[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

AI Mistake Prevention — Failure modes to avoid on every task:

• Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal.

• Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing.

• Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain.

• Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path.

• When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site.

• Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code.

• Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks.

• Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis.

• Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly.

• Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Quick Summary

Goal: Convert PDF files to well-formatted Markdown with auto-detection of native text vs scanned documents.

Workflow:

• Auto-Detect — Determine if PDF has native text or needs OCR

• Convert — Run scripts/convert.cjs with input path and optional mode/output flags

• Output — Returns JSON with success status, page count, and output path

Key Rules:

• Use --mode auto (default) to let the tool decide native vs OCR

• OCR for scanned PDFs requires additional tesseract.js setup

• Complex multi-column layouts may not preserve structure perfectly

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

pdf-to-markdown

Convert PDF files to Markdown format with automatic detection of native text vs scanned documents.

Installation Required

This skill requires npm dependencies. Run one of the following:

Option 1: Install via ClaudeKit CLI (recommended)

ck init # Runs install.sh which handles all skills

Option 2: Manual installation

cd .claude/skills/pdf-to-markdown npm install

Dependencies: @opendocsg/pdf2md (native PDFs), pdfjs-dist (PDF parsing)

Note: OCR for scanned PDFs requires additional setup (see OCR section).

Quick Start

Basic conversion (auto-detect native vs scanned)

node .claude/skills/pdf-to-markdown/scripts/convert.cjs --input ./document.pdf

Specify output path

node .claude/skills/pdf-to-markdown/scripts/convert.cjs -i ./doc.pdf -o ./output.md

Force native mode (skip OCR detection)

node .claude/skills/pdf-to-markdown/scripts/convert.cjs -i ./doc.pdf --mode native

CLI Options

Option Short Description Default

--input

-i

Input PDF file path (required)

--output

-o

Output markdown file path {input}.md

--mode

-m

Conversion mode: auto , native , ocr

auto

--help

-h

Show help message

Features

• Auto-Detection: Automatically determines if PDF has native text or requires OCR

• Native PDFs: Fast extraction using @opendocsg/pdf2md

• Tables: Basic table structure preservation

• Cross-Platform: Works on Windows, macOS, Linux

• No System Dependencies: Pure JavaScript implementation

Conversion Modes

Auto (Default)

Checks if PDF has extractable text on first page. Uses native extraction if text found, otherwise falls back to OCR warning.

Native

Fast direct text extraction. Best for PDFs with selectable text (not scanned images).

OCR (Scanned PDFs) - Coming Soon

For scanned documents. Currently not implemented - the skill will notify you if a PDF appears to be scanned.

Output

Returns JSON on success:

{ "success": true, "input": "/path/to/input.pdf", "output": "/path/to/output.md", "stats": { "pages": 5, "mode": "native" } }

Limitations

• Complex multi-column layouts may not preserve structure

• Scanned PDF OCR accuracy depends on image quality

• Mathematical formulas may not convert perfectly

• First-run OCR downloads language data (~15MB)

OCR Setup (Optional)

For scanned PDF support, install additional dependencies:

npm install tesseract.js pdfjs-dist canvas

Note: The canvas package may require build tools on some systems.

Closing Reminders

• IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting

• IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code

• IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act)

• IMPORTANT MUST ATTENTION add a final review todo task to verify work quality

• MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.

• MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.