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. USE when you need a peer-reviewed, source-grounded literature review — not general web research.
Scanned 9/2/2026
Install to Claude Code
npx -y skills add sergeeey/Claude-cod-top-2026 --skill academic-research --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: academic-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. USE when you need a peer-reviewed, source-grounded literature review — not general web research.
Triggers: /academic-research, систематический обзор литературы, academic literature review, research pipeline, обзор научных статей, найди академические источники.
argument-hint: [topic]
---
# Academic 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**: `skills/extensions/academic-research/` — references and this skill definition (the Bash-mode scripts referenced below are an external dependency, not shipped by this repo — see "Search Tools" below).
**Output**: `output/{topic-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)
### Bash mode requires external scripts — not shipped by this repo
The 6 scripts below (`paper_finder.py`, `search_semantic_scholar.py`,
`search_arxiv.py`, `download_papers.py`, `extract_pdf.py`, `paper_db.py`,
`bibtex_manager.py`, `compile_report.py`) are a companion automation toolkit
this SKILL.md was originally written against. **They are not part of this
repository and no path in `~/.claude/` will resolve them by default.** If you
have installed an equivalent toolkit separately, the flags below still apply.
Otherwise, skip straight to **WebFetch Mode** — it covers every search these
scripts perform, just with a manual step per query instead of one script call.
### 1. paper_finder (primary — conference papers only, external dependency)
Searches ai-paper-finder.info (HuggingFace Space) for published conference papers. Supports filtering by conference + year. Outputs JSONL with BibTeX.
```bash
python paper_finder.py --mode scrape --config <config.yaml>
python paper_finder.py --mode download --jsonl <results.jsonl>
python 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: output/{topic-slug}/phase1_frontier/search_results
overwrite: true
```
### 2. search_semantic_scholar.py (supplementary — citation data + broader coverage, external dependency)
Supports `--peer-reviewed-only` and `--top-conferences` filters. API key: your own keys file (field `S2_API_Key`)
### 3. search_arxiv.py (supplementary — latest preprints, external dependency)
For searching recent papers not yet published at conferences. Mark citations with `(preprint)`.
### Other Scripts (all external dependencies — not shipped by this repo)
| Script | Key Flags |
|--------|-----------|
| `download_papers.py` | `--jsonl`, `--output-dir`, `--max-downloads`, `--sort-by-citations` |
| `extract_pdf.py` | `--pdf`, `--pdf-dir`, `--output-dir`, `--sections-only` |
| `paper_db.py` | subcommands: `merge`, `search`, `filter`, `tag`, `stats`, `add`, `export` |
| `bibtex_manager.py` | `--jsonl`, `--output`, `--keys-only` |
| `compile_report.py` | `--topic-dir` |
### WebFetch Mode (no Bash, works with no external scripts)
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 (external dependency — see "Search Tools" above): `python paper_db.py merge`
4. Filter to 35-80 most relevant (same caveat): `python 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
- `references/workflow-phases.md` — Detailed 6-phase methodology
- `references/note-format.md` — Note templates, BibTeX format, report structure
- `references/api-reference.md` — arXiv, Semantic Scholar, ar5iv API guide
## Related Skills
- Downstream: [literature-review](../literature-review/), [citation-management](../citation-management/)
- See also: [novelty-assessment](../novelty-assessment/), [survey-generation](../survey-generation/)
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