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 agentmods add skills/mhrsdev/ai-agent-skills-library/edit-articlenpx skills add mhrsdev/AI-Agent-Skills-Library --skill edit-articlegit clone --depth 1 https://github.com/mhrsdev/AI-Agent-Skills-LibraryWrote 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/mhrsdev/ai-agent-skills-library/edit-article)<a href="https://agentmods.dev/skills/mhrsdev/ai-agent-skills-library/edit-article"><img src="https://agentmods.dev/badge/skills/mhrsdev/ai-agent-skills-library/edit-article.svg" alt="Measured on agentmods" 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 | $0.00034 | $0.00149 |
| Opus 5 | $0.00017 | $0.00075 |
| Sonnet 5 | $0.00007 | $0.00030 |
| Haiku 4.5 | $0.00003 | $0.00015 |
Grade A, and why
edit-article 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 6d 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.
This is a copy
91% identical to edit-article — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
- First, divide the article into sections based on its headings. Think about the main points you want to make during those sections.
Consider that information is a directed acyclic graph, and that pieces of information can depend on other pieces of information. Make sure that the order of the sections and their contents respects these dependencies.
Confirm the sections with the user.
- For each section:
2a. Rewrite the section to improve clarity, coherence, and flow. Use maximum 240 characters per paragraph.
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.
- 6d ago First seen · 16 lines · 34 tokens per session scan A e10fba546f45
edit-article is a skill published in the GitHub repository mhrsdev/AI-Agent-Skills-Library (6 stars, last pushed 2mo ago), licensed MIT. It adds 34 tokens to every session and 149 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to edit-article, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
agent-orchestrator
Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.
android-ui-journey-testing
XML-specified Android UI journey testing, interactive step execution, assertion verification, and JSON outcome reporting.
agent-self-scheduling
Schedule AI agent runs with cron, loops, or external clocks while avoiding unsafe tight autonomous timers.
plan
Strategic planning with optional interview workflow.
web-clone
URL-driven website cloning with visual + functional verification.
ralph
Self-referential loop until task completion with architect verification.