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)

Top AI conferences: NeurIPS, ICLR, ICML, ACL, EMNLP, NAACL, AAAI, IJCAI, CVPR, KDD, CoRL
Peer-reviewed journals: JMLR, TACL, Nature, Science, etc.
Workshop papers: NeurIPS/ICML workshops (lower bar but still reviewed)
arXiv preprints with high citations: Likely high-quality but unverified
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.

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:

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)

Paper discovery: WebSearch + WebFetch to query Semantic Scholar/arXiv APIs
Paper reading: WebFetch on ar5iv HTML or Read tool on downloaded PDFs
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.

Write phase1_frontier/paper_finder_config.yaml targeting latest 1-2 years
Run paper_finder scrape
WebSearch for latest accepted paper lists
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.

Write phase2_survey/paper_finder_config.yaml covering 2023-2025
Run paper_finder + Semantic Scholar + arXiv
Merge all results: python /Users/lingzhi/.claude/skills/deep-research/scripts/paper_db.py merge
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
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.

Select 8-15 papers from paper_db.jsonl with rationale → write phase3_deep_dive/selection.md
Download PDFs: python download_papers.py --jsonl paper_db.jsonl --output-dir phase3_deep_dive/papers/ --sort-by-citations --max-downloads 15
For EACH selected paper, read the full text (PDF via Read or HTML via WebFetch on ar5iv)
Write detailed structured notes per paper (see note-format.md template): problem, contributions, methodology, experiments, limitations, connections
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.

Extract GitHub URLs from papers read in Phase 3
WebSearch for implementations: "site:github.com {method name}", "site:paperswithcode.com {topic}"
For each repo found: record URL, stars, language, last updated, documentation quality
Search for related benchmarks and datasets
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-review, citation-management
See also: novelty-assessment, survey-generation