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 openpi-dev/openpi --skill subagentsgit clone --depth 1 https://github.com/openpi-dev/openpiWrote 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/openpi-dev/openpi/subagents)<a href="https://agentmods.dev/skills/openpi-dev/openpi/subagents"><img src="https://agentmods.dev/badge/skills/openpi-dev/openpi/subagents/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/openpi-dev/openpi/subagents"><img src="https://agentmods.dev/badge/skills/openpi-dev/openpi/subagents.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00029 | $0.00648 |
| Opus 5.5 | $0.00012 | $0.00259 |
| Sonnet 5.5 | $0.00006 | $0.00130 |
| Haiku 4.5 | $0.00003 | $0.00065 |
Grade A, and why
subagents 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 — 23 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Subagents
The tool definitions are canonical for parameters, limits, model syntax, isolation, and lifecycle commands. This skill governs delegation decisions only.
- Delegate substantial independent work, not a lookup or edit the parent can do directly.
- Give the child a standalone prompt with paths, constraints, relevant context, and the expected report; it cannot see the parent conversation or ask the user.
- Inherit the parent model by default. When choosing the child's reasoning effort, honor an explicit user requirement first; otherwise use the selected role's relative guidance and the task's difficulty, choosing from levels supported by the resolved child model.
- Prefer a matching agent type when one exists; built-ins inherit active parent tools, while an explicit custom tool list is enforced as a narrowing restriction. Model precedence is explicit spawn override, selected type-file model, configured built-in role model, then parent model. Reasoning precedence is explicit spawn override, selected type default, then parent effort. Types live in
~/.pi/agent/agents/*.mdand, for trusted projects,.pi/agents/*.md; see Agent types. - Isolate concurrent writers in worktrees according to the
subagent_spawnschema so they cannot overwrite one checkout or git index. While Plan Mode is active, use only read-only exploration types (or no type); worktree isolation and types narrowed by Plan Mode are rejected. - After spawning, continue useful parent work. In an interactive session, if none remains, tell the user the child is still running and end the turn; automatic result delivery will re-invoke the parent when it settles. Do not block merely because the next step depends on the result or because there is nothing else to do. Use
subagent_waitonly when the user explicitly asks to keep the current response open for the result, or when non-interactive automation must return it in the same invocation. - Use optional
output_schemawhen downstream work needs a machine-validated result rather than prose. The child then receives one terminatingstructured_outputtool, and the run fails if it finishes without submitting a matching value. Keep schemas small and task-specific; the validated JSON is delivered to the parent and preserved in a private content-addressed artifact. Omit the option for ordinary text reports.
What ships with it
1 file 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.
- 1mo ago Changed 45e64ba65851
- 1mo ago Changed · +1 lines 4960ac5bfaef
- 1mo ago First seen · 22 lines · 29 tokens per session scan A 19a54f9fa27c
subagents is a skill published in the GitHub repository openpi-dev/openpi (196 stars, last pushed 4d ago), licensed MIT. It adds 29 tokens to every session and 648 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 2026-08-30.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…