{
  "schema_version": "1.1",
  "id": "s9:https://github.com/virgiliojr94/book-to-skill",
  "slug": "book-to-skill-1av16sr",
  "url": "https://feed7.dev/p/book-to-skill-1av16sr",
  "title": "virgiliojr94/book-to-skill",
  "why_included": "book-to-skill compiles books and document sets into on-demand agent skills, reducing repeated context loading while preserving chapter-level references and reusable decision rules.",
  "summary": "book-to-skill converts PDFs, ebooks, document folders, or globs into a shared SKILL.md, a core index, and on-demand chapter files. Its tests report **24–51× fewer tokens** than repeatedly placing a full book in context.",
  "practical_implication": "Use this pattern for references you revisit: internal docs, standards, research clusters, or technical books. The generated structure front-loads mental models and loads only the relevant chapter, while the same skill format works in Claude Code, Amp, and GitHub Copilot CLI.",
  "agent_context": "book-to-skill converts PDFs, ebooks, document folders, or globs into a shared SKILL.md, a core index, and on-demand chapter files. Its tests report **24–51× fewer tokens** than repeatedly placing a full book in context.\n\nUse this pattern for references you revisit: internal docs, standards, research clusters, or technical books. The generated structure front-loads mental models and loads only the relevant chapter, while the same skill format works in Claude Code, Amp, and GitHub Copilot CLI.\n\nThe repository says conversion costs roughly **$1 per book**, and repeated use is where amortization helps. Chapter detection expects explicit numbered headings; one-off reading, broad library search, and poorly segmented sources may be better served by other approaches.",
  "source": {
    "name": "GitHub",
    "url": "https://github.com/virgiliojr94/book-to-skill",
    "published_at": null
  },
  "source_class": "tool",
  "content_type": "GitHub Repo",
  "layer": "context",
  "domains": [
    "coding",
    "research"
  ],
  "topics": [
    "skills",
    "context-engineering",
    "retrieval"
  ],
  "verification": {
    "status": "needs_review",
    "label": "Needs Review",
    "method": "unverified",
    "verified_at": null
  },
  "uncertainty": [
    "The repository says conversion costs roughly **$1 per book**, and repeated use is where amortization helps. Chapter detection expects explicit numbered headings; one-off reading, broad library search, and poorly segmented sources may be better served by other approaches."
  ],
  "connected_context": {
    "meaning": "This makes the shared-context pattern concrete for stable, repeatedly consulted documents: preprocess them into a portable skill with a compact index and selectively loaded chapters. Unlike repository graphs, recursive inspection, or evidence replay, it pays an upfront conversion cost and depends on explicit document structure; its reported token savings therefore support repeated reference use, not general library search or one-off reading.",
    "corpus_size": 262,
    "generated_at": "2026-07-29T10:04:56.704Z",
    "connections": [
      {
        "title": "WTF Is the Context Layer? The Missing Infrastructure for Production Agents — Prukalpa Sankar",
        "source_name": "AI Engineer",
        "source_url": "https://www.youtube.com/watch?v=8G_1-3IO4ZQ",
        "feed7_url": "https://feed7.dev/p/wtf-is-the-context-layer-the-missing-infrastructure-for-production-agent-0t47xqf",
        "reason": "It implements the talk’s portable, shared-context idea as a skill that multiple agent harnesses can reuse."
      },
      {
        "title": "RLM: Recursive Language Models for Large Codebases - Shashi, Superagentic AI",
        "source_name": "YouTube",
        "source_url": "https://www.youtube.com/watch?v=8oyalrfwgjw",
        "feed7_url": "https://feed7.dev/p/rlm-recursive-language-models-for-large-codebases-shashi-superagentic-ai-1w21nex",
        "reason": "Both keep large source material outside the main context, but book-to-skill preprocesses structured chapters while RLMs inspect external data recursively at runtime."
      },
      {
        "title": "Graphify-Labs/graphify",
        "source_name": "GitHub",
        "source_url": "https://github.com/Graphify-Labs/graphify",
        "feed7_url": "https://feed7.dev/p/graphify-1e0bs1f",
        "reason": "Both reduce repeated retrieval work, but target different source shapes: chapter-indexed documents versus provenance-tagged relationships in codebases."
      },
      {
        "title": "ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning",
        "source_name": "arXiv",
        "source_url": "https://arxiv.org/abs/2607.02509v1",
        "feed7_url": "https://feed7.dev/p/2607-02509v1-11vodps",
        "reason": "Both surface only relevant evidence, but ReContext performs query-time replay from long inputs whereas book-to-skill creates a reusable indexed artifact in advance."
      }
    ]
  },
  "lifecycle": "Current",
  "published_at": null,
  "modified_at": null,
  "supersedes": [],
  "expires_at": null,
  "formats": {
    "html": "https://feed7.dev/p/book-to-skill-1av16sr",
    "json": "https://feed7.dev/p/book-to-skill-1av16sr.json",
    "markdown": "https://feed7.dev/p/book-to-skill-1av16sr.md"
  }
}