deep-research

What this skill does

# 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 <topic>` 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 <config.yaml>
python /Users/lingzhi/Code/documents/tool/paper_finder/paper_finder.py --mode download --jsonl <results.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 MANDA