Borrowing it
Nothing to install: this file belongs to pskoett/measuring-ai-proficiency. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/pskoett/measuring-ai-proficiency/main/.claude/skills/use-agent-factory/SKILL.mdgit clone --depth 1 https://github.com/pskoett/measuring-ai-proficiencyWrote 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/pskoett/measuring-ai-proficiency/use-agent-factory)<a href="https://agentmods.dev/skills/pskoett/measuring-ai-proficiency/use-agent-factory"><img src="https://agentmods.dev/badge/skills/pskoett/measuring-ai-proficiency/use-agent-factory/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/pskoett/measuring-ai-proficiency/use-agent-factory"><img src="https://agentmods.dev/badge/skills/pskoett/measuring-ai-proficiency/use-agent-factory.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 | $0.00170 | $0.03153 |
| Opus 5 | $0.00085 | $0.01577 |
| Sonnet 5 | $0.00034 | $0.00631 |
| Haiku 4.5 | $0.00017 | $0.00315 |
Grade A, and why
use-agent-factory 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use the Agent Factory
Purpose
This repo runs a gh-aw agent factory plus one plain Actions workflow. Every non-trivial change should flow through it end to end: issue > spec > plan PR merged > source issue activated > implementer > review > fix > merge > learn. The source issue is the unit of work; there is no sub-issue layer. This skill tells you how to drive that flow correctly, where to stop and wait for a human, and what to do when something stalls.
When to invoke
Activate this skill when any of these hold:
- User asks you to add, fix, refactor, or ship something that will end up on
main - User references an issue number, PR number, or plan file that belongs to the factory chain
- User asks about factory status, stuck PRs, or the
needs-spec/needs-changes/fast-tracklabels - A PR in this repo has been labeled by the factory and you need to decide the next action
- User asks to file a new
needs-specissue or to spec-refine an existing issue
Skip this skill for: local scratch work that never reaches main, questions about the codebase that don't require any edits, read-only audits, or small doc tweaks the user explicitly says should bypass the factory.
Prerequisites the user must have already done
Before the factory works end to end, these one-time setup items must be in place. If any PR stalls with symptoms like "waiting for approval" or "labels don't stick", suspect these first. Full table in docs/AGENT_FACTORY.md#prerequisites.
| Category | What must be true |
|---|---|
| Repo settings | Settings > Actions > General: Read and write permissions, Allow GitHub Actions to create and approve pull requests checked, Approval for outside collaborators = Require approval for first-time contributors who are new to GitHub |
| Copilot | Settings > Copilot > Coding agent enabled; Settings > Copilot > Code review enabled |
| Secrets | COPILOT_GITHUB_TOKEN, GH_AW_GITHUB_TOKEN, GH_AW_GITHUB_MCP_SERVER_TOKEN, GH_AW_AGENT_TOKEN, GH_AW_CI_TRIGGER_TOKEN all present in Actions secrets |
| Labels | All factory labels from the Label Reference table must exist |
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 · 215 lines · 170 tokens per session scan A 01b2d105c77c
use-agent-factory is a skill published in the GitHub repository pskoett/measuring-ai-proficiency (11 stars, last pushed 1mo ago), licensed MIT. It adds 170 tokens to every session and 3,153 once invoked, about $0.0009 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 2026-08-31.
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