-ai-agent-skills --skill multi-agent-orchestration
Clone the repo
git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for multi-agent-orchestration

README.md
[![agentmods](https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/multi-agent-orchestration/github.svg)](https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/multi-agent-orchestration)
Your own site
<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/multi-agent-orchestration"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/multi-agent-orchestration/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for multi-agent-orchestration

Your own site · 80×15
<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/multi-agent-orchestration"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/multi-agent-orchestration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 71 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,089 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00071 $0.01089
Opus 5 $0.00036 $0.00544
Sonnet 5 $0.00014 $0.00218
Haiku 4.5 $0.00007 $0.00109

Measured 11d ago against content hash 6a7f61e5adb5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

multi-agent-orchestration scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/validate_plan.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agent-engineering/multi-agent-orchestration/SKILL.md · 75 lines

How it starts

The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Multi-Agent Orchestration

Use multiple agents only when specialization or safe parallelism outweighs coordination cost.

Use when

  • Split a large objective into independent, verifiable workstreams.
  • Coordinate specialists that need distinct tools, permissions, or context.
  • Run worker-reviewer, planner-executor, map-reduce, or bounded debate patterns.
  • Diagnose duplicate work, conflicting edits, weak handoffs, or stalled dependencies.

Do not delegate a tightly coupled, small, or inherently sequential task merely to increase agent count.

Inputs

Collect the objective, completion criteria, task graph, available agents and tools, concurrency limits, shared files or systems, authority boundaries, deadlines, budget, and final decision owner. State assumptions and unresolved dependencies.

Output contract

Produce:

  1. A decomposition rationale and explicit non-goals.
  2. A directed acyclic task graph with owner, dependencies, inputs, output contract, write scope, and verification for every task.
  3. A handoff protocol and shared-state policy.
  4. Approval points, timeout and retry limits, escalation routes, and stop conditions.
  5. A synthesis plan that resolves disagreements and verifies the integrated result.
  6. A completion report with evidence, remaining uncertainty, and unused or failed branches.

Workflow

  1. Define one measurable objective and the authority boundary before assigning work.
  2. Decompose by separable outputs, not vague roles. Keep shared mutable state to a minimum and retain tightly coupled steps under one owner.
  3. Draw dependencies and identify the critical path. Parallelize only tasks with independent inputs and non-overlapping side effects. Read orchestration-patterns.md when choosing a topology.
  4. Assign one accountable owner per task. Specify inputs, deliverable format, write scope, validation, deadline or timeout, and what warrants escalation.
  5. Give each agent the minimum context and permissions needed. Include source artifacts, not hidden conclusions, when independent judgment matters.
  6. Require structured handoffs: status, result, evidence, changed state, assumptions, risks, and next dependency. Acknowledge receipt before downstream mutation.
  7. Monitor dependency state and useful progress. Bound retries and debates; do not recursively delegate without a clear capacity and ownership model.
  8. Synthesize centrally or through a named integrator. Resolve conflicting claims from primary evidence, run integration checks, and confirm the original completion criteria.
  9. Close or cancel unused work, record unresolved risks, and return control to the final decision owner.

Read the full file on GitHub · 75 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 75 lines · 71 tokens per session scan A 6a7f61e5adb5

Subscribe to this mod's changes

multi-agent-orchestration is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 71 tokens to every session and 1,089 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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