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 morganmuli/metaskill --skill skillgit clone --depth 1 https://github.com/morganmuli/metaskillWrote 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/morganmuli/metaskill/skill)<a href="https://agentmods.dev/skills/morganmuli/metaskill/skill"><img src="https://agentmods.dev/badge/skills/morganmuli/metaskill/skill/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/morganmuli/metaskill/skill"><img src="https://agentmods.dev/badge/skills/morganmuli/metaskill/skill.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 | $0.00051 | $0.00623 |
| Opus 5.5 | $0.00020 | $0.00249 |
| Sonnet 5.5 | $0.00010 | $0.00125 |
| Haiku 4.5 | $0.00005 | $0.00062 |
Grade A, and why
metaskill 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.
This is a copy
91% identical to metaskill — 4 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.
What it actually says
You are an elite AI agent architect. You can create complete agent teams, individual agents, or custom skills — all through a single command.
User request: $ARGUMENTS
Auto-Detected Context
Working directory: !pwd
Existing subdirectories: !ls -d */ 2>/dev/null | head -20 || echo "empty directory"
Skill base: !for d in "$HOME/.claude/skills/metaskill" ".claude/skills/metaskill"; do [ -d "$d/flows" ] && echo "$d" && break; done 2>/dev/null || echo "$HOME/.claude/skills/metaskill"
Step 1: Detect Intent
Analyze $ARGUMENTS to determine the mode:
-
Team mode (default): The user wants a complete agent team for a project type. Trigger words: "app", "project", "team", "fullstack", "pipeline", "game dev", or any domain/technology without explicit "agent" or "skill" keywords. Examples: "ios app", "fullstack web", "data science pipeline", "game dev with Unity"
-
Agent mode: The user wants to create a single agent. Trigger words: "agent", "reviewer", "engineer" (as a role), or phrases like "create an agent that..." Examples: "a security reviewer agent", "code reviewer for Go", "create an agent that handles deployments"
-
Skill mode: The user wants to create a single skill (slash command). Trigger words: "skill", "command", "slash command", or phrases like "create a skill that..." Examples: "a deploy skill", "slash command to run tests", "create a skill for linting"
If the intent is ambiguous, use AskUserQuestion to ask the user which mode they want.
Step 2: Load and Execute Flow
Based on the detected mode, read the corresponding flow file from the Skill base path detected above:
- Team mode → Read
<skill-base>/flows/team.md - Agent mode → Read
<skill-base>/flows/agent.md - Skill mode → Read
<skill-base>/flows/skill.md
Then follow the instructions in that flow file exactly, using the user's request as context.
What ships with it
3 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.
- 1mo ago First seen · 46 lines · 51 tokens per session scan A 04f86455d9f1
metaskill is a skill published in the GitHub repository morganmuli/metaskill (1 stars, last pushed 5d ago), licensed MIT. It adds 51 tokens to every session and 623 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to metaskill, differing in 4 lines, and is treated as a copy.
Other skills, from other repositories
generate-report
Generate a comprehensive summary report of the latest experiment including metrics, plots, and comparison with baseline. Use this after training and evaluation to create a shareable experiment summary.
evaluate-model
Load the latest model checkpoint, run evaluation on the test set, and generate a metrics report with confusion matrix. Use this after training to assess model performance or to re-evaluate a specific checkpoint.
run-pipeline
Run the full data science pipeline: validate raw data, preprocess, engineer features, train model, and evaluate. Use this when you want to execute the end-to-end ML pipeline or re-run it after data or code changes.
api-test
Run API integration tests against the running backend, verify endpoints return expected responses and status codes. Use after deploying a preview or starting the dev server.
run-simulator
Build and launch the app in the iOS Simulator. Automatically selects an appropriate simulator device, boots it if needed, and installs and launches the app.
build-and-test
Install dependencies, run type checking, lint, tests, and build the project. Use after making code changes to verify nothing is broken.