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 DevelopersGlobal/ai-agent-skills --skill multi-agent-orchestrationgit clone --depth 1 https://github.com/DevelopersGlobal/ai-agent-skillsWrote 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/developersglobal/ai-agent-skills/multi-agent-orchestration)<a href="https://agentmods.dev/skills/developersglobal/ai-agent-skills/multi-agent-orchestration"><img src="https://agentmods.dev/badge/skills/developersglobal/ai-agent-skills/multi-agent-orchestration.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00033 | $0.01210 |
| Opus 5 | $0.00016 | $0.00605 |
| Sonnet 5 | $0.00007 | $0.00242 |
| Haiku 4.5 | $0.00003 | $0.00121 |
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 8d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Single agents are limited by context window, specialization depth, and parallelism. Multi-agent systems overcome these limits by routing subtasks to specialized agents. But multi-agent systems introduce new failure modes: lost context, conflicting decisions, infinite loops, and cascading failures.
This skill provides the architecture and coordination patterns to build multi-agent systems that are reliable, observable, and maintainable.
When to Use
- The task requires more context than a single agent can handle
- Different subtasks require different specializations (research, coding, review, security)
- Subtasks can be parallelized for speed
- The workflow is long-running and requires checkpointing
- Different tasks require different levels of human oversight
Process
Step 1: Design the Agent Network
- Define agent responsibilities: Each agent should have a single, well-defined job. Name them by role:
researcher,coder,reviewer,security-auditor,tester. - Define communication topology: Who can talk to whom?
- Pipeline: Agent A → Agent B → Agent C (sequential)
- Supervisor: Orchestrator dispatches to specialists (hub-and-spoke)
- Peer: Agents collaborate as equals (mesh)
- Define data contracts: What does each agent receive? What does it output? Use structured formats (JSON schemas) for inter-agent communication.
- Define the orchestration logic: Who decides which agent acts next?
Verify: You can draw the agent network on a whiteboard with clear roles and data flow.
Step 2: Implement Context Management
- Each agent should receive only the context it needs — not the full conversation history.
- Use a shared state store (database, key-value store) for information that multiple agents need.
- Pass summaries, not full transcripts, when context must traverse agent boundaries.
- Include a task ID in every message for tracing.
Verify: No agent receives more context than it requires for its specific task.
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.
- 8d ago First seen · 111 lines · 33 tokens per session scan A 27db8549fabc
multi-agent-orchestration is a skill published in the GitHub repository DevelopersGlobal/ai-agent-skills (65 stars, last pushed 4mo ago), licensed MIT. It adds 33 tokens to every session and 1,210 once invoked, about $0.0002 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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