literature-review – skill Lập trình cho agent AI
Thực hiện tổng quan tài liệu có hệ thống, phạm vi và tường thuật bằng PubMed, arXiv, bioRxiv, Semantic Scholar và các nguồn phù hợp khác. Dùng để tổng hợp nghiên cứu, tìm kiếm tài liệu có thể tái lập, sàng lọc, kiểm tra trích dẫn, hoặc chuẩn bị bản review Markdown và PDF. Theo dõi độ phủ tìm kiếm, số bản ghi so với nghiên cứu,
Cài skill literature-review bằng một lệnh
Chọn công cụ bạn đang dùng, copy lệnh và dán vào terminal. Skill sẽ nằm trong thư mục skills của công cụ đó và sẵn sàng dùng ở phiên làm việc tiếp theo.
Claude Code
mkdir -p ~/.claude/skills && curl -fsSL https://skill.dn1nguoi.com/z/literature-review-a6315.tgz | tar xz -C ~/.claude/skills
Gọi trong Claude Code: /literature-review – rồi mô tả công việc bạn muốn làm.
Codex
mkdir -p ~/.codex/skills && curl -fsSL https://skill.dn1nguoi.com/z/literature-review-a6315.tgz | tar xz -C ~/.codex/skills
Gọi trong Codex: $literature-review – rồi mô tả công việc bạn muốn làm.
Hermes Agent
mkdir -p ~/.hermes/skills && curl -fsSL https://skill.dn1nguoi.com/z/literature-review-a6315.tgz | tar xz -C ~/.hermes/skills
Gọi trong Hermes Agent: /literature-review – rồi mô tả công việc bạn muốn làm.
DeepSeek
mkdir -p ~/.deepcode/skills && curl -fsSL https://skill.dn1nguoi.com/z/literature-review-a6315.tgz | tar xz -C ~/.deepcode/skills
Gọi trong DeepSeek: /literature-review – rồi mô tả công việc bạn muốn làm.
Nội dung SKILL.md
Nội dung SKILL.md gốc bằng tiếng Anh.
Literature Review
Overview
Conduct systematic, comprehensive literature reviews following rigorous academic methodology. Search multiple literature databases, synthesize findings thematically, verify all citations for accuracy, and generate professional output documents in markdown and PDF formats.
Use discipline-appropriate bibliographic databases for the reproducible search, with parallel-web (parallel-cli search) for scoping and supplementary discovery. Ranked web results and extracted excerpts cannot establish exhaustive coverage or substitute for full-text assessment. Bundled scripts process normalized records, check DOI registration, and render documents; they do not run a complete systematic review automatically.
When to Use This Skill
Use this skill when:
- Conducting a systematic literature review for research or publication
- Synthesizing current knowledge on a specific topic across multiple sources
- Performing meta-analysis or scoping reviews
- Writing the literature review section of a research paper or thesis
- Investigating the state of the art in a research domain
- Identifying research gaps and future directions
- Requiring verified citations and professional formatting
Figures and PRISMA reporting
For systematic reviews, use the appropriate [PRISMA 2020 flow template](https://www.prisma-statement.org/prisma-2020-flow-diagram) and reconciled screening counts. Distinguish records, reports, and studies, including reports not retrieved and reasons for full-text exclusions. Use tables or deterministic plotting for exact numbers, effect estimates, and risk-of-bias results.
Conceptual schematics are optional. The bundled generator needs OPENROUTER_API_KEY and creates a PNG draft with up to two generation/review iterations:
python scripts/generate_schematic.py "Conceptual evidence map; labels from the reviewed extraction table" \
-o figures/evidence_map.png --doc-type journal --iterations 2Generation uses OpenRouter's POST /api/v1/images with
google/gemini-3.1-flash-image; review uses POST /api/v1/chat/completions with
google/gemini-3.7-flash. The output path must end in .png. Inspect labels,
counts, arrows, and accessibility yourself: success means an image was saved,
whereas quality_met, final_reviewed, and termination_reason in the review
log describe the automated review. Neither proves scientific accuracy. A failed
refinement preserves the previous draft. Check current model availability before
running paid generation; do not fabricate study counts in an image prompt.
Core Workflow
A literature review runs in seven phases, documented in full with commands and templates
in [references/core_workflow.md](references/core_workflow.md):
1. Planning and scoping — the question, inclusion and exclusion criteria, and scope.
2. Systematic literature search — multi-database searching with recorded queries.
3. Screening and selection — title/abstract then full-text screening with counts kept
for the PRISMA flow.
4. Data extraction and quality assessment — structured extraction and risk-of-bias
or quality appraisal.
5. Synthesis and analysis — thematic or quantitative synthesis across studies.
6. Citation verification — every citation checked against the actual source.
7. Document generation — assembling the review with a complete bibliography.
Record every search string and date as you go: a review that cannot reproduce its own
search is not systematic. Per-database search guidance and citation styles are in
[references/search_and_citation.md](references/search_and_citation.md), and a full worked
review is in [references/example_workflow.md](references/example_workflow.md).
Best Practices
Search Strategy
1. Pilot the question: Optionally use parallel-cli search for scoping, then test database queries against known eligible reports
2. Choose complementary sources: Justify database and registry coverage for the question; no fixed number of databases guarantees completeness
3. Include preprint servers: Captures latest unpublished findings
4. Document everything: Search strings, dates, result counts for reproducibility — save all parallel-cli output to sources/
5. Test and refine: Run pilot searches, review results, adjust search terms
6. Preserve all eligible records: Citation counts can order exploratory reading but must not determine eligibility or replace risk-of-bias assessment
7. Verify retrieved content: Extraction can be incomplete; obtain the actual full report, or record it as not retrieved
Records, Reports, and Studies
1. Deduplicate records, then link reports: DOI/title deduplication removes repeated search hits; it does not identify every paper from the same study. Link preprints, journal articles, protocols, and follow-up reports using trial IDs, cohort descriptions, sites, and recruitment dates.
2. Keep a study-to-report map: Preserve each source and explain which report supplies each outcome; do not count overlapping participants twice in a meta-analysis.
3. Reconcile PRISMA counts: Track records screened, reports sought/not retrieved/assessed, reports excluded with reasons, and included studies separately. Report counts can exceed study counts. See the [Cochrane selection guidance](https://www.cochrane.org/authors/handbooks-and-manuals/handbook/current/chapter-04).
Screening and Selection
1. Use clear criteria: Document inclusion/exclusion criteria before screening
2. Screen systematically: Title → Abstract → Full text
3. Document exclusions: Record reasons for excluding studies
4. Use independent eligibility decisions: For systematic reviews, use two reviewers for final full-text eligibility and document disagreement resolution; disclose any single-reviewer limitation
Synthesis
1. Organize thematically: Group by themes, NOT by individual studies
2. Synthesize across studies: Compare, contrast, identify patterns
3. Be critical: Evaluate quality and consistency of evidence
4. Identify gaps: Note what's missing or understudied
Quality and Reproducibility
1. Assess study quality: Use appropriate quality assessment tools
2. Verify citations and claims: Run verify_citations.py for DOI registration/metadata, then compare the reference and the cited claim with the actual source
3. Document methodology: Provide enough detail for others to reproduce
4. Follow guidelines: Use PRISMA for systematic reviews
Writing
1. Be objective: Present evidence fairly, acknowledge limitations
2. Be systematic: Follow structured template
3. Be specific: Include numbers, statistics, effect sizes where available
4. Be clear: Use clear headings, logical flow, thematic organization
Common Pitfalls to Avoid
1. Single database search: Misses relevant papers; always search multiple databases
2. No search documentation: Makes review irreproducible; document all searches
3. Study-by-study summary: Lacks synthesis; organize thematically instead
4. Treating DOI existence as support: A registered DOI can still identify the wrong work; check identity, claim support, corrections, and retractions
5. Too broad search: Yields thousands of irrelevant results; refine with specific terms
6. Too narrow search: Misses relevant papers; include synonyms and related terms
7. Ignoring preprints: Misses latest findings; include bioRxiv, medRxiv, arXiv
8. No quality assessment: Treats all evidence equally; assess and report quality
9. Publication bias: Only positive results published; note potential bias
10. Outdated search: Field evolves rapidly; clearly state search date
Integration with Other Skills
- parallel-web: Supplementary discovery, citation chaining, and URL extraction.
- citation-management: Metadata normalization, BibTeX/CSL export, and citation formatting.
- pubmed-database / paper-lookup: Domain-specific retrieval and lawful full-text discovery.
- matplotlib / seaborn: Reproducible evidence plots and quantitative figures.
- venue-templates: Target-journal structure, reference style, and submission requirements.
gget search queries Ensembl identifiers; it is not a PubMed or bioRxiv search command. Biological entity databases can inform background sections but do not replace bibliographic searching.
Resources
Bundled Resources
Scripts:
scripts/verify_citations.py: Check DOI registration and retrieve Crossref metadata for manual reviewscripts/generate_pdf.py: Convert markdown to professional PDFscripts/search_databases.py: Process, deduplicate, and format search results
References:
references/citation_styles.md: Detailed citation formatting guide (APA, Nature, Vancouver, Chicago, IEEE)references/database_strategies.md: Comprehensive database search strategies
Assets:
assets/review_template.md:
… (nội dung đầy đủ có trong gói tải về)
Skill liên quan
- azure-ai-projects-java – SDK Azure AI Projects cho Java. SDK cấp cao quản lý dự án Azure AI Foundry gồm…
- yeet – Chỉ dùng khi người dùng yêu cầu rõ ràng stage, commit, push và mở pull request trên…
- hunt-nodejs – Săn các lỗ hổng đặc thù Node.js — chuỗi Prototype Pollution → RCE (lodash/merge/assign)…
- google-workspace – Gmail, Calendar, Drive, Docs, Sheets qua gws CLI hoặc Python.
- azure-ai-vision-imageanalysis-java – Xây dựng ứng dụng phân tích ảnh với SDK Azure AI Vision cho Java. Dùng khi triển khai…
- azure-ai-voicelive-java – SDK Azure AI VoiceLive cho Java. Hội thoại giọng nói hai chiều thời gian thực với trợ lý…
- langchain-architecture – Thiết kế ứng dụng LLM bằng LangChain 1.x và LangGraph cho agent, bộ nhớ và tích hợp công…
- grok – Giao việc lập trình cho xAI Grok Build CLI (tính năng, PR).