--- name: deep-research description: Conduct systematic academic literature reviews in 6 phases, producing structured notes, a curated paper database, and a synthesized final report. Output is organized by phase for clarity. argument-hint: [topic] --- # Deep Research Skill ## Trigger Activate this skill when the user wants to: - "Research a topic", "literature review", "find papers about", "survey papers on" - "Deep dive into [topic]", "what's the state of the art in [topic]" - Uses `/research ` slash command ## Overview This skill conducts systematic academic literature reviews in 6 phases, producing structured notes, a curated paper database, and a synthesized final report. Output is organized **by phase** for clarity. **Installation**: `~/.claude/skills/deep-research/` — scripts, references, and this skill definition. **Output**: `.//Users/lingzhi/Code/deep-research-output/{slug}/` relative to the current working directory. ## CRITICAL: Strict Sequential Phase Execution **You MUST execute all 6 phases in strict order: 1 → 2 → 3 → 4 → 5 → 6. NEVER skip any phase.** This is the single most important rule of this skill. Violations include: - ❌ Jumping from Phase 2 to Phase 5/6 (skipping Deep Dive and Code) - ❌ Writing synthesis or report before completing Phase 3 deep reading - ❌ Producing a final report based only on abstracts/titles from search results - ❌ Combining or merging phases (e.g., doing "Phase 3-5 together") ### Phase Gate Protocol Before starting Phase N+1, you MUST verify that Phase N's **required output files** exist on disk. If they don't exist, you have NOT completed that phase. | Phase | Gate: Required Output Files | |-------|---------------------------| | 1 → 2 | `phase1_frontier/frontier.md` exists AND contains ≥10 papers | | 2 → 3 | `phase2_survey/survey.md` exists AND `paper_db.jsonl` has 35-80 papers | | 3 → 4 | `phase3_deep_dive/selection.md` AND `phase3_deep_dive/deep_dive.md` exist AND deep_dive.md contains detailed notes for ≥8 papers | | 4 → 5 | `phase4_code/code_repos.md` exists AND contains ≥3 repositories | | 5 → 6 | `phase5_synthesis/synthesis.md` AND `phase5_synthesis/gaps.md` exist | **After completing each phase, print a phase completion checkpoint:** ``` ✅ Phase N complete. Output: [list files written]. Proceeding to Phase N+1. ``` ### Why Every Phase Matters - **Phase 3 (Deep Dive)** is where you actually READ papers — without it, your synthesis is superficial and based only on abstracts - **Phase 4 (Code & Tools)** grounds the research in practical implementations — without it, you miss the open-source ecosystem - **Phase 5 (Synthesis)** requires deep knowledge from Phase 3 — you cannot synthesize papers you haven't read - **Phase 6 (Report)** assembles content from ALL prior phases — it should cite specific findings from Phase 3 notes ## Paper Quality Policy **Peer-reviewed conference papers take priority over arXiv preprints.** Many arXiv papers have not undergone peer review and may contain unverified claims. ### Source Priority (highest to lowest) 1. **Top AI conferences**: NeurIPS, ICLR, ICML, ACL, EMNLP, NAACL, AAAI, IJCAI, CVPR, KDD, CoRL 2. **Peer-reviewed journals**: JMLR, TACL, Nature, Science, etc. 3. **Workshop papers**: NeurIPS/ICML workshops (lower bar but still reviewed) 4. **arXiv preprints with high citations**: Likely high-quality but unverified 5. **Recent arXiv preprints**: Use cautiously, note "preprint" status explicitly ### When to Use arXiv Papers - As **supplementary** evidence alongside peer-reviewed work - For **very recent** results (< 3 months old) not yet at conferences - When a peer-reviewed version doesn't exist yet — note `(preprint)` in citations - For **survey/review** papers (these are useful even without peer review) ## Search Tools (by priority) ### 1. paper_finder (primary — conference papers only) **Location**: `/Users/lingzhi/Code/documents/tool/paper_finder/paper_finder.py` Searches ai-paper-finder.info (HuggingFace Space) for published conference papers. Supports filtering by conference + year. Outputs JSONL with BibTeX. ```bash python /Users/lingzhi/Code/documents/tool/paper_finder/paper_finder.py --mode scrape --config python /Users/lingzhi/Code/documents/tool/paper_finder/paper_finder.py --mode download --jsonl python /Users/lingzhi/Code/documents/tool/paper_finder/paper_finder.py --list-venues ``` Config example: ```yaml searches: - query: "long horizon reasoning agent" num_results: 100 venues: neurips: [2024, 2025] iclr: [2024, 2025, 2026] icml: [2024, 2025] output: root: /Users/lingzhi/Code/deep-research-output/{slug}/phase1_frontier/search_results overwrite: true ``` ### 2. search_semantic_scholar.py (supplementary — citation data + broader coverage) **Location**: `/Users/lingzhi/.claude/skills/deep-research/scripts/search_semantic_scholar.py` Supports `--peer-reviewed-only` and `--top-conferences` filters. API key: `/Users/lingzhi/Code/keys.md` (field `S2_API_Key`) ### 3. search_arxiv.py (supplementary — latest preprints) **Location**: `/Users/lingzhi/.claude/skills/deep-research/scripts/search_arxiv.py` For searching recent papers not yet published at conferences. Mark citations with `(preprint)`. ### Other Scripts | Script | Location | Key Flags | |--------|----------|-----------| | `download_papers.py` | `~/.claude/skills/deep-research/scripts/` | `--jsonl`, `--output-dir`, `--max-downloads`, `--sort-by-citations` | | `extract_pdf.py` | `~/.claude/skills/deep-research/scripts/` | `--pdf`, `--pdf-dir`, `--output-dir`, `--sections-only` | | `paper_db.py` | `~/.claude/skills/deep-research/scripts/` | subcommands: `merge`, `search`, `filter`, `tag`, `stats`, `add`, `export` | | `bibtex_manager.py` | `~/.claude/skills/deep-research/scripts/` | `--jsonl`, `--output`, `--keys-only` | | `compile_report.py` | `~/.claude/skills/deep-research/scripts/` | `--topic-dir` | ### WebFetch Mode (no Bash) 1. **Paper discovery**: `WebSearch` + `WebFetch` to query Semantic Scholar/arXiv APIs 2. **Paper reading**: `WebFetch` on ar5iv HTML or `Read` tool on downloaded PDFs 3. **Writing**: `Write` tool for JSONL, notes, report files ## 6-Phase Workflow ### Phase 1: Frontier Search the **latest** conference proceedings and preprints to understand current trends. 1. Write `phase1_frontier/paper_finder_config.yaml` targeting latest 1-2 years 2. Run paper_finder scrape 3. WebSearch for latest accepted paper lists 4. Identify trending directions, key breakthroughs → Output: `phase1_frontier/frontier.md`, `phase1_frontier/search_results/` ### Phase 2: Survey Build a comprehensive landscape with broader time range. Target **35-80 papers** after filtering. 1. Write `phase2_survey/paper_finder_config.yaml` covering 2023-2025 2. Run paper_finder + Semantic Scholar + arXiv 3. Merge all results: `python /Users/lingzhi/.claude/skills/deep-research/scripts/paper_db.py merge` 4. Filter to 35-80 most relevant: `python /Users/lingzhi/.claude/skills/deep-research/scripts/paper_db.py filter --min-score 0.80 --max-papers 70` 5. Cluster by theme, write survey notes → Output: `phase2_survey/survey.md`, `phase2_survey/search_results/`, `paper_db.jsonl` ### Phase 3: Deep Dive ⚠️ DO NOT SKIP **This phase is MANDATORY.** You must actually READ 8-15 full papers, not just their abstracts. 1. Select 8-15 papers from paper_db.jsonl with rationale → write `phase3_deep_dive/selection.md` 2. Download PDFs: `python download_papers.py --jsonl paper_db.jsonl --output-dir phase3_deep_dive/papers/ --sort-by-citations --max-downloads 15` 3. For EACH selected paper, read the full text (PDF via `Read` or HTML via `WebFetch` on ar5iv) 4. Write detailed structured notes per paper (see note-format.md template): problem, contributions, methodology, experiments, limitations, connections 5. Write ALL notes → `phase3_deep_dive/deep_dive.md` **Phase 3 Gate**: `deep_dive.md` must contain detailed notes for ≥8 papers, each with methodology and experiment sections filled in. Abstract-only summaries do NOT count. → Output: `phase3_deep_dive/selection.md`, `phase3_deep_dive/deep_dive.md`, `phase3_deep_dive/papers/` ### Phase 4: Code & Tools ⚠️ DO NOT SKIP **This phase is MANDATORY.** You must survey the open-source ecosystem. 1. Extract GitHub URLs from papers read in Phase 3 2. WebSearch for implementations: "site:github.com {method name}", "site:paperswithcode.com {topic}" 3. For each repo found: record URL, stars, language, last updated, documentation quality 4. Search for related benchmarks and datasets 5. Write → `phase4_code/code_repos.md` (must contain ≥3 repositories) **Phase 4 Gate**: `code_repos.md` must exist and contain at least 3 repositories with metadata. → Output: `phase4_code/code_repos.md` ### Phase 5: Synthesis (REQUIRES Phase 3 + 4 complete) Cross-paper analysis. **Weight peer-reviewed findings higher**. This phase MUST build on the detailed notes from Phase 3 and the code landscape from Phase 4. Taxonomy, comparative tables, gap analysis. **Before starting**: Verify `phase3_deep_dive/deep_dive.md` and `phase4_code/code_repos.md` exist. If not, go back and complete those phases first. → Output: `phase5_synthesis/synthesis.md`, `phase5_synthesis/gaps.md` ### Phase 6: Compilation (REQUIRES Phase 1-5 complete) Assemble final report from ALL prior phase outputs. Mark preprint citations with `(preprint)` suffix. **Before starting**: Verify ALL phase outputs exist: - `phase1_frontier/frontier.md` - `phase2_survey/survey.md` - `phase3_deep_dive/deep_dive.md` - `phase4_code/code_repos.md` - `phase5_synthesis/synthesis.md` + `gaps.md` If ANY are missing, go back and complete the missing phase(s) first. → Output: `phase6_report/report.md`, `phase6_report/references.bib` ## Output Directory ``` output/{topic-slug}/ ├── paper_db.jsonl # Master database (accumulated) ├── phase1_frontier/ │ ├── paper_finder_config.yaml │ ├── search_results/ │ └── frontier.md ├── phase2_survey/ │ ├── paper_finder_config.yaml │ ├── search_results/ │ └── survey.md ├── phase3_deep_dive/ │ ├── papers/ │ ├── selection.md │ └── deep_dive.md ├── phase4_code/ │ └── code_repos.md ├── phase5_synthesis/ │ ├── synthesis.md │ └── gaps.md └── phase6_report/ ├── report.md └── references.bib ``` ## Key Conventions - **Paper IDs**: Use `arxiv_id` when available, otherwise Semantic Scholar `paperId` - **Citations**: `[@key]` format, key = firstAuthorYearWord (e.g., `[@vaswani2017attention]`) - **JSONL schema**: title, authors, abstract, year, venue, venue_normalized, **peer_reviewed**, citationCount, paperId, arxiv_id, pdf_url, tags, source - **Preprint marking**: Always note `(preprint)` when citing non-peer-reviewed work - **Incremental saves**: Each phase writes to disk immediately - **Paper count**: Target 35-80 papers in final paper_db.jsonl (use `paper_db.py filter`) ## References - `/Users/lingzhi/.claude/skills/deep-research/references/workflow-phases.md` — Detailed 6-phase methodology - `/Users/lingzhi/.claude/skills/deep-research/references/note-format.md` — Note templates, BibTeX format, report structure - `/Users/lingzhi/.claude/skills/deep-research/references/api-reference.md` — arXiv, Semantic Scholar, ar5iv API guide ## Related Skills - Downstream: [literature-search](../literature-search/), [literature-review](../literature-review/), [citation-management](../citation-management/) - See also: [novelty-assessment](../novelty-assessment/), [survey-generation](../survey-generation/)