Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/lisihao/Solarnpx agentmods add skills/lisihao/solar/agent-orchestratorWrote 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/lisihao/solar/agent-orchestrator)<a href="https://agentmods.dev/skills/lisihao/solar/agent-orchestrator"><img src="https://agentmods.dev/badge/skills/lisihao/solar/agent-orchestrator.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 | $0.00100 | $0.01402 |
| Opus 5 | $0.00050 | $0.00701 |
| Sonnet 5 | $0.00020 | $0.00280 |
| Haiku 4.5 | $0.00010 | $0.00140 |
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 4d 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.
This is a copy
100% identical to agent-orchestrator — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Orchestrator
Orchestrate complex tasks by decomposing them into subtasks, spawning autonomous sub-agents, and consolidating their work.
Core Workflow
Phase 1: Task Decomposition
Analyze the macro task and break it into independent, parallelizable subtasks:
1. Identify the end goal and success criteria
2. List all major components/deliverables required
3. Determine dependencies between components
4. Group independent work into parallel subtasks
5. Create a dependency graph for sequential work
Decomposition Principles:
- Each subtask should be completable in isolation
- Minimize inter-agent dependencies
- Prefer broader, autonomous tasks over narrow, interdependent ones
- Include clear success criteria for each subtask
Phase 2: Agent Generation
For each subtask, create a sub-agent workspace:
python3 scripts/create_agent.py <agent-name> --workspace <path>
This creates:
<workspace>/<agent-name>/
âââ SKILL.md # Generated skill file for the agent
âââ inbox/ # Receives input files and instructions
âââ outbox/ # Delivers completed work
âââ workspace/ # Agent's working area
âââ status.json # Agent state tracking
Generate SKILL.md dynamically with:
- Agent's specific role and objective
- Tools and capabilities needed
- Input/output specifications
- Success criteria
- Communication protocol
See references/sub-agent-templates.md for pre-built templates.
Phase 3: Agent Dispatch
Initialize each agent by:
- Writing task instructions to
inbox/instructions.md - Copying required input files to
inbox/ - Setting
status.jsonto{"state": "pending", "started": null} - Spawning the agent using the Task tool:
# Spawn agent with its generated skill
Task(
description=f"{agent_name}: {brief_description}",
prompt=f"""
Read the skill at {agent_path}/SKILL.md and follow its instructions.
Your workspace is {agent_path}/workspace/
Read your task from {agent_path}/inbox/instructions.md
Write all outputs to {agent_path}/outbox/
Update {agent_path}/status.json when complete.
""",
subagent_type="general-purpose"
)
What ships with it
4 files 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.
- 4d ago First seen · 176 lines · 100 tokens per session scan A 19cef3165169
agent-orchestrator is a skill published in the GitHub repository lisihao/Solar (2 stars, last pushed 24d ago), licensed MIT. It adds 100 tokens to every session and 1,402 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to agent-orchestrator, differing in 0 lines, and is treated as a copy.
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