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 yaleh/meta-cc --skill prompt-findgit clone --depth 1 https://github.com/yaleh/meta-ccWrote 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/yaleh/meta-cc/prompt-find)<a href="https://agentmods.dev/skills/yaleh/meta-cc/prompt-find"><img src="https://agentmods.dev/badge/skills/yaleh/meta-cc/prompt-find/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/yaleh/meta-cc/prompt-find"><img src="https://agentmods.dev/badge/skills/yaleh/meta-cc/prompt-find.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 | $0.00030 | $0.00328 |
| Opus 5 | $0.00015 | $0.00164 |
| Sonnet 5 | $0.00006 | $0.00066 |
| Haiku 4.5 | $0.00003 | $0.00033 |
Grade B, and why
prompt-find scanned grade B with 1 finding 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
Keep output concise. Do not print full prompt bodies in this skill; tell the What it actually says
Prompt Library Search
Search saved prompts under .meta-cc/prompts/library/ in the current project.
Input
Use the user's prompt as the keyword query. If the user explicitly provides
terms after $prompt-find, search those terms. If no keywords are provided,
show concise usage help.
Workflow
- Resolve the library path as
.meta-cc/prompts/library/from the current project root. - If the directory does not exist, say no prompt library exists and suggest saving prompts before searching.
- Read every
*.mdfile in the library. - Parse YAML frontmatter when present. Prefer these fields when available:
idtitlecategorykeywordsusage_countupdated
- Score matches using keyword overlap:
- frontmatter
keywordsandtitle: strongest signal - body sections such as original prompts or optimized prompt: medium signal
category: weak signal
- frontmatter
- Sort matches descending by score.
- Present a compact table with ID, category, score, title, and updated date when available.
- If no entries match, say no matching prompts were found and suggest trying broader keywords.
Output
Keep output concise. Do not print full prompt bodies in this skill; tell the
user to use $prompt-show <id> for details.
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 · 40 lines · 30 tokens per session scan B 66986125c611
prompt-find is a skill published in the GitHub repository yaleh/meta-cc (21 stars, last pushed 20d ago), licensed MIT. It adds 30 tokens to every session and 328 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
prompt-optimization
Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a…
enhance-prompt
Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.
prompt-engineer
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot…
seedance-vocab-en
This skill should be used when an English Seedance 2.0 prompt needs clearer production wording, less generic prose, or precise vocabulary for camera, lighting, motion, VFX, audio, and constraints. Route blocked prompts through seedance-filter for context and boundary review.
ideogram4
Prompting patterns for Ideogram 4 text-to-image — best-in-class in-image text rendering and exact color/layout control via structured JSON captions. Use when generating images that need legible on-image text (title cards, thumbnails, logos, signage, CTAs), precise brand colors, or controlled spatial layout. Triggers…