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 markoblogo/abvx-agent-skills --skill book-to-skillgit clone --depth 1 https://github.com/markoblogo/abvx-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/markoblogo/abvx-agent-skills/book-to-skill)<a href="https://agentmods.dev/skills/markoblogo/abvx-agent-skills/book-to-skill"><img src="https://agentmods.dev/badge/skills/markoblogo/abvx-agent-skills/book-to-skill/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/markoblogo/abvx-agent-skills/book-to-skill"><img src="https://agentmods.dev/badge/skills/markoblogo/abvx-agent-skills/book-to-skill.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 | $0.00079 | $0.00663 |
| Opus 5.5 | $0.00032 | $0.00265 |
| Sonnet 5.5 | $0.00016 | $0.00133 |
| Haiku 4.5 | $0.00008 | $0.00066 |
Grade A, and why
book-to-skill 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 2mo 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Book To Skill
Turn written knowledge into an actionable skill. Extract reusable structure, not a book report.
Conversion Gate
Before generating files, decide whether the source deserves a skill:
- method density: frameworks, procedures, patterns, checklists, or decision rules;
- actionability: future work can be guided by "use X when Y";
- navigation value: sections can become references or trigger rules;
- extraction quality: text is legible enough to avoid invented structure;
- rights and privacy: no large raw excerpts, private data, or copyrighted chapter dumps.
If the source is mostly narrative, confidential, low quality OCR, or thin on reusable method, produce an extraction report or notes instead.
If the user wants the source document to become model memory or a generated adapter rather than a reusable instruction artifact, switch to doc-to-lora-evaluator before proposing any Doc-to-LoRA, RAG, or fine-tuning workflow.
Modes
- Analyze only: extract frameworks, concepts, terms, and candidate workflows for review.
- Generate skill: create
SKILL.md, optional references, and a short skill card from approved analysis. - Refine existing skill: compare a current skill against the source and propose scoped improvements.
Workflow
- Validate the input path or URL and identify format, size, author/title if available, and likely extraction risk.
- Extract text with the repo's available tooling. Prefer structured extractors for technical sources; use plain text extraction for prose.
- Estimate scope before heavy work: source length, expected references, and whether generation should be split into sections.
- Build a structure map: table of contents, named frameworks, repeated principles, examples, anti-patterns, and vocabulary.
- Select the skill boundary. A good skill should have one clear job. Split broad sources into multiple skills only when trigger rules differ.
- Write concise trigger-rich frontmatter. Keep always-loaded text small.
- Put detailed examples, chapter notes, or long checklists in
references/and load them only when needed. - Preserve precise names for frameworks, but paraphrase explanations and avoid long verbatim excerpts.
- Add risk gates: copyright, privacy, domain accuracy, outdated claims, and source uncertainty.
- Validate the resulting skill against the target repo's standard.
What ships with it
2 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.
- 2mo ago First seen · 69 lines · 79 tokens per session scan A 25ad9f73ca41
book-to-skill is a skill published in the GitHub repository markoblogo/abvx-agent-skills (16 stars, last pushed yesterday), licensed MIT. It adds 79 tokens to every session and 663 once invoked, about $0.0003 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.
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