Book-to-Skill Converter

Transform written knowledge into actionable agent skills by extracting structure — not producing summaries.

Host compatibility

• Generated skills default to the cross-agent personal root ~/.agents/skills, which Copilot CLI, Amp and Codex discover natively; Claude Code scans ~/.claude/skills, so Step 10 links the skill in from there (a junction on Windows, a symlink elsewhere).
• Compatible skill roots: GitHub Copilot CLI (~/.copilot/skills, ~/.agents/skills, .github/skills, .claude/skills, .agents/skills), Amp (.agents/skills, ~/.config/agents/skills, ~/.config/amp/skills), Claude Code (~/.claude/skills).
• allowed-tools is intentionally omitted to stay agent-neutral: Copilot CLI uses shell/MCP-server names, Claude uses Bash/Read/Write/Glob/Grep, and Amp adds shell_command. The skill needs shell to run extract.py plus file read/write; each host requests approval when those tools are invoked.
• Argument pattern: <path-to-document-folder-or-glob>... [skill-name-slug].

Philosophy

Books contain crystallized expertise: frameworks, principles, and techniques that took years to develop. This skill extracts that knowledge into a format GitHub Copilot CLI, Amp, Claude Code, or another compatible agent can leverage repeatedly.

Extract structure, not summaries. A skill isn't a book report. It's a toolkit of:

• Named frameworks (mental models with clear application)
• Actionable principles (rules that guide decisions)
• Techniques (step-by-step methods)
• Anti-patterns (what to avoid and why)
• Voice calibration (how the author thinks and communicates)

Preserve the author's precision. Frameworks often have specific names for reasons. "The 5 Whys" isn't interchangeable with "ask why multiple times." Capture the exact formulation.

Layer depth appropriately. Simple books → simple skills. Complex books with 10+ frameworks → skills with reference files and on-demand chapters.

Modes of Operation

Four paths available. Route based on what the user asks:

1. Full Conversion (Default)

Trigger: User provides one or more document/directory/glob paths without special instructions Action: Run all steps below (Steps 0–9) Output: Complete skill with SKILL.md, chapters/, glossary, patterns, cheatsheet

2. Analyze Only

Trigger: User says "analyze", "just extract", or "I want to review before generating" Action: Run Steps 0–3, then produce a structured extraction report (frameworks, principles, techniques found). Stop — do NOT generate skill files. Output: Analysis report for user review

3. Generate from Prior Analysis

Trigger: User has existing analysis notes or previously ran analyze-only Action: Skip Steps 0–3, use the provided analysis as input, run Steps 4–9 Output: Skill files from the provided analysis

4. Update / Fold-in (Existing Skill)

Trigger: User provides one or more new source paths and indicates they want to update an existing skill (either by pointing to the existing skill folder, providing a skill slug that already exists in SKILLS_HOME, or explicitly requesting an update). Action: Run Step 0 (out-of-scope check), Step 1 (validate inputs), Step 1.5 (identify book type), and Step 2 (extract new files). Then skip to Step 5 (identify/detect existing skill path) and run the Update / Fold-in Workflow to merge the new content into the existing skill files. Output: Updated existing skill with new/revised chapter summaries and merged indexes/glossaries.

Skill Locations

This converter can run from multiple skill systems. When looking for this converter's helper script or writing the generated book skill, prefer these locations in order:

1. Cross-agent personal skills (Copilot, Amp, Codex; the default for generated skills): ~/.agents/skills/
2. GitHub Copilot CLI personal skills: ~/.copilot/skills/
3. Claude Code personal skills: ~/.claude/skills/ (generated skills are linked in from the cross-agent root)

Generated skills use the personal cross-agent root unless the user explicitly asks for a host-private or project-local root. BOOK_TO_SKILL_SCOPE=project or personal can make that choice explicit for automation. 4. Project-local Copilot skills: .github/skills/ 5. Project-local Claude skills: .claude/skills/ 6. Project-local Amp / Copilot skills: .agents/skills/ 7. Amp global skills: ~/.config/agents/skills/ 8. Amp legacy global skills: ~/.config/amp/skills/

For generated book skills, pick a destination that the user's host agent can actually discover (see Step 5). When more than one valid root exists, ask the user once and remember the answer for the session — do not silently default.

Step 0 — Out-of-scope check

If no arguments are provided, stop and respond:

"book-to-skill requires a supported document path, folder, or glob pattern. Usage: book-to-skill <path-to-document-folder-or-glob>... [skill-name-slug]"

Throughout the workflow:

• Identify the input paths and the optional skill slug.
• If the last argument is not a file, folder, or glob that exists or matches any files, and it looks like a skill slug (e.g. lowercase hyphens, alphanumeric), treat it as SKILL_NAME.
• Treat all other arguments as the list of INPUT_PATHS.
• If an input path is an existing skill directory with a root manifest and a chapters/ sub-folder, or if the optional slug matches an existing directory under the selected skills root, flag this run as an Update/Fold-in operation (Mode 4).

Step 1 — Validate input

Verify that there is at least one supported file, directory, or glob pattern among the INPUT_PATHS. For directories and globs, expand them to find matching supported files (.pdf, .epub, .docx, .txt, .md, .markdown, .rst, .adoc, .html, .htm, .rtf, .mobi, .azw, .azw3).

If no supported files are found, stop with a clear error message.

Step 1.5 — Identify content type

Before extracting, ask the user:

"What kind of content do these sources have? This helps me choose the best extraction method.

1. Technical — has code blocks, tables, formulas, diagrams (e.g. programming books, academic papers, architecture guides)
2. Text-heavy — mostly prose, few or no tables/code (e.g. management, productivity, narrative non-fiction)
3. Not sure — I'll use the fast method and warn you if quality seems limited"

Store the answer as BOOK_TYPE:

• Option 1 → BOOK_TYPE=technical
• Option 2 → BOOK_TYPE=text
• Option 3 → BOOK_TYPE=text

If BOOK_TYPE=technical, inform the user before proceeding:

"📐 Technical mode selected — using Docling for structure-aware extraction (tables, code blocks, formulas preserved as markdown). This takes ~1.5s per page, so expect a few minutes for longer sources. First run may download models and is not guaranteed offline. Starting now…"

If BOOK_TYPE=text, inform:

"📄 Text mode selected — using the fastest suitable extractor for each file type. Plain text/Markdown/HTML are usually ready in seconds; PDFs use pdftotext when available."

Step 2 — Extract text from the source documents

Run the extraction script, passing the input paths:

SCRIPT_PATH=""
for candidate in \
  "$HOME/.copilot/skills/book-to-skill/scripts/extract.py" \
  "$HOME/.agents/skills/book-to-skill/scripts/extract.py" \
  "$HOME/.claude/skills/book-to-skill/scripts/extract.py" \
  ".github/skills/book-to-skill/scripts/extract.py" \
  ".claude/skills/book-to-skill/scripts/extract.py" \
  ".agents/skills/book-to-skill/scripts/extract.py" \
  "$HOME/.config/agents/skills/book-to-skill/scripts/extract.py" \
  "$HOME/.config/amp/skills/book-to-skill/scripts/extract.py"
do
  if [ -f "$candidate" ]; then
    SCRIPT_PATH="$candidate"
    break
  fi
done

if [ -z "$SCRIPT_PATH" ]; then
  echo "Could not find scripts/extract.py for book-to-skill" >&2
  exit 1
fi

PYTHON_BIN="${PYTHON_BIN:-python3}"
if ! command -v "$PYTHON_BIN" >/dev/null 2>&1; then
  PYTHON_BIN="python"
fi

"$PYTHON_BIN" "$SCRIPT_PATH" $INPUT_PATHS --mode <BOOK_TYPE> --install-missing ask

Before extraction, the script checks optional Python packages needed for the detected format. If a better extractor is missing, it prompts the user with the available fallback. Non-interactive sessions default to fallback unless install mode is explicitly yes.

Tip — preflight the environment: run <code>"$PYTHON_BIN" "$SCRIPT_PATH" --check</code> to print a per-format report of which extractors are installed and the exact command to install whatever is missing, without processing any file. Useful when a user reports a setup or quality problem.

This creates work files whose exact paths are printed by the extractor (Text -> and Meta ->). Do not assume /tmp/book_skill_work.

Defaults when BOOK_SKILL_WORKDIR is unset:

• Windows: %LOCALAPPDATA%\book-to-skill\work\full_text.txt and metadata.json
• POSIX: <tempdir>/book_skill_work/full_text.txt and metadata.json

Read the printed metadata.json path to inspect the results.

Step 2.5 — Pre-flight cost estimate

Read the extractor-printed metadata.json path and present the user with an estimate before doing any generation:

📖 Sources detected: <total_sources> source(s)
<list each source filename and format from the sources metadata list>
<if images_dropped > 5: warn that N source images were not read>
📄 Combined Pages/Sections: ~<N> | Words: ~<N> | Total tokens: ~<N>K

💰 Estimated token cost (Full Conversion / Update):
   Input  (reading + prompts): ~<N>K tokens
   Output (skill files generated/updated):  ~<N>K tokens
   Total:                           ~<N>K tokens

   Cost: multiply the token counts above by your model's current
   input/output per-1M-token rates (prices and model names change often —
   do not hardcode them; quote today's rate and label it as an estimate).

   ⏱  Estimated time: ~<N> minutes

📁 Files to be generated/updated:
   SKILL.md + chapter files + glossary + patterns + cheatsheet

➡  Proceed with Full Conversion / Update? (or type "analyze only" to preview first)

First, sanity-check the chapter count — the estimate is only as good as this number.

chapters_detected comes from a heading scan that looks for "Chapter N" and for Markdown/AsciiDoc headings. A book that numbers its sections 1., 2., 3., or a PDF flattened by pdftotext (which carries no Markdown syntax), yields 0 while being perfectly well structured.

Zero is not a small error here, it is a structural one: the output formula multiplies by this count, so a 0 deletes the entire chapter-generation term and the estimate collapses to a flat ~8,500 tokens for a book of any size. The user then approves a number that can be off by half or more, and nothing in the output looks wrong.

So when chapters_detected is 0 or obviously disagrees with the source, do the Step 3 structure analysis now, before presenting the estimate, and use the count you derive from the table of contents and confirmed body chapters. Say which number you used and where it came from: "the script detected 0 chapters because this book numbers its sections rather than titling them 'Chapter N'; I mapped 6 from the table of contents and confirmed body text, then estimated on that."

How to estimate:

• Input tokens ≈ estimated_tokens from metadata × 1.3 (prompts overhead per chapter pass)
• Output tokens ≈ chapters × per-chapter budget + 4,000 (SKILL.md) + 4,500 (glossary + patterns + cheatsheet)
  • Per-chapter budget midpoint by BOOK_TYPE (DEPTH is decided later in Step 4 and can raise it): text ≈ 1,000, technical ≈ 1,800. If the user has already indicated reference-only vs deep study, use the matching row of the Step 7 matrix.
• Cost: report the token counts and multiply by the user's current per-1M-token input/output rates. Do NOT hardcode dollar figures — model names and prices change; if you show one, label it an estimate and date it.

Wait for the user to confirm before proceeding. If they say "analyze only", switch to Mode 2.

Step 2.6 — REPL-style access for large books (> 50k tokens)

Inspired by the Recursive Language Model (RLM) paradigm: treat full_text.txt as a queryable corpus, not a single read. Loading the whole file into context burns budget you will need later for generation.

For books over ~50k tokens, prefer programmatic probes over Read(full_text.txt) without bounds:

# Size check before any Read
wc -w "$FULL_TEXT_PATH"

# Find chapter offsets without loading the whole file
grep -n -E "^\s*(Chapter|CHAPTER)\s+[0-9]+" "$FULL_TEXT_PATH" | head -40

# Pull only the chapter you need (lines start..end inclusive)
sed -n '<start>,<end>p' "$FULL_TEXT_PATH"

# Verify a framework is actually mentioned before claiming it in SKILL.md
grep -c -i "westrum\|dora" "$FULL_TEXT_PATH"

# Targeted Read with offset/limit avoids dumping the full file
# Read(file_path=full_text.txt, offset=<line>, limit=<lines>)

Use this approach for Step 3 (structure analysis), Step 7 (per-chapter summaries), and Step 8 (glossary / patterns extraction). On books under 50k tokens, a single Read is fine.

Why this matters: a 200-page book is ~75k tokens. Re-reading it once per chapter (28 passes) costs ~2M input tokens; using grep + sed to pull only relevant slices keeps generation cost proportional to the output, not the source.

Step 3 — Analyze book structure

Read the first 8,000 characters of the extracted full_text.txt to identify:

• Book title and author(s)
• Chapter structure (look for "Chapter N", "PART I", numbered headings, table of contents)
• Core themes and subject domain
• Approximate number of chapters

Then read the Table of Contents section if present to map all chapters.

Treat the ToC as a claim about the book, not as the book. Two things routinely go wrong, and both are silent:

• An entry may resolve to nothing. A ToC row is just a line of text with a page number; extraction can drop the section it points at (a front/back-matter page the extractor skipped, an image-only spread, a PDF whose final pages linearize into boilerplate). Before you commit to a chapter list, confirm each entry actually has body text — grep -n -i "<entry title>" and check for a hit outside the ToC block. If an entry appears only in the ToC, it does not exist as far as you are concerned. Treat page gaps, phantom entries, and duplicate hits the same way: do not write a chapter for them and do not reconstruct one from the title, which is fabrication dressed as extraction (Quality Rule 7). Record the gap in the generated SKILL.md's Scope & Limits so the reader knows the book has a section your skill does not cover.
• Not every top-level entry is a chapter. Front and back matter (Introduction, Preface, Foreword, Conclusion, Afterword, Appendix) often sit at the same indent level as numbered sections. Chapters should be the numbered or clearly parallel body sections. When unnumbered matter carries real content — an Introduction that states the book's core principles, say — fold that content into SKILL.md's Core Frameworks rather than minting a ch00, because a reader looking for those principles will look in the front matter of your skill too. When it carries only throat-clearing, drop it.

If mode is "Analyze Only": produce the extraction report now and stop. Structure:

## Extraction Report — <Title>

### Author's Core Frameworks
- **<Framework Name>**: <what it is and when to apply>

### Key Principles
- <Principle>: <actionable rule>

### Techniques & Methods
- <Technique>: <step-by-step or how-to>

### Anti-patterns
- <What to avoid>: <why>

### Suggested Skill Name
`{author-lastname}-{core-concept}` — e.g. `cialdini-influence`

### Chapters Detected
| # | Title | Main Frameworks |

Step 4 — Ask purpose (Full Conversion only)

Before generating, ask the user:

"What should this skill help you do? (Pick one or more)

1. Apply the author's frameworks while working
2. Think with the author's mental models
3. Reference specific chapters and concepts
4. All of the above"

Use the answer to weight what gets highlighted in the SKILL.md Core section.

Derive DEPTH from the answer (no extra prompt):

• Answer is only option 3 (reference) → DEPTH=reference — lean, fast-lookup chapters.
• Answer includes option 1, 2, or 4 → DEPTH=study — deeper chapters with more worked detail, examples, and reasoning.

DEPTH and BOOK_TYPE together set the per-chapter token budget in Step 7. Do not ask a separate "study vs reference" question — it is inferred here. (In Modes 2/3, where Step 4 is skipped, default DEPTH=study.)

Step 5 — Determine skill name

If SKILL_NAME was provided, use it as the skill slug. Otherwise, propose two options and let the user choose:

• By author-concept: {author-lastname}-{core-concept} (e.g. cialdini-influence, meadows-systems)
• By title: lowercase hyphens from book title (e.g. designing-data-intensive-apps)

Default to author-concept format if the book has a strong methodological identity.

Choose the destination skill root (SKILLS_HOME). First resolve scope from an explicit user request or BOOK_TO_SKILL_SCOPE, then probe host. A request for project-local/project output selects the project-local row; a request for personal/global output selects the personal row. If neither scope is requested, preserve the established personal default (~/.agents/skills). Do not ask a mandatory scope question solely because project-local roots exist. The selected root may still require host approval before writing.

For personal (user-level) installs, the cross-agent root ~/.agents/skills is one physical copy that every supported host reaches, natively or through a link:

Selection rules:

1. Personal install: set SKILLS_HOME to ~/.agents/skills (create it if missing). One exception, so the default does not invent a convention in someone else's house: if ~/.agents/skills does not exist and the host's private root already holds skills, use the private root instead and say why in the report.
2. Claude Code does not scan ~/.agents/skills — Step 10 links the skill into ~/.claude/skills/<skill_name> after generation.
3. If the user explicitly asks for a host-private root (~/.copilot/skills, ~/.claude/skills, ~/.config/agents/skills, ~/.config/amp/skills), honor it and skip the link.
4. If the user explicitly asked for project-local output, or BOOK_TO_SKILL_SCOPE=project is set, use the project-local row for their host.
5. If the choice requires knowing the host (project-local output or the Claude Code link) and you cannot identify it, ask: "Which agent are you running this in — GitHub Copilot CLI, Amp, Codex, or Claude Code?"

Set SKILLS_HOME to the selected root and check whether the selected skill directory already exists. On Claude Code, also check whether ~/.claude/skills/<skill_name> exists as a real directory (not a link) — an earlier install may live there. If it does, offer to migrate it (move the directory into ~/.agents/skills/, then put the link at the original path) before continuing. If the skill directory already exists, prompt the user to choose:

1. Update / Fold-in (Mode 4) — integrate new files/content into the existing skill components.
2. Overwrite — delete and regenerate the skill from scratch.
3. Rename — append -2 or use a different custom slug.

If the user selects Update / Fold-in, proceed immediately to the Update / Fold-in Workflow section after Step 2.5 (skipping Steps 3, 4, 6, 7, 8, 9).

Step 6 — Create skill directory structure

mkdir -p "$SKILLS_HOME/<skill_name>/chapters"

Step 7 — Generate chapter summaries

TOKEN BUDGET RULE — CRITICAL (adaptive):

The per-chapter budget scales with BOOK_TYPE and DEPTH. Technical chapters need room for code and tables; study depth needs room for worked reasoning. Pick the budget from this matrix:

• These are per-file targets, not hard caps — a dense chapter may run over, a thin one under. Density still beats length (Quality Rule #3): never pad to hit a number.
• Files are loaded on-demand, so a larger chapter only costs tokens when that chapter is actually read.
• When in doubt between two cells (e.g. mixed-content book), use the lower budget and let depth come from precision, not volume.

DEPTH=study is earned with content, not a bigger number. The standard section template (Core Idea → Connects To) naturally lands a dense prose chapter around 700–900 tokens. To reach the study budget honestly — not by padding — a study-depth chapter must add concrete material:

• Reproduce one worked example or artifact from the chapter (e.g. the example press release, a sample dialogue, a filled-in template, a decision the author walks through) under a ## Worked Example section. This is the single biggest lever and the main thing a learner returns for.
• Expand the "How" of each framework into explicit steps or criteria, not a one-liner.
• Add a short "Why it works / failure mode" note to the top 1–2 frameworks.

If a chapter genuinely has no worked example and resists expansion, let it land below the study floor rather than padding — and note that the chapter is thin in its Core Idea. A reference-depth chapter, by contrast, deliberately omits worked examples and keeps only the decision-ready essentials.

For EACH chapter/major section identified in Step 3:

Read the corresponding section of the extracted full_text.txt (use character offsets or grep for chapter headings).

Create ch<NN>-<slug>.md in the selected skill directory's chapters/ folder using the structure below.

Adapt emphasis based on BOOK_TYPE:

• technical → prioritize "Code Examples", "Reference Tables", and "Commands & APIs" sections; preserve exact syntax
• text → prioritize "Frameworks Introduced", "Mental Models", and "Key Takeaways"; skip empty technical sections

# Chapter N: <Full Title>

## Core Idea
<1–2 sentences: the single most important thing this chapter teaches>

## Frameworks Introduced
- **<Framework Name>**: <exact formulation — preserve the author's naming>
  - When to use: <specific situation>
  - How: <steps or criteria>

## Key Concepts
- **<Term>**: <precise definition in 1 sentence>
(5–10 most important terms from this chapter)

## Mental Models
<2–4 frameworks or thinking tools. Write as "Use X when Y" or "Think of X as Y">

## Anti-patterns
- **<What to avoid>**: <why it fails>

## Code Examples *(technical books only — omit if BOOK_TYPE=text)*
```<language>
<key code example from this chapter>

• What it demonstrates: <one line>

Reference Tables (technical books only — omit if BOOK_TYPE=text)

Reproduce any comparison matrix, parameter table, or decision table from the chapter in Markdown.

Worked Example (DEPTH=study only — omit for DEPTH=reference)

Reproduce or reconstruct one concrete example the author works through: a sample document, dialogue, filled-in template, before/after, or decision walked end-to-end. Keep it faithful to the source; never copy long raw passages; reconstruct the example compactly.

Key Takeaways

1. <Actionable insight>
2. <Actionable insight>
3. <Actionable insight>

(3–7 takeaways a practitioner must remember)

Connects To

• Ch N: <why this chapter relates>
• <Concept>: <external concept or standard it connects with>

---

## Step 8 — Generate supporting files

### glossary.md
Create <code>$SKILLS_HOME/&lt;skill_name&gt;/glossary.md</code>:
- Every significant term from the book, alphabetically sorted
- Format: `**Term** — definition (Ch N)`
- Max 1,500 tokens

### patterns.md
Create <code>$SKILLS_HOME/&lt;skill_name&gt;/patterns.md</code>:
- All concrete techniques, design patterns, algorithms from the book
- Format: `## Pattern Name\n**When to use**: ...\n**How**: ...\n**Trade-offs**: ...`
- Max 2,000 tokens

### cheatsheet.md
Create <code>$SKILLS_HOME/&lt;skill_name&gt;/cheatsheet.md</code>:

**This is the most differentiated layer of the skill — treat it as a reasoning aid, not a keyword list.** Anyone can grep the glossary for a term. The cheatsheet captures the author's *judgment*: the decisions they'd make and why. It's the file that turns "I know the words" into "I'd act the way the author would".

Prioritize, in order:
1. **Decision rules** — "When X, do Y, because Z." The if/then logic the author applies, stated so the reader can apply it without re-reading the book.
2. **Decision trees / flowcharts** (as nested bullets or a small table) — for choices with more than two branches.
3. **Trade-off matrices** — competing options scored on the dimensions the author cares about, so the reader can pick under their own constraints.
4. **Thresholds & defaults** — the specific numbers, ratios, or rules of thumb the author commits to (e.g. "keep functions under ~20 lines", "alert when error budget < 10%").
5. **Tells & smells** — fast heuristics for recognizing a situation ("if you see X, you're probably in trouble Y").

Avoid: bare term→definition rows (that's the glossary), and prose paragraphs (that's the chapters). Every line should help the reader *decide* something.

- Format mostly as compact tables and decision rules; the content you'd want on a single printed page kept beside you while working.
- Max 1,200 tokens.

---

## Step 9 — Generate the master SKILL.md

**CRITICAL TOKEN BUDGET: Keep SKILL.md body under 4,000 tokens.**
Compaction truncates from the END — put the most important content FIRST.

Create <code>$SKILLS_HOME/&lt;skill_name&gt;/SKILL.md</code>:

```markdown
---
name: <skill_name>
description: "Knowledge base from \"<Full Title>\" by <Author(s)>. Use when applying <author>'s frameworks for <key topics, 3–6 terms>, studying the book, or referencing its concepts."
---

# <Full Title>
**Author**: <Author(s)> | **Pages**: ~<N> | **Chapters**: <N> | **Generated**: <YYYY-MM-DD>

## How to Use This Skill

- **Without arguments** — load core frameworks for reference
- **With a topic** — ask about `replication`, `pricing`, or another indexed topic; I find and read the relevant chapter
- **With chapter** — ask for `ch05`; I load that specific chapter
- **Browse** — ask "what chapters do you have?" to see the full index

When you ask about a topic not covered in Core Frameworks below, I will read
the relevant chapter file before answering.

---

## Core Frameworks & Mental Models
Use about 2,000 tokens for the author's most important named frameworks and
principles. Preserve exact names. Write as "Use X when Y" or "Prefer X over Y
because Z". This is a toolkit, not a summary.

<generate 2,000 tokens of the most critical frameworks and insights here>

---

## Chapter Index

| # | Title | Key Frameworks |
|---|-------|----------------|
| [ch01](chapters/ch01-<slug>.md) | <Title> | <framework1>, <framework2> |
| [ch02](chapters/ch02-<slug>.md) | <Title> | <framework1>, <framework2> |
...

## Topic Index

List major terms and frameworks alphabetically with the chapters that cover them.
- **<Term>** → ch<N>[, ch<N>]
- **<Term>** → ch<N>

## Supporting Files

- [glossary.md](glossary.md) — all key terms with definitions
- [patterns.md](patterns.md) — all techniques and design patterns
- [cheatsheet.md](cheatsheet.md) — quick reference tables and decision guides

---

## Scope & Limits

This skill covers the book content only. For hands-on implementation in your codebase,
combine with project-specific tools. For topics beyond this book, check related skills
or ask the agent directly.
<if images_dropped > 5: state that N source images were not read>

Step 9.5 — Scan the generated skill

Before reporting success, loading the skill in another session, or publishing it, run the advisory security scan:

SKILL_CONVERTER_ROOT="$(cd "$(dirname "$SCRIPT_PATH")/.." && pwd)"
"$PYTHON_BIN" "$SKILL_CONVERTER_ROOT/tools/scan_generated_skill.py" "$SKILLS_HOME/<skill_name>"

If the scanner exits non-zero, stop and ask a human to review its file/line findings. Do not silently rewrite the generated files, and do not load or publish the skill until the findings are resolved or explicitly accepted.

Step 10 — Cleanup and report

Delete the directory this run actually used, not one derived again from the environment: metadata.json records it as workdir. Re-deriving can name a different directory — a concurrent run's, or the previous one if BOOK_SKILL_WORKDIR changed since the extraction — and rm -rf does not ask twice.

$META_PATH below is the Meta -> path the extractor printed.

PYTHON_BIN="${PYTHON_BIN:-python3}"
if ! command -v "$PYTHON_BIN" >/dev/null 2>&1; then
  PYTHON_BIN="python"
fi

"$PYTHON_BIN" - "$META_PATH" <<'PY'
import json
import shutil
import sys
from pathlib import Path

meta = Path(sys.argv[1])
try:
    workdir = json.loads(meta.read_text(encoding="utf-8")).get("workdir")
except (OSError, ValueError):
    workdir = None

if workdir:
    shutil.rmtree(workdir, ignore_errors=True)
else:
    # No metadata to read back: leave the directory alone and say so. A guess here
    # would be a guess about which directory to delete.
    print("Skipped cleanup: could not read the workdir from", meta)
PY

Link the skill into Claude Code

When the host is Claude Code and SKILLS_HOME is ~/.agents/skills (the default personal install), expose the skill to Claude Code with a link — Claude Code only scans ~/.claude/skills. Skip this when the user chose a host-private or project-local root (Step 5, rules 3–4).

A real directory already sitting at the link path must never be overwritten or nested into: ln -sfn into an existing directory puts the link inside it (~/.claude/skills/<skill_name>/<skill_name>) and Claude Code keeps loading the stale copy. If the user declined the Step 5 migration, skip the link and say so.

POSIX hosts:

mkdir -p "$HOME/.claude/skills"
LINK="$HOME/.claude/skills/<skill_name>"
TARGET="$HOME/.agents/skills/<skill_name>"
if [ -d "$LINK" ] && [ ! -L "$LINK" ]; then
  CLAUDE_STATUS="skipped-realdir"
else
  ln -sfn "$TARGET" "$LINK" 2>/dev/null || true
  if [ -L "$LINK" ] && [ "$(readlink "$LINK")" = "$TARGET" ]; then
    CLAUDE_STATUS="linked"
  elif [ -e "$LINK" ]; then
    CLAUDE_STATUS="copy"
  else
    CLAUDE_STATUS="absent"
  fi
fi

Windows: use a directory junction, not a symlink. Measured on Windows 11 without Developer Mode, New-Item -ItemType SymbolicLink fails with "此作業需要系統管理員權限" (this operation requires administrator privileges), while a junction is created by an ordinary user and Claude Code follows it. ln -s under Git Bash/MSYS may silently copy instead of link, so do not use it here.

$link = Join-Path $HOME ".claude\skills\<skill_name>"
$target = Join-Path $HOME ".agents\skills\<skill_name>"
New-Item -ItemType Directory -Force -Path (Split-Path -Parent $link) | Out-Null
$existing = Get-Item -LiteralPath $link -Force -ErrorAction SilentlyContinue
if ($existing -and -not $existing.LinkType) {
    $claudeStatus = "skipped-realdir"
} else {
    if ($existing) { Remove-Item -LiteralPath $link -Force -Recurse }
    New-Item -ItemType Junction -Path $link -Target $target -ErrorAction SilentlyContinue | Out-Null
    $now = Get-Item -LiteralPath $link -Force -ErrorAction SilentlyContinue
    if ($now -and $now.LinkType -and $now.Target -contains $target) { $claudeStatus = "linked" }
    elseif ($now) { $claudeStatus = "copy" }
    else { $claudeStatus = "absent" }
}

Read the link back before reporting anything about it. The link is a claim until the filesystem confirms it: fill the "Discoverable by" line from the status variable, never from "the command was issued". A missing link is not a failure of the run — the skill exists at the cross-agent root and every other host still finds it. The honest report is "written to ~/.agents/skills/<skill_name>; Claude Code will not see it until the link exists", not an abort.

Then report to the user:

✅ Skill created: $SKILLS_HOME/<skill_name>/

📚 Book: <Full Title> — <Author>
📄 Pages: ~<N> | Chapters: <N>

Files generated:
  SKILL.md         — core frameworks + index   (~X tokens)
  chapters/        — <N> chapter summaries     (~X tokens each, ~X total)
  glossary.md      — key terms                 (~X tokens)
  patterns.md      — techniques & patterns     (~X tokens)
  cheatsheet.md    — quick reference           (~X tokens)
  ─────────────────────────────────────────────────────
  Total skill size: ~X tokens (loaded on-demand, not all at once)

💡 Tip: check your agent's session cost/usage command to see actual token usage.

Usage:
  Ask for <skill_name>                  → load core frameworks
  Ask <skill_name> about <topic>        → find and explain a topic
  Ask <skill_name> for ch<N>            → dive into a specific chapter

Reload (if your agent doesn't auto-detect new skills):
  GitHub Copilot CLI:  /skills reload
  Claude Code:         restart the session
  Amp:                 restart the session

Share this skill (optional):
  GitHub repo, installable on any host (Step 11):  say "publish"
  Copilot ecosystem:  gh skill publish $SKILLS_HOME/<skill_name>

Step 11 — Publish the generated skill to GitHub (optional)

After the Step 10 report, offer once — and only if the Step 9.5 scan passed:

"Want me to publish this skill to GitHub so any Agent Skills host can install it with npx skills@1.5.23 add? (yes / skip)"

If the user declines, stop here. Requirements: the gh CLI, authenticated (check gh auth status). If gh is missing or unauthenticated, offer to set it up (brew install gh or https://cli.github.com, then gh auth login) — or use the no-gh path: the user creates an empty repo of the chosen visibility in the GitHub web UI, then you run the git init/add/commit commands below followed by git remote add origin <repo-url> && git push -u origin main. The visibility rule below applies to the web-created repo exactly the same.

Visibility is a separate closed question — never inferred, never read out of an earlier answer. Once the user accepts, ask it on its own and require a one-word reply:

"Private or public repository? Reply with one word: private or public."

The reply must be public, not merely contain it — a hard rule, not a suggestion. Run gh repo create with --private in every case except one: the answer to the visibility question is the bare word public. Substring matching is forbidden, because a sentence about the source's licence is not a visibility answer — "it's public domain", "the book is public domain", "it's publicly available" all describe the material, not the repository, and all resolve to --private. A paraphrase, a sentence, an ambiguous answer, silence, or your own inference is NOT consent: re-ask once, and if the reply is still not the bare word, use --private and say so in the report. A private repo can be flipped public later; a public push of book-derived content cannot be un-published.

Copyright gate — always apply before creating the repo: chapter files are synthesized summaries, not raw text, but they still derive from the source material. Per the README's Copyright & fair use policy, skills generated from third-party copyrighted books must stay private; offer public only when the source is the user's own writing, openly licensed content, or material the user explicitly confirms they are authorized to redistribute publicly — and state which case applies. Having access to internal company material is not permission to disclose it: skills from internal docs stay private unless the user states they hold publication rights.

If accepted:

1. Add a repo README.md inside <code>$SKILLS_HOME/<skill_name>/</code> (never overwrite an existing file) — the skill title, a one-paragraph description ("Agent skill generated from <Title> by <Author> with book-to-skill"), the install command from step 3 below, the file inventory, and a note that the content is synthesized summaries, not the book text.
2. Initialize the skill folder as a git repository and create the remote (default repo name <skill_name>; let the user override — some prefer a <skill_name>-skill suffix). Nested-repo guard: first check whether the skill folder already sits inside a git repository (<code>git -C "$SKILLS_HOME/<skill_name>" rev-parse --show-toplevel</code> — always the case for project-local roots like .claude/skills/). If it does, do NOT git init in place: the outer repository would record the folder as an embedded repo (gitlink, mode 160000) without .gitmodules, and fresh clones of the outer project would silently omit the skill. Instead, copy the skill folder to a scratch directory, run the commands below from the copy, and tell the user the published repo — not the project-local folder — is the remote's working copy.

cd "$SKILLS_HOME/<skill_name>"
git init -b main
git add -A
git commit -m "Add <skill_name> skill"
gh repo create <repo_name> --private --source . --push
# --private is the default; substitute --public ONLY under the visibility rule above
# (the visibility answer WAS the bare word "public" AND the copyright gate allows it)

3. Report the repo URL and the cross-host install command:

✅ Published: https://github.com/<owner>/<repo_name> (<private|public>)

Install on any Agent Skills host:
  npx skills@1.5.23 add https://github.com/<owner>/<repo_name> --skill <skill_name>

When the nested-repo guard fired and the repo was published from a scratch copy, add one line — that local folder never gains a remote, so the Update/Fold-in push offer will never appear for it:

⚠️  Published from a copy: <skill folder> sits inside another git repository, so it has
    no remote of its own. To publish a later update, re-run Step 11, or clone
    https://github.com/<owner>/<repo_name> and fold new material into the clone.

The generated root manifest layout is exactly what the pinned skills CLI detects, so the repo is installable as-is — no restructuring needed. Outside the nested-repo case the local folder stays the live install for this machine and is the remote's working copy, so later Update/Fold-in runs can commit and push their changes to the same remote.

Update / Fold-in Workflow

When performing an Update/Fold-in operation on an existing skill at <code>$SKILLS_HOME/<skill_name>/</code>:

1. Read Existing Skill Structure

Read and parse the existing skill's files:

• Read <code>$SKILLS_HOME/<skill_name>/SKILL.md</code> to parse the existing Chapter Index, Topic Index, metadata (author, total chapters), and Core Frameworks.
• List all files in <code>$SKILLS_HOME/<skill_name>/chapters/</code> to find the highest chapter number (e.g. ch12).
• Read <code>$SKILLS_HOME/<skill_name>/glossary.md</code>, <code>$SKILLS_HOME/<skill_name>/patterns.md</code>, and <code>$SKILLS_HOME/<skill_name>/cheatsheet.md</code> to see what terms and frameworks are already indexed.

2. Match Content & Identify Revisions vs. Additions

Analyze the new extracted text in the extractor-printed full_text.txt path to identify if the new content represents:

• Updates/Revisions to existing chapters: If a section of the new content directly updates or expands an existing chapter's topic, read the existing chapter file, merge the new details into it, and rewrite the file.
• New additions: If the content introduces new chapters, papers, or separate sections, create new chapter summary files under chapters/. Start numbering these files after the highest existing chapter number (e.g. if the existing chapters stop at ch12, create ch13-*.md, ch14-*.md, etc.).

3. Generate or Update Chapter Summary Files

For each new or revised chapter:

• Read the corresponding section of the extracted new text.
• Follow the formatting guidelines in Step 7 to build the summary.
• Write/update the file in <code>$SKILLS_HOME/<skill_name>/chapters/</code>.

4. Merge Supporting Files

• Merge glossary.md:
  • Read the existing <code>$SKILLS_HOME/<skill_name>/glossary.md</code>.
  • Extract all new terms and definitions from the new content (Step 8 glossary guidelines).
  • Combine and alphabetize the list of existing and new terms.
  • If a term already exists, append the new chapter/source references to it (e.g. **Term** — definition (Ch 4, Ch 13)).
  • Rewrite <code>$SKILLS_HOME/<skill_name>/glossary.md</code> with the fully merged, alphabetized list.
• Merge patterns.md:
  • Read existing <code>$SKILLS_HOME/<skill_name>/patterns.md</code>.
  • Extract any new techniques, algorithms, or patterns from the new content.
  • Append the new patterns, ensuring consistent formatting, and keeping the total length concise (under 2,500 tokens).
• Merge cheatsheet.md:
  • Read existing <code>$SKILLS_HOME/<skill_name>/cheatsheet.md</code>.
  • Extract new comparison rules, decision tables, or parameter guides.
  • Integrate them cleanly into the cheatsheet structure.

5. Re-generate the Master SKILL.md

Update the master skill file <code>$SKILLS_HOME/<skill_name>/SKILL.md</code>:

• Metadata: Increment the chapter count, update the estimated page count, and add the new source names if appropriate. Update the Generated date to the current date.
• Core Frameworks: Fold in the most high-impact mental models or principles from the new content (ensuring the overall file remains under 4,000 tokens).
• Chapter Index: Append the new chapters to the index table, linking to the newly created files.
• Topic Index: Merge the new topics alphabetically. If an existing topic is also covered in the new chapters, append the new chapter links to its line (e.g. - **Topic** → ch05, ch13).

6. Scan, Cleanup, and Report

Once the files are successfully written and merged, run Step 9.5, then proceed to Step 10 to perform cleanup and print a custom update report summarizing the newly added chapters, merged glossary terms, and updated indices. If the skill folder is a git repository with a remote (published via Step 11), offer to commit the update and push it.

Quality Rules

1. Extract structure, not summaries — capture named frameworks, exact formulations, anti-patterns; not chapter recaps
2. Preserve the author's precision — "The 5 Whys" ≠ "ask why multiple times"; keep exact naming
3. Density over completeness — a 1,000-token summary beats a 10,000-token excerpt
4. Practitioner voice — write "Use X when Y", not "The book explains X"
5. Front-load SKILL.md — compaction keeps the first 5,000 tokens; most important content comes first
6. Chapter files are on-demand — they don't count against skill budget until loaded
7. Never copy raw book text — always synthesize, summarize, extract signal
8. Topic index is critical — it's how the agent navigates to the right chapter file