From PDF Skill Builder

Build a skill from PDF(s) via vision

This skill is the playbook for converting one or more PDF reference documents into a high-quality Claude Code skill. The output is a skill folder where:

• Source PDFs are bundled (the skill is self-contained and portable)
• Each chapter/section is a structured markdown file (real headings, tables, blockquotes, inline diagram references)
• Diagrams are extracted as PNG and named by content
• SKILL.md has frontmatter triggers, navigation, quick-lookup tables, and "common questions → file" pointers

The defining technique is vision extraction via a general-purpose subagent per chapter. Reading PDF pages as images and writing structured markdown gives dramatically higher quality than pdftotext for any document with tables, multi-column layouts, callouts, or diagrams.

Target skill structure

<skill-name>/
├── SKILL.md              # Frontmatter + navigation + quick-lookup
├── <source>.pdf          # Bundled original (or multiple)
├── kapitler/             # Use original-language word: "chapters", "kapitler", "kapitel", "luvut"...
│   ├── 01-<slug>.md
│   ├── 02-<slug>.md
│   └── ...
└── diagrammer/           # Match source-language word as for chapters
    ├── <content-name>.png
    └── ...

Optional subfiles for related but distinct topics that don't fit the chapter model (e.g. a verbatim certificate forsikringsbevis.md alongside curated redningsselskapet.md).

Process

1. Setup

1. Choose a kebab-case skill name that names the domain, not the document title (e.g. motor-yamaha-f100, not yamaha-f100-owners-manual-2010).
2. Create folder structure: SKILL.md, kapitler/, diagrammer/.
3. Copy source PDF(s) into the skill folder. Rename to descriptive slugs (e.g. manual.pdf, policy.pdf, loggen.pdf) — not their original filenames.

2. Discover document structure

Before extracting, identify chapters/sections:

• Read the TOC page or sample a few pages with pdftotext -f N -l N <pdf> - to get titles and ranges.
• Note any difference between physical PDF pages (what pdftoppm uses) and logical pages (what the document prints). Vehicle/machinery manuals often have a 6-page offset; magazines and books usually match.
• Map each chapter to a physical page range. Save this mapping — every subagent needs its range.

3. Vision-extract chapters in parallel

For each chapter, spawn one general-purpose subagent using the template at templates/subagent-vision-prompt.md. Fill in the placeholders, then invoke.

Send all subagent invocations in a single message so they run in parallel. Wall-clock time is bounded by the longest chapter (typically 10-15 minutes for a 20+ page chapter, 1-3 minutes for short chapters).

Token cost is roughly 40-70k per chapter; budget accordingly for large documents.

Each subagent returns a brief report:

• Pages processed
• Suggested new diagrams (with proposed slug and description)
• Uncertain transcriptions to verify
• Quality verdict

Collect the diagram suggestions across all subagents.

4. Extract diagrams

Render suggested diagrams (and any obvious whole-page figures you want available):

pdftoppm -png -r 200 -f N -l N source.pdf /path/to/diagrammer/<content-name>

• 200 DPI is the default — good balance of quality and file size.
• 300 DPI for verification of suspect text or fine detail.
• Name PNGs by what the figure shows, not its page number. greasing-points-f80bet.png, not page-80.png.
• pdftoppm zero-pads the resulting filename with 2 or 3 digits depending on the page range — handle both when renaming:

# Strip the -NN or -NNN suffix pdftoppm adds:
for f in *.png; do
  new=$(echo "$f" | sed -E 's/-[0-9]{2,3}\.png$/.png/')
  [ "$f" != "$new" ] && mv "$f" "$new"
done

Verify each rendered diagram by opening it with the Read tool — confirm it shows what the chapter file references.

5. Build SKILL.md

Use the skeleton at templates/skill-md-skeleton.md.

Key choices:

• Description language: write the frontmatter description in the user's likely query language, not the document's language. A Norwegian user querying about an English manual will type in Norwegian; the description must match those queries to trigger.
• Description content: list explicit trigger phrases — synonyms, specific identifiers (policy numbers, model codes), product names. The combined description + when_to_use is capped at 1,536 characters, so be dense.
• Quick-lookup tables: put the most-asked facts directly in SKILL.md (specs that change rarely, key numbers, contacts). These avoid loading a full chapter for a one-line answer.
• Navigation table: every chapter and diagram should be linked from SKILL.md so users can jump straight in. Include short descriptions of what each contains.
• Common questions → file: add a table like "Where is X? → [chapter Y]". This is a learned-question map; populate from real or anticipated queries.

Keep SKILL.md under 500 lines per Anthropic guidance. Move detail to chapter files or topical subfiles.

6. Validate

• Spot-check 15-25 critical datapoints from the chapter files against the PDF, focusing on safety- or finance-critical numbers (torques, capacities, deductibles, sums, fuse ratings, dates, prices).
• Verify all markdown links resolve. Pattern:

  cd <skill-folder>
  grep -hoE '\(kapitler/[^)]+\)|\(diagrammer/[^)]+\)' SKILL.md kapitler/*.md | \
    sed -E 's/^\(//; s/\)$//' | sort -u | \
    while read p; do [ -f "$p" ] && echo "OK $p" || echo "MISS $p"; done
  

• Test the trigger. Restart Claude (or open a new session) and ask 3-5 sample questions matching the description. Verify the skill activates and gives correct answers.

7. Correct OCR errors

When subagent reports flag uncertain transcriptions, re-render the suspect page at 300 DPI and verify with the Read tool. Apply targeted edits to the chapter file. See "OCR error patterns" below.

Conventions

Faithfulness to source

• Do not translate. Source-language content stays in source language. Only the SKILL.md navigation layer (descriptions, quick-lookup labels) uses the user's language.
• Do not paraphrase or summarize. Chapter files are verbatim transcriptions.
• Preserve original typos. Do not "correct" the source author. (This is part of validation honesty.)
• [unreadable: see source.pdf page N] rather than guess.

Filename language

Match the source document's language:

Within each folder, use English filenames matching the document's own chapter/diagram terminology. So an English boat manual's chapter 5 "Characteristics and use" becomes chapters/05-characteristics-and-use.md.

Bundling

Always bundle the source PDF(s) into the skill folder. The skill is then portable and self-verifiable — anyone can compare extracted markdown against the source. Symlinks are brittle; copy the files.

Quality rules

Vision rendering

• Default 200 DPI. Higher DPI = sharper text but larger PNGs and more processing.
• 300 DPI for verification of dense small text or where subagent flagged uncertainty.
• Re-render is cheap. When in doubt, look at the page image again.

Two-column / multi-column layouts

This is the single biggest reason to use vision instead of pdftotext. The subagent prompt explicitly instructs: read each column top-to-bottom completely before moving to the next column, never interleave.

Numbers and exact values

Safety- and finance-critical numbers must be exact. For each number transcribed, the subagent should be able to re-read it from the page image without ambiguity. The prompt includes "re-read each torque/capacity/voltage" and "preserve units exactly (mm vs in, L vs US qt, Nm vs ft-lb)".

OCR error patterns

Vision occasionally misreads visually-similar glyphs. Watch for:

When the subagent reports an uncertain word, re-render at 300 DPI and trust the image.

Page chrome

Strip from every chapter:

• Page numbers (e.g. "12 (36)")
• Repeating headers (chapter title bars, magazine name)
• Repeating footers (URL, organization name)
• Logos and decorative elements
• Book/file identifiers (e.g. U6D777E0.book Page 39 Wednesday, April 7, 2010 8:33 AM)

Form feed characters (\f) prefix some page headers in pdftotext output and break naive regex anchored with ^. Not an issue for vision extraction directly, but if you ever fall back to text-based cleanup, run tr -d '\f' first.

Diagram embedding

In each chapter file, embed an inline diagram reference at the natural reading point:

![Short description of what the figure shows](../diagrammer/<filename>.png)

For diagrams that exist but the subagent's chapter doesn't naturally reference, list them in SKILL.md's diagram-overview table only.

For very small inline figures (single icons, single arrows), the subagent should describe in prose rather than insert a placeholder.

For useful figures the subagent identifies but that don't exist as PNGs yet, the subagent should insert a placeholder line:

*[Figure: <description>, see source.pdf page <N>]*

…and flag it in its final report. You then extract those PNGs in step 4 and either replace the placeholders or leave them as references.

When NOT to use vision

Use simpler tools when:

• The document is pure prose, single-column, no figures, no tables. Plain pdftotext (no flags) handles this in seconds.
• A single-page document. Manual transcription may be faster than spinning up a subagent.
• The document is already structured markdown or HTML. Convert directly without vision.

Even in these cases, the structure conventions (SKILL.md navigation, quick-lookup, faithfulness) still apply.

Multi-PDF skills

When a skill covers several related PDFs:

• Bundle all PDFs in the skill folder with descriptive slugs.
• Either keep separate chapter folders per source (chapters-manual/, chapters-policy/) or merge into one chapters/ with a source-prefixed naming scheme.
• SKILL.md navigation lists each PDF and its chapters/sections explicitly.
• Cross-reference between sources where they touch (e.g. policy mentions a procedure documented in manual).

Templates and references

• Vision subagent prompt template: templates/subagent-vision-prompt.md. Copy, fill in placeholders, and pass as the subagent prompt.
• SKILL.md skeleton: templates/skill-md-skeleton.md. Copy and adapt for the specific skill.

Notes on subagent usage

• Subagent has zero shared memory with the parent. Every prompt is self-contained: PDF path, page range, output path, available diagrams, faithfulness rules, expected report format.
• Use general-purpose as the subagent type. It has full tool access including Read (which handles PNGs as images) and Bash (for pdftoppm and cleanup).
• One subagent per chapter — not per page, not per document. Per-chapter scope keeps each task bounded (5-50 pages) and parallelizable.
• Send all subagent invocations in one message to run them in parallel. Sequential is much slower.
• Each subagent uses a unique /tmp/<prefix>- for its rendered PNGs to avoid collisions when running in parallel.
• Subagents clean up their own temp files as the last step.

Iteration

Skills mature through revision:

• First pass often surfaces things you didn't know about the document.
• Keep a backup of the first extraction during conversion (e.g. chapters-raw/) until you've validated quality, then delete.
• Re-run individual chapter subagents when you find quality issues — much cheaper than re-running everything.
• SKILL.md grows as you encounter more "common questions" that should be in the quick-lookup.