| name | book-to-skill |
| description | Converts books and documents (PDF, EPUB, DOCX, HTML, Markdown, plain text, RTF, MOBI/AZW with Calibre) into structured agent skills, extracting frameworks, mental models, principles, techniques, and anti-patterns. Use when the user wants to study a document through GitHub Copilot CLI, Amp, or Claude Code, apply an author's frameworks while working, or build a reusable knowledge base from a file. |
Book-to-Skill Converter
Transform written knowledge into actionable agent skills by extracting structure — not producing summaries.
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:
- GitHub Copilot CLI personal skills:
~/.copilot/skills/
- Cross-agent personal skills (Copilot + Amp):
~/.agents/skills/
- Claude Code personal skills:
~/.claude/skills/
- Project-local Copilot skills:
.github/skills/
- Project-local Claude skills:
.claude/skills/
- Project-local Amp / Copilot skills:
.agents/skills/
- Amp global skills:
~/.config/agents/skills/
- 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 any input path is an existing skill directory (contains
SKILL.md and a chapters/ sub-folder), or if SKILL_NAME matches an existing skill slug in SKILLS_HOME, 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.
- Technical — has code blocks, tables, formulas, diagrams (e.g. programming books, academic papers, architecture guides)
- Text-heavy — mostly prose, few or no tables/code (e.g. management, productivity, narrative non-fiction)
- 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. 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" $INPUT_PATHS --mode <BOOK_TYPE> --install-missing ask
Before extraction, the script checks optional Python packages needed for the detected format. If a better extractor is missing, it prompts the user with the available fallback. Non-interactive sessions default to fallback unless install mode is explicitly yes.
Tip — preflight the environment: run "$PYTHON_BIN" "$SCRIPT_PATH" --check 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:
<tempdir>/book_skill_work/full_text.txt — combined extracted text of all sources with clear visually demarcated boundaries.<tempdir>/book_skill_work/metadata.json — overall combined size, words, pages, token counts, and a detailed list of individual processed sources.
Read <tempdir>/book_skill_work/metadata.json to inspect the results.
Step 2.5 — Pre-flight cost estimate
Read <tempdir>/book_skill_work/metadata.json 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>
📄 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)
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:
wc -w "$FULL_TEXT_PATH"
grep -n -E "^\s*(Chapter|CHAPTER)\s+[0-9]+" "$FULL_TEXT_PATH" | head -40
sed -n '<start>,<end>p' "$FULL_TEXT_PATH"
grep -c -i "westrum\|dora" "$FULL_TEXT_PATH"
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.
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)
- Apply the author's frameworks while working
- Think with the author's mental models
- Reference specific chapters and concepts
- 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). Probe the user's filesystem for existing skill homes and pick by the host the user is running in:
| Host agent | Personal skill root (probe in order) | Project-local root |
|---|
| GitHub Copilot CLI | ~/.copilot/skills → ~/.agents/skills | .github/skills → .claude/skills → .agents/skills |
| Amp | ~/.agents/skills → ~/.config/agents/skills → ~/.config/amp/skills | .agents/skills |
| Claude Code | ~/.claude/skills | .claude/skills |
Selection rules:
- If exactly one of the host's candidate roots exists on disk, use it without asking.
- If none exist (fresh machine), ask the user which root to create — present the host-appropriate options and remember the choice for the session. Do not silently pick.
- If the user explicitly asked for project-local output, prefer the project-local row.
- If you cannot identify the host, ask: "Which agent are you running this in — GitHub Copilot CLI, Amp, or Claude Code?"
Set SKILLS_HOME to the selected root and check if $SKILLS_HOME/<skill_name>/ already exists.
If it does, prompt the user to choose:
- Update / Fold-in (Mode 4) — integrate new files/content into the existing skill components.
- Overwrite — delete and regenerate the skill from scratch.
- Rename — append
-2 or use a different custom slug.
If the user selects Update / Fold-in, proceed immediately to the Update / Fold-in Workflow section after Step 2.5 (skipping Steps 3, 4, 6, 7, 8, 9).
Step 6 — Create skill directory structure
mkdir -p "$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:
| DEPTH=reference | DEPTH=study |
|---|
BOOK_TYPE=text | 800–1,200 tokens | 1,000–1,800 tokens |
BOOK_TYPE=technical | 1,200–1,800 tokens | 2,000–3,000 tokens |
- 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 $SKILLS_HOME/<skill_name>/chapters/ch<NN>-<slug>.md using the structure below.
Adapt emphasis based on BOOK_TYPE:
technical → prioritize "Code Examples", "Reference Tables", and "Commands & APIs" sections; preserve exact syntaxtext → prioritize "Frameworks Introduced", "Mental Models", and "Key Takeaways"; skip empty technical sections
# Chapter N: <Full Title>
## Core Idea
<1–2 sentences: the single most important thing this chapter teaches>
## Frameworks Introduced
- **<Framework Name>**: <exact formulation — preserve the author's naming>
- When to use: <specific situation>
- How: <steps or criteria>
## Key Concepts
- **<Term>**: <precise definition in 1 sentence>
(5–10 most important terms from this chapter)
## Mental Models
<2–4 frameworks or thinking tools. Write as "Use X when Y" or "Think of X as Y">
## Anti-patterns
- **<What to avoid>**: <why it fails>
## Code Examples
<!-- Copy the most instructive snippet from the chapter. Preserve indentation exactly. -->
Reference Tables (technical books only — omit if BOOK_TYPE=text)
Worked Example (DEPTH=study only — omit for DEPTH=reference)
Key Takeaways
-
-
-
(3–7 takeaways a practitioner must remember)
Connects To
---
## Step 8 — Generate supporting files
### glossary.md
Create `$SKILLS_HOME/<skill_name>/glossary.md`:
- Every significant term from the book, alphabetically sorted
- Format: `**Term** — definition (Ch N)`
- Max 1,500 tokens
### patterns.md
Create `$SKILLS_HOME/<skill_name>/patterns.md`:
- All concrete techniques, design patterns, algorithms from the book
- Format: `## Pattern Name\n**When to use**: ...\n**How**: ...\n**Trade-offs**: ...`
- Max 2,000 tokens
### cheatsheet.md
Create `$SKILLS_HOME/<skill_name>/cheatsheet.md`:
**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 `$SKILLS_HOME/<skill_name>/SKILL.md`:
```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."
---
<!-- argument-hint: [topic, framework name, or chapter number] -->
# <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
<!-- ~2,000 tokens: the author's most important named frameworks and principles.
Preserve exact names. Write as "Use X when Y", "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
<!-- Alphabetical. Major terms/frameworks → chapter(s) 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.
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" "$SKILLS_HOME/<skill_name>"
If the scanner exits non-zero, stop and ask a human to review its file/line findings. Do not silently rewrite the generated files, and do not load or publish the skill until the findings are resolved or explicitly accepted.
Step 10 — Cleanup and report
PYTHON_BIN="${PYTHON_BIN:-python3}"
if ! command -v "$PYTHON_BIN" >/dev/null 2>&1; then
PYTHON_BIN="python"
fi
"$PYTHON_BIN" - <<'PY'
import os
import shutil
import tempfile
from pathlib import Path
shutil.rmtree(
os.environ.get("BOOK_SKILL_WORKDIR", Path(tempfile.gettempdir()) / "book_skill_work"),
ignore_errors=True,
)
PY
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 (Copilot ecosystem, optional):
gh skill publish $SKILLS_HOME/<skill_name>
Update / Fold-in Workflow
When performing an Update/Fold-in operation on an existing skill at $SKILLS_HOME/<skill_name>/:
1. Read Existing Skill Structure
Read and parse the existing skill's files:
- Read
$SKILLS_HOME/<skill_name>/SKILL.md to parse the existing Chapter Index, Topic Index, metadata (author, total chapters), and Core Frameworks.
- List all files in
$SKILLS_HOME/<skill_name>/chapters/ to find the highest chapter number (e.g. ch12).
- Read
$SKILLS_HOME/<skill_name>/glossary.md, $SKILLS_HOME/<skill_name>/patterns.md, and $SKILLS_HOME/<skill_name>/cheatsheet.md to see what terms and frameworks are already indexed.
2. Match Content & Identify Revisions vs. Additions
Analyze the new extracted text in <tempdir>/book_skill_work/full_text.txt 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
$SKILLS_HOME/<skill_name>/chapters/.
4. Merge Supporting Files
- Merge glossary.md:
- Read the existing
$SKILLS_HOME/<skill_name>/glossary.md.
- 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
$SKILLS_HOME/<skill_name>/glossary.md with the fully merged, alphabetized list.
- Merge patterns.md:
- Read existing
$SKILLS_HOME/<skill_name>/patterns.md.
- 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
$SKILLS_HOME/<skill_name>/cheatsheet.md.
- 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 $SKILLS_HOME/<skill_name>/SKILL.md:
- 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.
Quality Rules
- Extract structure, not summaries — capture named frameworks, exact formulations, anti-patterns; not chapter recaps
- Preserve the author's precision — "The 5 Whys" ≠ "ask why multiple times"; keep exact naming
- Density over completeness — a 1,000-token summary beats a 10,000-token excerpt
- Practitioner voice — write "Use X when Y", not "The book explains X"
- Front-load SKILL.md — compaction keeps the first 5,000 tokens; most important content comes first
- Chapter files are on-demand — they don't count against skill budget until loaded
- Never copy raw book text — always synthesize, summarize, extract signal
- Topic index is critical — it's how the agent navigates to the right chapter file
