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.
npx skills add jnPiyush/AgentX --skill multi-agent-orchestrationgit clone --depth 1 https://github.com/jnPiyush/AgentXWrote 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.
[](https://agentmods.dev/skills/jnpiyush/agentx/multi-agent-orchestration)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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
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.
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.
- 7d ago Changed · -1 lines b0ddbb20191f
- 1mo ago Changed 941396445450
- 1mo ago First seen · 162 lines · 83 tokens per session scan A 901148d54945
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.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…