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 automagik-dev/genie --skill refinegit clone --depth 1 https://github.com/automagik-dev/genieWrote 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/automagik-dev/genie/refine)<a href="https://agentmods.dev/skills/automagik-dev/genie/refine"><img src="https://agentmods.dev/badge/skills/automagik-dev/genie/refine/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/automagik-dev/genie/refine"><img src="https://agentmods.dev/badge/skills/automagik-dev/genie/refine.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 | $0.00037 | $0.01271 |
| Opus 5.5 | $0.00015 | $0.00508 |
| Sonnet 5.5 | $0.00007 | $0.00254 |
| Haiku 4.5 | $0.00004 | $0.00127 |
Grade A, and why
refine 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 18d 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Refine
Preserve the prompt’s intent, language, scope, audience, permissions, and explicit output contract. Rewrite the prompt; never execute it.
Providers and sources
Exactly two provider switches, plus one Claude-only target modifier, with model names used only as documentation baselines:
| Switch | Bundled guidance | Baseline and official documentation |
|---|---|---|
--for openai |
prompts/openai.md |
GPT-6 Astra: prompting best practices, prompt engineering |
--for claude |
prompts/claude.md |
Claude Opus 5.5 (default baseline): Opus 5.5 guide, Claude best practices |
--for claude --target sonnet |
prompts/claude.md + prompts/claude-sonnet-5-5.md |
Claude Sonnet 5.5: Sonnet 5.5 guide |
--for claude --target opus |
prompts/claude.md + prompts/claude-opus-5-5.md |
Claude Opus 5.5: Opus 5.5 guide |
--for claude --target fable |
prompts/claude.md + prompts/claude-fable-5-1.md |
Claude Fable 5.1: Fable 5.1 guide |
This table is drift-prone: documentation URLs move, model baselines are superseded, and the bundled guides go stale without announcing it. The freshness claim is therefore one line, on its own, in exactly this shape, and it covers every URL, every baseline, and every bundled guide:
Checked: 2026-10-01
Compare that date before promising the guidance is current. If it predates the release these skills shipped with, or either URL no longer resolves, the guidance is unverified: re-read the official pages, refresh the bundled guide deliberately, and update the line in the same edit. Keep the provider switches stable either way — a stale date narrows what may be claimed, never the switch surface.
What ships with it
6 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.
- 18d ago Changed · +1 lines 0fc23e95ddd2
- 21d ago Changed · +4 lines 0e402cda6793
- 1mo ago Changed · -4 lines · +12 tokens per session 1f42365d55bd
- 1mo ago First seen · 51 lines · 25 tokens per session scan A fc3b04c4f0ff
refine is a skill published in the GitHub repository automagik-dev/genie (345 stars, last pushed 2d ago), licensed MIT. It adds 37 tokens to every session and 1,271 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
v4-best-practices
Use when working with deepseek-v4-pro or deepseek-v4-flash in thinking mode on multi-step or plan-driven tasks. Provides rules to prevent stale references, unverified plan assumptions, and vague plan output.
agent-evolution
A guide to making an AI agent improve from its past runs. It covers how to judge results, extract reusable lessons, and decide whether to store them as knowledge, instructions, code, or model training.
post-training-strategy
A guide to choosing and planning ways to improve an AI model after its initial training. It explains when to add knowledge, teach a response format, use trial-and-error training, or use a smaller model to learn from a larger one.
best-practices
Transforms vague prompts into optimized Claude Code prompts. Adds verification, specific context, constraints, and proper phasing. Invoke with /best-practices.
llm-application-dev
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
prompt-engineering
Use when one prompt must give the same right answer across reruns, models, and pasted-in hostile input: forcing a fixed schema, picking the few-shot set, ordering the prompt blocks, or the inline cases you run while tuning. NOT the agent loop, tools, or retrieval (that is building-agents), NOT a standing CI eval…