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 vignesh2027/AI-AGENT-SKILLS --skill agent-orchestrationgit clone --depth 1 https://github.com/vignesh2027/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/vignesh2027/ai-agent-skills/agent-orchestration)<a href="https://agentmods.dev/skills/vignesh2027/ai-agent-skills/agent-orchestration"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/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/vignesh2027/ai-agent-skills/agent-orchestration"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/agent-orchestration.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00020 | $0.00922 |
| Opus 5 | $0.00010 | $0.00461 |
| Sonnet 5 | $0.00004 | $0.00184 |
| Haiku 4.5 | $0.00002 | $0.00092 |
Grade A, and why
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 10d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Multi-agent systems fail loudly or silently. Loudly: an agent calls a tool that doesn't exist. Silently: an agent completes with a subtly wrong result and the orchestrator never notices. This skill designs agent systems that are auditable, recoverable, and deterministic about what succeeded and what failed.
When to Use
- Before designing any system with more than one AI agent
- When building tool interfaces for agents
- When debugging agent behavior that is unpredictable or hard to reproduce
- Before deploying an agent to handle user-facing tasks
Process
Step 1: Define the task boundary
Agents work best on well-scoped tasks with clear completion criteria. Avoid: "make the application better." Use: "fix all TypeScript type errors in src/components/." Ambiguous tasks produce ambiguous results.
Step 2: Design the tool interface first
Tools are the agent's API to the world. Each tool must have:
- A precise, unambiguous name
- A description that tells the agent WHEN to use it (not just what it does)
- Strongly-typed input schema (JSON Schema)
- Well-defined output schema
- Error behavior documented
Step 3: Implement tool observability
Every tool call must be logged: which tool, what inputs, what outputs, how long it took, did it succeed. This is non-negotiable — you cannot debug an agent you cannot observe.
Step 4: Design for idempotency
Tools that create or modify state must be idempotent where possible. If an agent retries a tool call (due to failure), the second call must not create duplicate state.
Step 5: Plan the agent loop
Define: what does the agent do on each step? How does it decide it's done? What is the maximum number of steps? (Always set a maximum — unbounded loops are production incidents.)
Step 6: Define the handoff protocol
If multiple agents coordinate: define exactly what one agent passes to the next. Use structured data, not natural language, for inter-agent communication. Natural language is lossy.
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.
- 10d ago First seen · 95 lines · 20 tokens per session scan A e016fafb9071
agent-orchestration is a skill published in the GitHub repository vignesh2027/AI-AGENT-SKILLS (2 stars, last pushed 12d ago), licensed MIT. It adds 20 tokens to every session and 922 once invoked, about $0.0001 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 2026-08-31.
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