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Use when asked to review, read, or summarize a paper — especially with a URL, arXiv link, or paper title.\n---\n\n# Paper Review Skill\n\nAutonomous paper review: fetch → summarize → find repo → read code → map to paper → export to Notion.\n\n## Config\n\n- **Notion Papers DB**: Auto-discovered by searching for a database named \"Papers\"\n- **Repos directory**: `repos/` (workspace-relative)\n- **Notion API key**: `~/.config/notion/api_key` or `NOTION_API_KEY` env\n\n## Workflow\n\n### Phase 1: Fetch Paper\n\n```bash\npython3 scripts/fetch_paper.py \"<url_or_arxiv_id>\" -o /tmp/paper.json\n```\n\nOutput: JSON with `title`, `authors`, `abstract`, `year`, `url`, `pdf_url`, `text`.\n\nIf text extraction fails or is poor quality, use `web_fetch` on the arXiv HTML version: `https://arxiv.org/html/<id>`.\n\n### Phase 2: Summarize Paper\n\nRead the extracted text. Identify:\n- **TL;DR**: 1-2 sentences\n- **Problem**: What gap does this address?\n- **Method**: Core approach + key innovations\n- **Key equations**: The essential math (will become LaTeX blocks)\n- **Architecture**: System components and how they interact\n- **Results**: Main benchmarks and numbers\n- **Limitations**: Acknowledged or observed\n\n### Phase 3: Find & Clone Repo\n\n```bash\npython3 scripts/find_repo.py --title \"<title>\" --authors \"<authors>\" --text-file /tmp/paper_text.txt\n```\n\nPick the best repo: prefer repos linked in the paper text (`source: paper_text`), then highest-star GitHub search result whose description matches.\n\nIf a repo is found:\n```bash\ncd repos/ && git clone <repo_url>\n```\n\nIf no repo found, skip to Phase 5.\n\n### Phase 4: Read Code & Map to Paper\n\nThis is the high-value phase. Follow this approach:\n\n1. **Read README** — find the recommended example / quickstart\n2. **Pick ONE example** — typically from `examples/` or mentioned in README\n3. **Trace the entry point** — find the main training/inference loop\n4. **Identify core components** by following the data flow:\n   - What goes in? (data loading)\n   - What generates? (inference/rollout)\n   - What computes loss? (training objective)\n   - What optimizes? (optimizer, scheduler)\n   - What coordinates? (controller, orchestrator)\n5. **For each component**, note:\n   - File path + class/function name\n   - What it does (1-2 sentences)\n   - Key code snippet or pseudocode\n   - Which paper section/equation it implements\n\n**Code reading strategy**: Start from the example, trace into the main loop, then go one level deeper into each function called by the loop. Don't read every file — follow the data flow.\n\n### Phase 5: Build Notion Page\n\nStructure the page body as Notion blocks following this template:\n\n```\n1. TL;DR                          — paragraph\n2. Main Loop                      — code block (from example entry point)\n3. Data Flow                      — code block (ASCII diagram)\n4. Core Components                — for each:\n   4.x Component Name             — heading_2\n     What:                         — paragraph\n     Code:                         — paragraph (file:line, class/fn names in code annotations)\n     [code block]                  — code block (key snippet or pseudocode)\n     Paper:                        — paragraph (section ref)\n     [equation]                    — equation block (LaTeX, if applicable)\n5. Code Structure                 — code block (tree view)\n6. Results                        — bullets\n7. Notes & Relevance              — paragraphs + bullets\n```\n\nIf no repo was found, skip sections 2-5 and use:\n\n```\n1. TL;DR\n2. Problem & Motivation\n3. Method\n4. Key Equations                  — equation blocks (LaTeX)\n5. Results\n6. Limitations\n7. Notes & Relevance\n```\n\n#### Notion Block Types Reference\n\n```python\n# Heading\n{\"type\": \"heading_1\", \"heading_1\": {\"rich_text\": [{\"text\": {\"content\": \"...\"}}]}}\n\n# Paragraph with mixed formatting\n{\"type\": \"paragraph\", \"paragraph\": {\"rich_text\": [\n    {\"text\": {\"content\": \"bold \"}, \"annotations\": {\"bold\": True}},\n    {\"text\": {\"content\": \"code_ref\"}, \"annotations\": {\"code\": True}},\n    {\"text\": {\"content\": \" normal text\"}}\n]}}\n\n# Code block\n{\"type\": \"code\", \"code\": {\"rich_text\": [{\"text\": {\"content\": \"...\"}}], \"language\": \"python\"}}\n\n# LaTeX equation (display block)\n{\"type\": \"equation\", \"equation\": {\"expression\": \"J(\\\\theta) = ...\"}}\n\n# Bullet\n{\"type\": \"bulleted_list_item\", \"bulleted_list_item\": {\"rich_text\": [{\"text\": {\"content\": \"...\"}}]}}\n```\n\n#### Export\n\nWrite properties to `/tmp/paper_props.json`:\n```json\n{\"Name\": \"...\", \"Authors\": \"...\", \"Year\": 2025, \"Tags\": [\"RL\"], \"Status\": \"Summarized\", \"URL\": \"...\", \"GitHub\": \"https://github.com/...\", \"Summary\": \"one-line TL;DR\"}\n```\n\nWrite blocks array to `/tmp/paper_blocks.json`.\n\n```bash\npython3 scripts/notion_export.py \\\n    --properties /tmp/paper_props.json \\\n    --blocks /tmp/paper_blocks.json\n```\n\nTo update an existing page:\n```bash\npython3 scripts/notion_export.py \\\n    --properties /tmp/paper_props.json \\\n    --blocks /tmp/paper_blocks.json \\\n    --update <page_id>\n```\n\n## Tags (use from this set, or add new ones)\n\nRL, LLM, Agents, Safety, Training, Inference, Architecture, Alignment, Reasoning, Vision, Multimodal, Diffusion, Efficiency, Data, Evaluation, Memory\n\n## Critical Rules\n\n- **NEVER fabricate URLs or arXiv IDs.** Always use the URL from Phase 1 (`fetch_paper.py` output) for the Notion properties. 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