/deep-research
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.
$ npx -y skills add lingzhi227/agent-research-skills --skill deep-research --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/deep-research
Context preview
The summary Claude sees to decide when to auto-load this skill.
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.
SKILL.md
deep-research.SKILL.mdname: 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 <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.
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: true2. 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 disc
Read more
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 <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.
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: true2. 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 disc
31 skills for Claude Code covering the full academic research paper lifecycle — from literature search to slide generation — plus GitHub repository analysis for research topics. Extracted from 17 GitHub repos studying LLM-agent-driven research automation.
Other skills on agent-research-skills.
- /algorithm-design
Design algorithms with LaTeX pseudocode and UML diagrams. Generate algorithmic environments, Mermaid class/sequence diagrams, and ensure consistency between pseudocode and implementation. Use when formalizing methods for a paper.
Open skill - /atomic-decomposition
Decompose research ideas into atomic, self-contained concepts with bidirectional math-code mapping. For each concept, extract the math formula from papers and find code implementations. Use for complex system papers requiring formal grounding.
Open skill - /backward-traceability
Make every number in the final PDF traceable to the exact code line that produced it. Uses \hypertarget/\hyperlink LaTeX commands and \num{formula} evaluated at compile time. Use for reproducibility and data integrity verification.
Open skill - /citation-management
Manage BibTeX citations for LaTeX papers. Harvest missing citations from a draft using Semantic Scholar, validate cite keys against .bib files, deduplicate entries, and format bibliography. Use when working with references, BibTeX, or citations.
Open skill - /code-debugging
Debug experiment code with structured error analysis. Categorize errors, apply targeted fixes with retry logic, and use reflection to prevent recurring issues. Use when experiment code fails or produces incorrect results.
Open skill - /data-analysis
Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper.
Open skill

