literature-review
Skill340 starsChanged 7 months agoMITView on GitHub
Systematic literature review workflow: scope, search (arXiv, Semantic Scholar, Google Scholar), screen, extract, synthesize, and identify gaps. Triggers on: "literature review", "survey the literature", "related work", "systematic review", "synthesize the research", "find papers about", "research gap analysis".
We split this skill into the rules it gives an agent
6 rules. 0 are common, 4 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 59 edited versions we read keep these.
4
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 31
Before searching, establish the review boundaries:
- Distinctive: line 74
Use citation count as a signal for influence, not quality
- Distinctive: line 90
When uncertain, include. It is cheaper to drop a paper later than to miss it.
- Very specific: line 119
If the paper is available via pdf_url, read it for extraction.
- Very specific: line 172
Default to Author et al. (Year) in-text with full references at the end.
- Distinctive: line 176
Before delivering the review, verify:
“Common” is measured: kept by at least 6 in 10 of the 59 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Literature Review
Systematic discovery, extraction, and synthesis of academic research on a defined topic.
When to use this skill vs. others
| Need | Skill |
| Survey a research area, synthesize multiple papers | literature-review (this skill) |
| Critique a single paper's methodology and claims | research-critique |
| Audit a manuscript's formatting, structure, citations | manuscript-review |
| Verify a manuscript's numbers trace to code | manuscript-provenance |
| Search arXiv for papers matching a query | arxiv-search (utility) |
Workflow
Phase 1: Scope Definition
Before searching, establish the review boundaries:
- Research question — What specific question does the review answer? Vague topics produce vague reviews. "What techniques exist for X" is weaker than "How do methods for X compare on metric Y across domains Z?"
- Inclusion criteria — Define what counts:
- Date range (e.g., 2020–present)
- Publication type (peer-reviewed, preprints, both)
- Domains/categories (e.g., cs.CL, cs.AI)
- Minimum relevance threshold
- Exclusion criteria — Define what does not count:
- Tangentially related work
- Non-primary sources (blog posts, tutorials) unless explicitly included
- Duplicate or superseded versions
- Expected output — What form should the review take? Narrative synthesis, tabular comparison, gap analysis, annotated bibliography, or related-work section?
Present the scope to the user for confirmation before proceeding.
Phase 2: Search & Discovery
Execute searches across available sources. Use multiple queries with varying specificity to avoid single-query blind spots.
Primary source: arXiv (via arxiv-search utility)
uv run --with arxiv python scripts/arxiv_search.py "QUERY" --max-results 30 --sort-by relevanceVary queries systematically:
- Broad topic query:
"retrieval augmented generation" - Field-scoped query:
ti:retrieval AND abs:generation AND cat:cs.CL - Author-anchored query:
au:lewis AND abs:retrieval(when key authors are known) - Recency query: same terms with
--sort-by submitted
Secondary sources (via web search/fetch):
- Semantic Scholar API:
https://api.semanticscholar.org/graph/v1/paper/search?query=QUERY&limit=20&fields=title,authors,abstract,year,citationCount,externalIds - Google Scholar (via web search):
site:scholar.google.com QUERY - Connected Papers (for citation graph exploration):
https://www.connectedpapers.com/search?q=QUERY
Snowball strategy:
- Forward snowball: find papers that cite a key paper (Semantic Scholar citations endpoint)
- Backward snowball: follow the references of key papers
- Use citation count as a signal for influence, not quality
Phase 3: Screening & Filtering
For each discovered paper, apply the inclusion/exclusion criteria from Phase 1.
Produce a screening table:
| # | ID | Title | Authors | Year | Relevant? | Reason |
| 1 | 2301.07041 | Paper Title | Author et al. | 2023 | Yes | Directly addresses RQ |
| 2 | 2302.12345 | Other Paper | Author B | 2023 | No | Tangential — focuses on X not Y |
Rules:
- Screen on title + abstract first. Read full paper only for borderline cases.
- When uncertain, include. It is cheaper to drop a paper later than to miss it.
- Track exclusion reasons — they inform the review's limitations section.
- Flag papers that appear in multiple search queries as likely high-relevance.
Phase 4: Data Extraction
For each included paper, extract a structured record:
- id: "2301.07041"
title: "Paper Title"
authors: ["Author One", "Author Two"]
year: 2023
venue: "NeurIPS 2023"
research_question: "How does X affect Y?"
methodology: "Controlled experiment with N=1000"
key_findings:
- "Finding 1 with quantitative result"
- "Finding 2 with effect size"
limitations: "Single-domain evaluation"
relevance_to_rq: "Directly compares methods A and B on metric Y"
citation_count: 142Extraction discipline:
- Record what the paper demonstrates, not what it claims to demonstrate.
- Distinguish empirical findings (data-backed) from interpretive claims (author's framing).
- Note methodology details that enable cross-paper comparison (datasets, metrics, baselines).
- If the paper is available via
pdf_url, read it for extraction. Do not extract from abstracts alone for included papers.
Phase 5: Synthesis
Transform extracted records into structured analysis. The synthesis method depends on the output format requested in Phase 1.
Thematic synthesis — Group papers by theme, approach, or finding:
- Identify recurring themes across papers
- Note where papers agree, disagree, or address different aspects
- Highlight methodological trends (what approaches are gaining/losing traction)
Comparative synthesis — Build comparison tables:
| Method | Paper(s) | Dataset | Metric | Result | Limitations |
| Method A | [1], [3] | D1 | F1 | 0.85 | Domain-specific |
| Method B | [2], [4] | D1, D2 | F1 | 0.82 | Requires X |
Chronological synthesis — Map the evolution of the field:
- What was the state of knowledge at time T?
- What shifted and why?
- Where is the field heading?
Gap analysis — Identify what is missing:
- Questions raised but not answered by existing work
- Methodological gaps (no one has tried approach X on problem Y)
- Domain gaps (studied in domain A but not B)
- Contradictions between studies that remain unresolved
Phase 6: Output
Produce the review document in the format specified in Phase 1.
Standard structure for a narrative review:
- Introduction — Research question, scope, and motivation for the review
- Search methodology — Databases searched, queries used, inclusion/exclusion criteria, screening results (N found → N screened → N included)
- Findings — Thematic or chronological synthesis of included papers
- Discussion — Cross-cutting analysis, trends, contradictions, gaps
- Limitations of this review — Search scope restrictions, potential biases, papers not accessible
- References — Full citation list for all included papers
Standard structure for a tabular review:
- Summary table (all included papers with key metadata)
- Comparison matrix (methods × metrics × results)
- Gap analysis table (questions × coverage)
- Reference list
Citation format:
Default to Author et al. (Year) in-text with full references at the end. Adapt to the user's specified format (APA, Chicago, IEEE) if requested.
Quality Checks
Before delivering the review, verify:
- Every included paper has a structured extraction record
- Every claim in the synthesis is traceable to at least one extracted finding
- The gap analysis identifies at least one concrete research opportunity
- The review acknowledges its own limitations (search scope, access, biases)
- Citation format is consistent throughout
- No paper is cited that was excluded during screening
- Contradictions between papers are noted, not silently resolved by picking a side
Edge Cases
| Situation | Adaptation |
| Very few papers found (<5) | The field may be nascent. Note this explicitly. Broaden search terms or check if the topic goes by different terminology. Consider adjacent fields. |
| Too many papers found (>100) | Tighten inclusion criteria. Consider limiting to top venues, recent years, or high-citation papers. Produce a scoping review rather than exhaustive review. |
| User provides a paper list instead of a topic | Skip Phase 2 (Search). Start from Phase 3 (Screening) with the provided list. |
| User wants a related-work section for their own paper | Tailor synthesis to position the user's contribution. Organize by approaches the user's work builds on, alternatives it competes with, and gaps it fills. |
| No full-text access to key papers | Extract from abstracts and note the limitation. Do not fabricate methodology details. Flag which papers were abstract-only in the extraction records. |
| Interdisciplinary topic | Search across multiple category prefixes. Note when different fields use different terminology for the same concept. |
| User asks for a "quick" literature review | Reduce Phase 2 to a single search query, Phase 3 to title-only screening, Phase 4 to abstract-only extraction. Label the output as a preliminary survey, not a systematic review. |
Other versions76 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 76
- advaitpaliwal/feynman★ 10ksame name
- companion-inc/feynman★ 10ksame name
- getcompanion-ai/feynman★ 10ksame name
- Norman-bury/research-writing-skill★ 3.4ksame name
- Norman-bury/articlewriting-skill★ 3.4ksame name
- K-Dense-AI/claude-scientific-writer★ 2.4ksame name
- xuzhougeng/wisp-science★ 1.1ksame name
- wu-yc/LabClaw★ 1.1ksame name
- LeonChaoX/qinyan-academic-skills★ 910same name
- ZimoLiao/scholaraio★ 585same name
66 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
Used alongsideProjects with it also use pdf-explore, figure-style and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
JimLiuSkillpdf-explore
Use this skill when the user has attached a PDF, paper, report, or other document and the answer needs content from more than one place in it: summarize the methods or any other section, compare sections, find where a topic is discussed, read a value or label off a figure or chart, or find/list/extract every instance of something across the whole document (datasets, benchmarks, citations, figures, table rows, accession numbers — including appendices). Skip it only for a single lookup of 1–4 pages quoted in your very next response — `read_file(pages=[...])` attaches pages as images that are dropped from context after one turn, so multi-section answers end up re-reading the same ranges repeatedly. Parses the PDF once in the Python kernel: `pdf_pages` (pages as persistent text), `pdf_outline` (TOC), `pdf_scan` (rank pages by relevance), `pdf_map`/`pdf_extract` (per-page summary / structured fields via parallel haiku calls). For PDF creation/manipulation, use reportlab/pypdf directly.
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Publication-grade figure correctness and legibility rules for final-deliverable figures — not every plot. Quick look or iterating on the analysis (EDA scatters, sanity-check histograms)? Plot plainly without this skill. Producing a figure that ships — report, paper, export, or kept artifact — load this skill first and call `apply_figure_style()` — sets a role-mapped font-size ladder, outward ticks, frameless legends, and 300-dpi output. The skill is a checklist, not a house look: data fidelity (claim-titles tested against every row, excluded data never enters summaries), label economy (floor and ceiling), color threading, chart-choice-by-data-shape, layout, and a render-then-verify QA loop (bbox collision + per-panel perceptual check). Ships helpers: focal_palette, bar_with_points, strip_with_median, end_of_line_labels, panel_letter, set_frame, panel_crops. For multi-panel figures load `figure-composer`; for whole-paper figure arc load `paper-narrative`.
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lingzhi227Skilllatex-formatting
Handle LaTeX formatting, templates, and styling for academic papers. Set up conference templates (ICML, ICLR, NeurIPS, AAAI, ACL), fix formatting issues, manage packages, and ensure venue-specific compliance. Use when the user needs to set up a paper template, fix LaTeX formatting, or prepare for submission.
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garri333Skillresearch-lookup
Look up current research information using Perplexity's Sonar Pro Search or Sonar Reasoning Pro models through OpenRouter. Automatically selects the best model based on query complexity. Search academic papers, recent studies, technical documentation, and general research information with citations.
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