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 eddiebelaval/squire --skill agent-orchestratorgit clone --depth 1 https://github.com/eddiebelaval/squireWrote 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/eddiebelaval/squire/agent-orchestrator)<a href="https://agentmods.dev/skills/eddiebelaval/squire/agent-orchestrator"><img src="https://agentmods.dev/badge/skills/eddiebelaval/squire/agent-orchestrator/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/eddiebelaval/squire/agent-orchestrator"><img src="https://agentmods.dev/badge/skills/eddiebelaval/squire/agent-orchestrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 166 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00014 | $0.03506 |
| Opus 5.5 | $0.00006 | $0.01402 |
| Sonnet 5.5 | $0.00003 | $0.00701 |
| Haiku 4.5 | $0.00001 | $0.00351 |
Grade A, and why
Agent Orchestrator 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 1mo 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 — 476 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Orchestrator
The Agent Orchestrator skill coordinates multiple specialized AI agents, skills, and tools to accomplish complex tasks that benefit from distributed expertise. It acts as a conductor, delegating subtasks to appropriate agents, managing dependencies, integrating results, and ensuring coherent final outputs.
This skill understands the capabilities of available agents (general-purpose, operations-manager, specialized skills), determines optimal task decomposition, manages inter-agent communication, handles failures, and synthesizes diverse outputs into unified results. It's the meta-layer that makes multi-agent collaboration effective.
Use this skill for complex projects requiring diverse expertise, tasks that benefit from parallel execution, or workflows where specialized agents outperform general-purpose approaches.
Core Workflows
Workflow 1: Decompose Task & Delegate
- Analyze the complex task:
- What's the end goal?
- What are the components?
- What expertise is needed?
- Map to available agents/skills:
- Which agents have relevant capabilities?
- What's each agent's specialty?
- What tools/MCPs do they access?
- Decompose into subtasks:
- Break along expertise boundaries
- Identify dependencies
- Determine execution order
- Delegate to appropriate agents:
- Assign subtasks with clear instructions
- Provide necessary context
- Set success criteria
- Specify output format
- Monitor execution:
- Track progress
- Identify blockers
- Handle failures
- Integrate results:
- Collect agent outputs
- Resolve conflicts
- Synthesize into coherent whole
- Validate final result
Workflow 2: Parallel Agent Execution
- Identify parallelizable subtasks:
- Which tasks are independent?
- Which share no dependencies?
- Which can run concurrently?
- Prepare parallel execution:
- Assign subtasks to agents
- Provide isolated contexts
- Set timeout limits
- Launch agents in parallel:
- Initiate all at once
- Maintain separate contexts
- Monitor all executions
- Coordinate completion:
- Wait for all to finish
- Handle stragglers
- Manage timeout failures
- Aggregate results:
- Collect all outputs
- Merge related findings
- Resolve inconsistencies
- Synthesize final output
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.
- 1mo ago First seen · 476 lines · 14 tokens per session scan A dc69c196c8ab
Agent Orchestrator is a skill published in the GitHub repository eddiebelaval/squire (21 stars, last pushed 2mo ago), licensed MIT. It adds 14 tokens to every session and 3,506 once invoked, about $0.0001 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
Agent Orchestrator
Coordinate multiple AI agents and skills for complex workflows.
Agent Orchestrator
Coordinate multiple AI agents and skills for complex workflows.
orchestrate
This skill should be used when the user asks to 'orchestrate a task', 'break down work into parallel agents', 'coordinate subtasks', 'run agents in parallel', or mentions 'multi-agent'. Decomposes complex tasks into tracked subtasks, dispatches parallel subagents, and coordinates until completion.
coordination-bus
Use when two or more agents share work, when a task spans sessions, or when claiming, heartbeating, completing, or blocking a coordination bus task. The bus is the single source of truth for who owns what — never coordinate through private agreement.
swarmclaw
AI agent runtime and multi-agent orchestration platform. Teaches agents how to use SwarmClaw's 6 primitive tools, persistent memory, dreaming, delegation, connectors, credentials, and the skill system. Use when an agent is running on SwarmClaw and needs to understand the platform's capabilities.
Agentic Orchestration for Oracle
Multi-agent coordination patterns for enterprise AI systems on Oracle Cloud Infrastructure.