multi-agent-orchestration

multi-agent-orchestration is a skill for Claude Code, Codex from jnPiyush/AgentX. It costs 83 tokens per session (1,317 once invoked), scanned A, original, Apache-2.0.

A guide to coordinating several specialized AI agents so they can share work on a larger task. It covers patterns such as supervisors, pipelines, handoffs, swarms, and hierarchies.

In plain words
What is it for?
It helps design agent teams for tasks such as research, coding, review, delegation, cross-agent communication, and long-running workflows.
Why use it?
A single agent can lose focus or run out of context when a task spans multiple specialties or lasts a long time. Separate roles and clear handoffs make the work easier to organize and inspect.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps design agent teams for tasks such as research, coding, review, delegation, cross-agent communication, and long-running workflows.

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Install with agentmods
npx agentmods add skills/jnpiyush/agentx/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 jnPiyush/AgentX --skill multi-agent-orchestration
Clone the repo
git clone --depth 1 https://github.com/jnPiyush/AgentX

Made for: Claude Code, 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/jnpiyush/agentx/multi-agent-orchestration/github.svg)](https://agentmods.dev/skills/jnpiyush/agentx/multi-agent-orchestration)
Your own site
<a href="https://agentmods.dev/skills/jnpiyush/agentx/multi-agent-orchestration"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/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/jnpiyush/agentx/multi-agent-orchestration"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/multi-agent-orchestration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 83 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,317 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 $0.00083 $0.01317
Opus 5.5 $0.00033 $0.00527
Sonnet 5.5 $0.00017 $0.00263
Haiku 4.5 $0.00008 $0.00132

Measured 7d ago against content hash b0ddbb20191f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 7d ago.

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.

.github/skills/ai-systems/multi-agent-orchestration/SKILL.md · 161 lines

How it starts

The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Multi-Agent Orchestration

Purpose: Coordinate multiple specialized agents to solve tasks no single agent can handle reliably. Scope: Topologies, handoff protocols, framework selection, failure modes, anti-patterns.


When to Use This Skill

  • Task spans multiple specialties (research + code + review) and a single agent loop loses focus
  • Need explicit role separation for auditability or compliance
  • Long-horizon tasks where one agent's context budget is insufficient
  • Cross-organization agent communication (A2A protocol)

When NOT to Use Multi-Agent

  • Single-domain task -- a well-prompted single agent is cheaper and more reliable
  • Latency-sensitive (<1s) -- handoffs add round-trips
  • Tasks solvable by tool calls alone -- prefer tool-use-and-function-calling

Topology Decision Tree

What is the task structure?
+- Linear pipeline (research -> draft -> review)?
|  -> Sequential / Pipeline
+- One coordinator delegates to specialists?
|  -> Supervisor / Worker (most common)
+- Peers swap control based on context?
|  -> Swarm / Handoff (OpenAI Swarm pattern)
+- Tree of sub-tasks?
|  -> Hierarchical (manager -> sub-managers -> workers)
+- Arbitrary directed graph with conditional edges?
|  -> Graph (LangGraph)
+- Independent agents across orgs?
   -> A2A protocol with shared task object

Topology Patterns

Supervisor / Worker (default)

A supervisor agent decomposes the task and routes each sub-task to a specialist worker. Worker results return to the supervisor, which decides the next step or finalizes.

  • Pros: simple, auditable, easy to add workers
  • Cons: supervisor is a bottleneck and a single point of prompt failure
  • Frameworks: LangGraph supervisor, AutoGen GroupChat (with manager), CrewAI hierarchical

Swarm / Handoff

Each agent decides when to hand control to a peer by emitting a handoff(target_agent, context) tool call. No central supervisor.

  • Pros: emergent routing, less prompt overhead per turn
  • Cons: harder to debug, risk of ping-pong loops
  • Frameworks: OpenAI Swarm / Agents SDK, Microsoft Agent Framework

Read the full file on GitHub · 161 lines

Files

What ships with it

1 file 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. 7d ago Changed · -1 lines b0ddbb20191f
  2. 1mo ago Changed 941396445450
  3. 1mo ago First seen · 162 lines · 83 tokens per session scan A 901148d54945

Subscribe to this mod's changes

multi-agent-orchestration is a skill published in the GitHub repository jnPiyush/AgentX (16 stars, last pushed 3d ago), licensed Apache-2.0. It adds 83 tokens to every session and 1,317 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.

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