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 saffron-health/libretto --skill generate-specgit clone --depth 1 https://github.com/saffron-health/librettoWrote 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/saffron-health/libretto/generate-spec)<a href="https://agentmods.dev/skills/saffron-health/libretto/generate-spec"><img src="https://agentmods.dev/badge/skills/saffron-health/libretto/generate-spec/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/saffron-health/libretto/generate-spec"><img src="https://agentmods.dev/badge/skills/saffron-health/libretto/generate-spec.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 | $0.00030 | $0.01945 |
| Opus 5 | $0.00015 | $0.00972 |
| Sonnet 5 | $0.00006 | $0.00389 |
| Haiku 4.5 | $0.00003 | $0.00194 |
Grade A, and why
generate-spec 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 10d 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create a spec sheet for the given feature/fix request in specs/ directory.
Ultrathink. Follow the following steps:
Understand existing code
Use code search sub-agents and grep as much as possible to deeply understand all of the relevant code. Be smart about your code search: start with where you think it might be, and if that inspires different places to read, follow up with sub-agents to do so. Each sub-agent should give you back information, and potentially other files to read or searches that might be relevant.
Understand external documentation/libraries
If external libraries are involved, always look up and research their relevant documentation as well. Tend to adhere strictly to the examples and best practices provided by the external libraries.
Ask critical guiding questions
After completing the research steps above, pause and ask the user any critical guiding questions before writing the spec. The feature/fix request will not always be completely defined. There may be logical errors, ambiguous requirements, or important clarifications required. Examples:
- "To store this data, we could either add a new table or extend the existing X table. The new table keeps concerns separate but adds a join; extending X is simpler but couples the concepts. Which do you prefer?"
- "There are two ways to surface this to the user: a modal dialog or an inline panel. The modal is more disruptive but harder to miss; the inline panel is less intrusive but easier to overlook. Which feels right?"
- "We need to sync this state. We could poll on an interval or use a WebSocket. Polling is simpler to implement but adds latency; WebSocket is real-time but more complex. Which trade-off do you want?"
Present the options you see, explain the trade-offs briefly, and let the user decide. If the feature request is fully defined and the path forward is obvious, skip the questions and write the spec directly. Practice good judgement.
Establish goals and non-goals
After research and any clarifying questions, establish explicit goals and non-goals for the spec. These come directly from the user. If the user did not provide them in the initial prompt, suggest a set of goals and non-goals and ask for confirmation before proceeding.
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.
- 10d ago First seen · 168 lines · 30 tokens per session scan A ea4748eede85
generate-spec is a skill published in the GitHub repository saffron-health/libretto (889 stars, last pushed 19d ago), licensed MIT. It adds 30 tokens to every session and 1,945 once invoked, about $0.0002 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
robotgo-flow
Use when building YAML-driven Windows RPA workflows in Go — step-by-step desktop automation with image template matching, interactive recording mode, hotkey triggers. RobotGo-Flow: YAML-based Windows RPA framework built on RobotGo.
fetch-weather-report
Fetch the current weather forecast for a specified location and return a structured summary.
qirabot
Drive any GUI with AI vision on raw screenshots — no DOM, no CSS/XPath selectors — via the Qirabot Python SDK or the qirabot CLI. Hand it a whole goal to complete autonomously, or make single natural-language actions: click, type, extract, and verify on web browsers, Android, iOS, desktop apps, and games. Use this…
robotgo
Use when automating desktop GUI operations in Go — mouse/keyboard control, screen capture, image recognition via OpenCV, global hotkeys via gohook. RobotGo: cross-platform Go desktop automation (RPA) library for macOS, Windows, and Linux.
wx4-skill
A WeChat automation tool. WeChat is a messaging platform used for one-to-one and group chats, file sharing, and community management.
qirabot
Drive any GUI with AI vision on raw screenshots — no DOM, no CSS/XPath selectors — via the Qirabot Python SDK or the qirabot CLI. Hand it a whole goal to complete autonomously, or make single natural-language actions: click, type, extract, and verify on web browsers, Android, iOS, desktop apps, and games. Use this…