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 adamwang99/NEUTRON-EVO-OS --skill orchestrationgit clone --depth 1 https://github.com/adamwang99/NEUTRON-EVO-OSWrote 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/adamwang99/neutron-evo-os/orchestration)<a href="https://agentmods.dev/skills/adamwang99/neutron-evo-os/orchestration"><img src="https://agentmods.dev/badge/skills/adamwang99/neutron-evo-os/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/adamwang99/neutron-evo-os/orchestration"><img src="https://agentmods.dev/badge/skills/adamwang99/neutron-evo-os/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 | $0.00003 | $0.02716 |
| Opus 5 | $0.00002 | $0.01358 |
| Sonnet 5 | $0.00001 | $0.00543 |
| Haiku 4.5 | $0.00000 | $0.00272 |
Grade A, and why
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 11d 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 — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestration Skill — Task Decomposition + Parallel Coordination
Purpose
This skill decomposes large tasks into independent units and coordinates parallel execution.
It is a PLANNING + COORDINATION tool — it does NOT spawn agents directly.
Agents are spawned via Claude Code's built-in /batch command (git worktree-based parallel).
This skill answers: "How do I split this work? Which agents do what? How do I make sure they don't conflict? How do I merge their outputs?"
How to Run
run_orchestration(task, {"phase": "analyze"}) → Decompose task, score parallelism
run_orchestration(task, {"phase": "plan"}) → Present plan (after analyze)
run_orchestration(task, {"phase": "execute"}) → Build unit configs → use /batch to spawn
run_orchestration(task, {"phase": "update", "unit_id": "...", "result": {...}}) → Record unit result
run_orchestration(task, {"phase": "merge"}) → Validate + merge results
run_orchestration(task, {"phase": "report"}) → Final summary
Execute Phase — neutron_spawn_agent
The execute phase returns unit configs. Call neutron_spawn_agent for each unit via MCP:
1. run_orchestration(..., phase="execute") → unit configs in output
2. For each unit: call neutron_spawn_agent(agent_id=unit_id, prompt=unit_prompt, ...)
→ agents run in parallel via claude-agent-sdk
3. Wait for results (or use background=True for non-blocking)
4. run_orchestration(..., phase="merge", results={...}) → validate + merge
Each spawned agent uses Claude Code's tools (Read/Edit/Bash/Glob/Grep) with its own context window.
Agents can run in the same project or different subdirectories (use cwd param to isolate).
# After run_orchestration(..., phase="execute"):
configs = result["agent_configs"] # list of agent configs
# Call Agent() for EACH config concurrently:
for cfg in configs:
Agent(
prompt=cfg["prompt"],
agent=cfg["agent"], # "Plan" | "Explore" | "general-purpose"
background=cfg["background"], # True = concurrent, False = blocking
maxTurns=cfg["max_turns"],
isolation=cfg["isolation"], # project root path → git worktree isolation
skills=cfg["skills"], # ["spec", "context"] preloaded
)
# When ALL agents finish, call:
run_orchestration(task, {"phase": "merge"})
What ships with it
2 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.
- 11d ago First seen · 307 lines · 3 tokens per session scan A ef72835ca0f2
orchestration is a skill published in the GitHub repository adamwang99/NEUTRON-EVO-OS (20 stars, last pushed 4mo ago), licensed MIT. It adds 3 tokens to every session and 2,716 once invoked, about $0.0000 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.
Other skills, from other repositories
metago-expert-agent-lifecycle-manager
An expert role that manages an AI agent through its full lifecycle, from defining its requirements and abilities to packaging, testing, deploying, upgrading, and retiring it. It also designs how multiple agents work together.
metago-security-audit
A security review skill based on the OWASP Top 10, a commonly used list of major web-application security risks. It checks code, dependencies, configuration, authentication, and data handling.
metago-delivery-gate
A delivery checklist for software work that links each requirement to code, tests, and proof that the result looks right. It includes screenshot comparison, where a new image is checked against a reference image.
metago-expert-dcv-economist
An expert role for measuring how people, teams, or projects contribute value using a six-part scoring model. It also evaluates credit, balance, and possible contribution risks from records such as work logs and output data.
metago-expert-industrial-interconnect-engineer
An industrial-connectivity engineering guide for linking factory equipment such as PLCs, CNC machines and sensors to software systems. It covers industrial communication protocols, digital twins, operational-technology security and compliance standards.
metago-expert-knowledge-crystal-curator
A Chinese-language system for managing short-term conversation notes, long-term stored knowledge, and reusable skills. It covers saving, finding, updating, and retiring this information over time.