Agent Skills Platform turns documented work and supporting evidence into tested, installable skills for coding agents. Teams use it to review, publish, maintain, and safely reuse agent workflows, with the catalogue entries serving as skills and plugins built for that ecosystem.
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 FrancyJGLisboa/agent-skill-creator --skill agent-skill-creatorgit clone --depth 1 https://github.com/FrancyJGLisboa/agent-skill-creatorWrote 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/francyjglisboa/agent-skill-creator/agent-skill-creator)<a href="https://agentmods.dev/skills/francyjglisboa/agent-skill-creator/agent-skill-creator"><img src="https://agentmods.dev/badge/skills/francyjglisboa/agent-skill-creator/agent-skill-creator/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/francyjglisboa/agent-skill-creator/agent-skill-creator"><img src="https://agentmods.dev/badge/skills/francyjglisboa/agent-skill-creator/agent-skill-creator.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 | $0.00151 | $0.10027 |
| Opus 5 | $0.00076 | $0.05013 |
| Sonnet 5 | $0.00030 | $0.02005 |
| Haiku 4.5 | $0.00015 | $0.01003 |
Grade A, and why
agent-skill-creator 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 6d 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 — 651 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/agent-skill-creator — Turn Existing Work Into a Reusable Skill
The user provides whatever already represents their work — a description, document, link, script, screenshot, transcript, or partial example. Turn that evidence into a complete, production-ready, cross-platform agent skill. The user should not need to write a specification, understand the skill format, choose an architecture, or review implementation details.
Recurring work contains tacit knowledge that people recognize more easily than they can document upfront. Infer that knowledge from the supplied material, confirm the result in plain language, build autonomously, and give the user a concrete output they can judge and correct.
The User Journey
Use this guided-light path by default. Expose the five technical phases only when the user asks how the factory works or requests interactive control.
- Understand — read the evidence and summarize the question, trigger, supported decision, required evidence, and measurable success condition alongside the workflow, input, and output. Ask for one confirmation or correction.
- Build — create the skill autonomously. Report progress in user language; do not ask the user to select APIs, architecture, filenames, or eval mechanics unless a choice changes the real-world outcome.
- Check — run validation, pipeline, security, and eval gates. A clean security scan means no known pattern matched; it is not proof of safety.
- Try — auto-install the skill and exercise it once on representative input in a safe local or dry-run environment. Show the output and ask whether it matches the user's work.
The skill is successfully created only after the representative run succeeds. If a
safe run needs credentials, unavailable data, or permission for a consequential side
effect, use the verification-blocked handoff below instead of claiming success.
At creation start, run python3 scripts/success_ledger.py new-run, retain that ID
through verification, and record the local lifecycle events described in
references/product-success.md. Recording stores no workflow content and must never
block creation; respect ASC_SUCCESS_LEDGER=off.
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.
- 6d ago Changed 77e64dd3d717
- 9d ago First seen · 651 lines · 151 tokens per session scan A 932d1802bec2
agent-skill-creator is a skill published in the GitHub repository FrancyJGLisboa/agent-skill-creator (2,378 stars, last pushed 5d ago), licensed MIT. It adds 151 tokens to every session and 10,027 once invoked, about $0.0008 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.
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