pdf-processing
Read a PDF directly with vision and extract text, summarize, or analyze its structure. Use when the user passes a PDF file.
git clone --depth 1 https://github.com/skrun-dev/skrun /tmp/pdf-processing && cp -r /tmp/pdf-processing/agents/pdf-processing ~/.claude/skills/pdf-processingSKILL.md
# PDF Processing You are a PDF processing assistant. The user passes you a PDF file and a `task`. You read the PDF directly using your native document capability — no extraction tools, no upstream OCR. ## Instructions 1. Read the input PDF carefully (you receive it as a document part in the conversation). 2. Look at the `task` field: - `extract` → return the readable text content of the PDF, preserving paragraph and section structure as best you can. - `summarize` → return a single concise paragraph (3-5 sentences) covering the document's purpose and main points. - `analyze` → return a short structural analysis: list the key topics, sections, and any tables/figures detected. 3. Estimate the number of pages and return it as `pages`. ## Output Return a JSON object with: - `result`: the string for the requested task (extracted text, summary, or analysis). - `pages`: integer number of pages. ## Notes - If the PDF is unreadable or empty, return `result: "Could not read PDF"` and `pages: 0`. - Don't fabricate content — if a section is illegible, say so in the result.
Generate a numbered Architecture Decision Record (ADR) following the standard nygard/MADR convention. Reads the target ADR directory to compute the next number and to surface candidates for cross-linking. Use when asked to document an architectural decision, draft an ADR, or capture a technical choice with its rationale.
Generate a polished CHANGELOG.md and release-notes.md from a local git repository (or a captured `.git-log.txt` dump). Groups commits by Conventional Commit type, writes both artifacts to the run output directory. Use when asked to draft release notes, summarize commits between tags, or produce a human-readable changelog.
Review code for quality, bugs, security issues, and suggest improvements. Use when asked to review, audit, or improve code.
Turn a CSV of operational data (sales, usage, signups, support tickets) into a multi-page styled PDF executive report with narrative + matplotlib charts. The LLM analyzes the data, picks what's interesting, writes the prose, and emits a structured render request that becomes a polished PDF. Use when given a CSV and asked for a report, summary, or analysis.
Analyze structured data (CSV/JSON), find patterns, generate insights, and suggest visualizations. Use for data analysis tasks.
Draft professional emails based on context, tone, and recipient. Use for composing business emails.
Turn a folder of Markdown notes (Obsidian vault, Notion export, plain repo docs) into a navigable static HTML knowledge base bundled as a single .zip file. Maintains a persistent concept graph across runs — concepts that appear in multiple runs gain prominence, and the index becomes denser over time. Use when given a Markdown vault and asked to publish, share, or render it as a browsable site.
Listen to a meeting recording and extract structured action items, decisions, and open questions. Maintains a persistent ledger across runs — previously-open actions are auto-resolved when mentioned as done in subsequent meetings. Outputs `actions.csv` (importable to Linear/Asana/Notion) + `recap.md` (paste into Slack). Use when given a meeting recording and asked for a recap or action items.