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 theagentledger/agent-skills --skill prompt-librarygit clone --depth 1 https://github.com/theagentledger/agent-skillsWrote 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/theagentledger/agent-skills/prompt-library)<a href="https://agentmods.dev/skills/theagentledger/agent-skills/prompt-library"><img src="https://agentmods.dev/badge/skills/theagentledger/agent-skills/prompt-library/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/theagentledger/agent-skills/prompt-library"><img src="https://agentmods.dev/badge/skills/theagentledger/agent-skills/prompt-library.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 | $0.00119 | $0.02983 |
| Opus 5.5 | $0.00048 | $0.01193 |
| Sonnet 5.5 | $0.00024 | $0.00597 |
| Haiku 4.5 | $0.00012 | $0.00298 |
Grade A, and why
prompt-library 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 1mo 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
What ships with it
1 file 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.
- 1mo ago First seen · 387 lines · 119 tokens per session scan A c3af62acb7d0
prompt-library is a skill published in the GitHub repository theagentledger/agent-skills (6 stars, last pushed 7mo ago), with no licence file. It adds 119 tokens to every session and 2,983 once invoked, about $0.0005 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 2026-08-31.
Other skills, from other repositories
ai-wrapper-product
Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc. ) into focused tools people will pay for. Not just "ChatGPT but different" - products that solve specific problems with AI.
ai-product
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.
skill-ai-orchestration
Use for server-side AI orchestration in SaaS products, including OpenAI, Gemini, Claude, ElevenLabs, streaming, transcription, structured extraction, prompt contracts, token budgets, model routing, queues, retries, observability, consent, validation, and safe API key handling.
hyper-research
Send one research question to as many independent LLMs as possible at once, each in its strongest reasoning mode with proof of that mode, then synthesize. Floor of 3 models, quorum of 4; late answers are collected later. Use for high-stakes questions where one model's view is not enough. Triggers: /hyper, hyper…
x9-agent-instructions
A guide for writing or reviewing instructions given to another coding agent. It treats the prompt or instruction file as a working document with clear goals, constraints, and handoff details.
prompt-regression-gate
Gate prompt, model, tool-schema, or packer changes with paired runs on a frozen eval set, slice-level regression checks, relative baselines, and explicit go/no-go thresholds. Use before merging prompt PRs or swapping models.