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 CyannSHI/ai-interview-kit --skill generate-promptgit clone --depth 1 https://github.com/CyannSHI/ai-interview-kitWrote 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/cyannshi/ai-interview-kit/generate-prompt)<a href="https://agentmods.dev/skills/cyannshi/ai-interview-kit/generate-prompt"><img src="https://agentmods.dev/badge/skills/cyannshi/ai-interview-kit/generate-prompt/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/cyannshi/ai-interview-kit/generate-prompt"><img src="https://agentmods.dev/badge/skills/cyannshi/ai-interview-kit/generate-prompt.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 | $0.00081 | $0.00136 |
| Opus 5 | $0.00041 | $0.00068 |
| Sonnet 5 | $0.00016 | $0.00027 |
| Haiku 4.5 | $0.00008 | $0.00014 |
Grade A, and why
generate-prompt 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 9d 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.
What it actually says
请读取并完整遵循 skills/generate-prompt.md 中的所有指令。该文件包含本技能的完整工作流程。
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.
- 9d ago First seen · 10 lines · 81 tokens per session scan A c92ba0689fe0
generate-prompt is a skill published in the GitHub repository CyannSHI/ai-interview-kit (11 stars, last pushed 2mo ago), licensed MIT. It adds 81 tokens to every session and 136 once invoked, about $0.0004 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
science-vibecoding
Structured AI-assisted scientific code generation. 6 safety guards, 8 principles, 11 prompt templates. Grounded in Nature (2026).
ai-native-context-engineering
A workflow for designing how an AI system receives and manages information for a task. It covers choosing context, controlling the amount of information, and checking the result.
role-AI工程师
An AI engineering role for designing AI calls, packaging agent components, building workflows where multiple agents work together, and checking output quality.
state-prompt
Rewrite a generic product-design prompt into a paste-ready prompt for one specific Stateful condition or one explicit high-risk composition of lifecycle state, interruption context, surface, and changed-data or dependency condition. Use when generating a screen, component, email, notification, or prototype from an…
prompt-engineering
Prompt engineering techniques and patterns. Use when writing agent commands, hooks, skills, subagent prompts, or any LLM interaction: optimizing prompts, improving output reliability, and designing production-grade prompt templates. Trigger words: prompt engineering, prompt, prompt optimization, LLM interaction.
seedance-antislop
Detect and remove hollow AI filler language, empty superlatives, and vague boosters that degrade Seedance 2.0 prompt quality. Use when a prompt feels generic, over-written, or 'AI-sounding', or when generation output looks bland and needs a quality pass.