[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QF19A7ZXMC9WZP2K19ZXC4":3,"skill-same:01M6QF19A7ZXMC9WZP2K19ZXC4":143},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":34,"publishedAt":55,"linkOnly":56,"markdownContent":57,"markdownBody":58,"frontMatter":59,"refName":70,"commitSha":71,"skillDirectory":72,"forksCount":14,"repositoryHtmlUrl":73,"repositoryHomepageUrl":74,"ownerHtmlUrl":75,"ownerBlogUrl":76,"ownerDescription":77,"updatedAt":78,"install":79,"resources":92},"01M6QF19A7ZXMC9WZP2K19ZXC4","literature-review",null,"Runs systematic literature reviews by searching PubMed, arXiv, bioRxiv, and Semantic Scholar (plus web search via parallel-cli), screening studies, extracting data, synthesizing themes, and verifying citations with verify_citations.py. Produces a markdown and PDF review with a PRISMA-style flow diagram and a bibliography in APA, Nature, or Vancouver style. Use when conducting a systematic or scoping review, synthesizing research on a topic, writing the literature review section of a paper or thesis, identifying research gaps, or checking citations in a review. Not for looking up a single paper or database record; use the specific database skill instead.","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-literature-review\u002FSKILL.md",[18,23,26,29],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":30,"categoryName":31,"taxonomyType":32,"taxonomyTypeDesc":33},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54],"review","literature review","literature","prisma","section","for","screening","scholar","reviews","themes","pubmed","systematic","PDF","edit","write","read","parallel","web search","search","writing","2026-11-10 10:12:30",false,"---\nname: literature-review\ndescription: Runs systematic literature reviews by searching PubMed, arXiv, bioRxiv, and Semantic Scholar (plus web search via parallel-cli), screening studies, extracting data, synthesizing themes, and verifying citations with verify_citations.py. Produces a markdown and PDF review with a PRISMA-style flow diagram and a bibliography in APA, Nature, or Vancouver style. Use when conducting a systematic or scoping review, synthesizing research on a topic, writing the literature review section of a paper or thesis, identifying research gaps, or checking citations in a review. Not for looking up a single paper or database record; use the specific database skill instead.\nlicense: MIT\nallowed-tools: Read Write Edit Bash\nmetadata:\n  version: '1.8'\n  category: literature-review\n  maintainer: Kalaris Labs\n  openclaw:\n    primaryEnv: OPENROUTER_API_KEY\n    envVars:\n    - name: OPENROUTER_API_KEY\n      required: false\n      description: OpenRouter API key for the skill's LLM-powered steps.\n  tags: literature review, systematic review, PubMed, arXiv, evidence synthesis, citations\n---\n\n# Literature Review\n\n## Overview\n\nConduct 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.\n\nThis skill uses the **parallel-web skill** (`parallel-cli search`) as the primary web search tool for broad academic literature discovery, supplemented by specialized database access skills (gget, bioservices, datacommons-client). It provides specialized tools for citation verification, result aggregation, and document generation.\n\n## When to Use This Skill\n\nUse this skill when:\n- Conducting a systematic literature review for research or publication\n- Synthesizing current knowledge on a specific topic across multiple sources\n- Performing meta-analysis or scoping reviews\n- Writing the literature review section of a research paper or thesis\n- Investigating the state of the art in a research domain\n- Identifying research gaps and future directions\n- Requiring verified citations and professional formatting\n\n## Visual Enhancement with Scientific Schematics\n\n**⚠️ MANDATORY: Every literature review MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.**\n\nThis is not optional. Literature reviews without visual elements are incomplete. Before finalizing any document:\n1. Generate at minimum ONE schematic or diagram (e.g., PRISMA flow diagram for systematic reviews)\n2. Prefer 2-3 figures for comprehensive reviews (search strategy flowchart, thematic synthesis diagram, conceptual framework)\n\n**How to generate figures:**\n- Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams\n- Simply describe your desired diagram in natural language\n- Nano Banana Pro will automatically generate, review, and refine the schematic\n\n**How to generate schematics:**\n```bash\npython scripts\u002Fgenerate_schematic.py \"your diagram description\" -o figures\u002Foutput.png\n```\n\nThe AI will automatically:\n- Create publication-quality images with proper formatting\n- Review and refine through multiple iterations\n- Ensure accessibility (colorblind-friendly, high contrast)\n- Save outputs in the figures\u002F directory\n\n**When to add schematics:**\n- PRISMA flow diagrams for systematic reviews\n- Literature search strategy flowcharts\n- Thematic synthesis diagrams\n- Research gap visualization maps\n- Citation network diagrams\n- Conceptual framework illustrations\n- Any complex concept that benefits from visualization\n\nFor detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.\n\n---\n\n## Core Workflow\n\nA literature review runs in seven phases, documented in full with commands and templates\nin [references\u002Fcore_workflow.md](references\u002Fcore_workflow.md):\n\n1. **Planning and scoping** — the question, inclusion and exclusion criteria, and scope.\n2. **Systematic literature search** — multi-database searching with recorded queries.\n3. **Screening and selection** — title\u002Fabstract then full-text screening with counts kept\n   for the PRISMA flow.\n4. **Data extraction and quality assessment** — structured extraction and risk-of-bias\n   or quality appraisal.\n5. **Synthesis and analysis** — thematic or quantitative synthesis across studies.\n6. **Citation verification** — every citation checked against the actual source.\n7. **Document generation** — assembling the review with a complete bibliography.\n\nRecord every search string and date as you go: a review that cannot reproduce its own\nsearch is not systematic. Per-database search guidance and citation styles are in\n[references\u002Fsearch_and_citation.md](references\u002Fsearch_and_citation.md), and a full worked\nreview is in [references\u002Fexample_workflow.md](references\u002Fexample_workflow.md).\n\n## Best Practices\n\n### Search Strategy\n1. **Start with parallel-web**: Use `parallel-cli search` with academic domains for initial broad coverage before querying specialized databases\n2. **Use multiple databases** (minimum 3): Ensures comprehensive coverage — parallel-web counts as one source\n3. **Include preprint servers**: Captures latest unpublished findings\n4. **Document everything**: Search strings, dates, result counts for reproducibility — save all parallel-cli output to `sources\u002F`\n5. **Test and refine**: Run pilot searches, review results, adjust search terms\n6. **Sort by citations**: When available, sort search results by citation count to surface influential work first\n7. **Use parallel-cli extract**: Fetch full content from promising URLs found during search to verify relevance before full-text screening\n\n### Screening and Selection\n1. **Use multiple databases** (minimum 3): Ensures comprehensive coverage\n2. **Include preprint servers**: Captures latest unpublished findings\n3. **Document everything**: Search strings, dates, result counts for reproducibility\n4. **Test and refine**: Run pilot searches, review results, adjust search terms\n\n### Screening and Selection\n1. **Use clear criteria**: Document inclusion\u002Fexclusion criteria before screening\n2. **Screen systematically**: Title → Abstract → Full text\n3. **Document exclusions**: Record reasons for excluding studies\n4. **Consider dual screening**: For systematic reviews, have two reviewers screen independently\n\n### Synthesis\n1. **Organize thematically**: Group by themes, NOT by individual studies\n2. **Synthesize across studies**: Compare, contrast, identify patterns\n3. **Be critical**: Evaluate quality and consistency of evidence\n4. **Identify gaps**: Note what's missing or understudied\n\n### Quality and Reproducibility\n1. **Assess study quality**: Use appropriate quality assessment tools\n2. **Verify all citations**: Run verify_citations.py script\n3. **Document methodology**: Provide enough detail for others to reproduce\n4. **Follow guidelines**: Use PRISMA for systematic reviews\n\n### Writing\n1. **Be objective**: Present evidence fairly, acknowledge limitations\n2. **Be systematic**: Follow structured template\n3. **Be specific**: Include numbers, statistics, effect sizes where available\n4. **Be clear**: Use clear headings, logical flow, thematic organization\n\n## Common Pitfalls to Avoid\n\n1. **Single database search**: Misses relevant papers; always search multiple databases\n2. **No search documentation**: Makes review irreproducible; document all searches\n3. **Study-by-study summary**: Lacks synthesis; organize thematically instead\n4. **Unverified citations**: Leads to errors; always run verify_citations.py\n5. **Too broad search**: Yields thousands of irrelevant results; refine with specific terms\n6. **Too narrow search**: Misses relevant papers; include synonyms and related terms\n7. **Ignoring preprints**: Misses latest findings; include bioRxiv, medRxiv, arXiv\n8. **No quality assessment**: Treats all evidence equally; assess and report quality\n9. **Publication bias**: Only positive results published; note potential bias\n10. **Outdated search**: Field evolves rapidly; clearly state search date\n\n## Integration with Other Skills\n\nThis skill works seamlessly with other scientific skills:\n\n### Web Search & Extraction (parallel-web skill — PRIMARY)\n- **parallel-cli search**: Broad academic and general web search with domain filtering — use for initial scoping, finding papers, citation chaining, and supplementary searches\n- **parallel-cli extract**: Fetch full content from paper URLs, journal websites, and preprint servers — use for reading abstracts, extracting reference lists, and verifying paper details\n- **parallel-cli search --include-domains**: Academic-focused search across scholarly domains (arxiv.org, pubmed, nature.com, etc.)\n\n### Database Access Skills\n- **gget**: PubMed, bioRxiv, COSMIC, AlphaFold, Ensembl, UniProt\n- **bioservices**: ChEMBL, KEGG, Reactome, UniProt, PubChem\n- **datacommons-client**: Demographics, economics, health statistics\n\n### Analysis Skills\n- **pydeseq2**: RNA-seq differential expression (for methods sections)\n- **scanpy**: Single-cell analysis (for methods sections)\n- **anndata**: Single-cell data (for methods sections)\n- **biopython**: Sequence analysis (for background sections)\n\n### Visualization Skills\n- **matplotlib**: Generate figures and plots for review\n- **seaborn**: Statistical visualizations\n\n### Writing Skills\n- **brand-guidelines**: Apply institutional branding to PDF\n- **internal-comms**: Adapt review for different audiences\n- **venue-templates**: Access venue-specific writing style guides when preparing reviews for publication\n\n### Venue-Specific Writing Styles\n\nWhen preparing a literature review for a specific journal, consult the **venue-templates** skill for writing style guidance:\n- `venue_writing_styles.md`: Master style comparison across venues\n- `nature_science_style.md`: Nature\u002FScience flowing abstract style, story-driven structure\n- `cell_press_style.md`: Cell Press graphical abstracts, Highlights format\n- `medical_journal_styles.md`: NEJM\u002FLancet\u002FJAMA structured abstracts, PRISMA compliance\n\nThese guides help adapt your review's tone, abstract format, and structure to match the target venue's expectations.\n\n## Resources\n\n### Bundled Resources\n\n**Scripts:**\n- `scripts\u002Fverify_citations.py`: Verify DOIs and generate formatted citations\n- `scripts\u002Fgenerate_pdf.py`: Convert markdown to professional PDF\n- `scripts\u002Fsearch_databases.py`: Process, deduplicate, and format search results\n\n**References:**\n- `references\u002Fcitation_styles.md`: Detailed citation formatting guide (APA, Nature, Vancouver, Chicago, IEEE)\n- `references\u002Fdatabase_strategies.md`: Comprehensive database search strategies\n\n**Assets:**\n- `assets\u002Freview_template.md`: Complete literature review template with all sections\n\n### External Resources\n\n**Guidelines:**\n- PRISMA (Systematic Reviews): http:\u002F\u002Fwww.prisma-statement.org\u002F\n- Cochrane Handbook: https:\u002F\u002Ftraining.cochrane.org\u002Fhandbook\n- AMSTAR 2 (Review Quality): https:\u002F\u002Famstar.ca\u002F\n\n**Tools:**\n- MeSH Browser: https:\u002F\u002Fmeshb.nlm.nih.gov\u002Fsearch\n- PubMed Advanced Search: https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002Fadvanced\u002F\n- Boolean Search Guide: https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fbooks\u002FNBK3827\u002F\n\n**Citation Styles:**\n- APA Style: https:\u002F\u002Fapastyle.apa.org\u002F\n- Nature Portfolio: https:\u002F\u002Fwww.nature.com\u002Fnature-portfolio\u002Feditorial-policies\u002Freporting-standards\n- NLM\u002FVancouver: https:\u002F\u002Fwww.nlm.nih.gov\u002Fbsd\u002Funiform_requirements.html\n\n## Dependencies\n\n### Required CLI Tools\n```bash\n# parallel-cli (PRIMARY — for web search and URL extraction)\ncurl -fsSL https:\u002F\u002Fparallel.ai\u002Finstall.sh | bash\n# Or: uv tool install \"parallel-web-tools[cli]\"\n# Authenticate: parallel-cli auth\n```\n\n### Required Python Packages\n```bash\nuv pip install requests  # For citation verification\n```\n\n### Required System Tools\n```bash\n# For PDF generation\nbrew install pandoc  # macOS\napt-get install pandoc  # Linux\n\n# For LaTeX (PDF generation)\nbrew install --cask mactex  # macOS\napt-get install texlive-xetex  # Linux\n```\n\nCheck dependencies:\n```bash\npython scripts\u002Fgenerate_pdf.py --check-deps\n```\n\n## Summary\n\nThis literature-review skill provides:\n\n1. **Systematic methodology** following academic best practices\n2. **Parallel-web powered search** using `parallel-cli search` for fast, broad academic literature discovery with scholarly domain filtering\n3. **Multi-database integration** via existing scientific skills (gget, bioservices, datacommons-client)\n4. **Citation verification** ensuring accuracy and credibility\n5. **Professional output** in markdown and PDF formats\n6. **Comprehensive guidance** covering the entire review process\n7. **Quality assurance** with verification and validation tools\n8. **Reproducibility** through detailed documentation requirements\n\nConduct thorough, rigorous literature reviews that meet academic standards and provide comprehensive synthesis of current knowledge in any domain.\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm the research question, databases, date range and inclusion criteria.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Run the search on one database with a narrow query and check that the results are relevant.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Every reference is retrieved from a real record with a resolvable identifier; counts and search strings are recorded.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| An API rate-limits or returns errors | Back off and retry, reduce the batch size, or switch database, and report the gap. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Never summarize a paper you have not retrieved; never cite from memory.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `systematic-review-prisma`: Plan, run and report systematic reviews and meta-analyses to PRISMA 2020 standards.\n- `citation-management`: Searches OpenAlex, PubMed, and Google Scholar, extracts metadata from DOIs, PMIDs, PMCIDs, arXiv IDs, and URLs via CrossRef, PubMed, and ar…\n- `paper-lookup`: Search 18 scholarly APIs for papers, preprints, citations, open-access full text, repository records, and journal OA status, and return res…\n","# Literature Review\n\n## Overview\n\nConduct 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.\n\nThis skill uses the **parallel-web skill** (`parallel-cli search`) as the primary web search tool for broad academic literature discovery, supplemented by specialized database access skills (gget, bioservices, datacommons-client). It provides specialized tools for citation verification, result aggregation, and document generation.\n\n## When to Use This Skill\n\nUse this skill when:\n- Conducting a systematic literature review for research or publication\n- Synthesizing current knowledge on a specific topic across multiple sources\n- Performing meta-analysis or scoping reviews\n- Writing the literature review section of a research paper or thesis\n- Investigating the state of the art in a research domain\n- Identifying research gaps and future directions\n- Requiring verified citations and professional formatting\n\n## Visual Enhancement with Scientific Schematics\n\n**⚠️ MANDATORY: Every literature review MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.**\n\nThis is not optional. Literature reviews without visual elements are incomplete. Before finalizing any document:\n1. Generate at minimum ONE schematic or diagram (e.g., PRISMA flow diagram for systematic reviews)\n2. Prefer 2-3 figures for comprehensive reviews (search strategy flowchart, thematic synthesis diagram, conceptual framework)\n\n**How to generate figures:**\n- Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams\n- Simply describe your desired diagram in natural language\n- Nano Banana Pro will automatically generate, review, and refine the schematic\n\n**How to generate schematics:**\n```bash\npython scripts\u002Fgenerate_schematic.py \"your diagram description\" -o figures\u002Foutput.png\n```\n\nThe AI will automatically:\n- Create publication-quality images with proper formatting\n- Review and refine through multiple iterations\n- Ensure accessibility (colorblind-friendly, high contrast)\n- Save outputs in the figures\u002F directory\n\n**When to add schematics:**\n- PRISMA flow diagrams for systematic reviews\n- Literature search strategy flowcharts\n- Thematic synthesis diagrams\n- Research gap visualization maps\n- Citation network diagrams\n- Conceptual framework illustrations\n- Any complex concept that benefits from visualization\n\nFor detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.\n\n---\n\n## Core Workflow\n\nA literature review runs in seven phases, documented in full with commands and templates\nin [references\u002Fcore_workflow.md](references\u002Fcore_workflow.md):\n\n1. **Planning and scoping** — the question, inclusion and exclusion criteria, and scope.\n2. **Systematic literature search** — multi-database searching with recorded queries.\n3. **Screening and selection** — title\u002Fabstract then full-text screening with counts kept\n   for the PRISMA flow.\n4. **Data extraction and quality assessment** — structured extraction and risk-of-bias\n   or quality appraisal.\n5. **Synthesis and analysis** — thematic or quantitative synthesis across studies.\n6. **Citation verification** — every citation checked against the actual source.\n7. **Document generation** — assembling the review with a complete bibliography.\n\nRecord every search string and date as you go: a review that cannot reproduce its own\nsearch is not systematic. Per-database search guidance and citation styles are in\n[references\u002Fsearch_and_citation.md](references\u002Fsearch_and_citation.md), and a full worked\nreview is in [references\u002Fexample_workflow.md](references\u002Fexample_workflow.md).\n\n## Best Practices\n\n### Search Strategy\n1. **Start with parallel-web**: Use `parallel-cli search` with academic domains for initial broad coverage before querying specialized databases\n2. **Use multiple databases** (minimum 3): Ensures comprehensive coverage — parallel-web counts as one source\n3. **Include preprint servers**: Captures latest unpublished findings\n4. **Document everything**: Search strings, dates, result counts for reproducibility — save all parallel-cli output to `sources\u002F`\n5. **Test and refine**: Run pilot searches, review results, adjust search terms\n6. **Sort by citations**: When available, sort search results by citation count to surface influential work first\n7. **Use parallel-cli extract**: Fetch full content from promising URLs found during search to verify relevance before full-text screening\n\n### Screening and Selection\n1. **Use multiple databases** (minimum 3): Ensures comprehensive coverage\n2. **Include preprint servers**: Captures latest unpublished findings\n3. **Document everything**: Search strings, dates, result counts for reproducibility\n4. **Test and refine**: Run pilot searches, review results, adjust search terms\n\n### Screening and Selection\n1. **Use clear criteria**: Document inclusion\u002Fexclusion criteria before screening\n2. **Screen systematically**: Title → Abstract → Full text\n3. **Document exclusions**: Record reasons for excluding studies\n4. **Consider dual screening**: For systematic reviews, have two reviewers screen independently\n\n### Synthesis\n1. **Organize thematically**: Group by themes, NOT by individual studies\n2. **Synthesize across studies**: Compare, contrast, identify patterns\n3. **Be critical**: Evaluate quality and consistency of evidence\n4. **Identify gaps**: Note what's missing or understudied\n\n### Quality and Reproducibility\n1. **Assess study quality**: Use appropriate quality assessment tools\n2. **Verify all citations**: Run verify_citations.py script\n3. **Document methodology**: Provide enough detail for others to reproduce\n4. **Follow guidelines**: Use PRISMA for systematic reviews\n\n### Writing\n1. **Be objective**: Present evidence fairly, acknowledge limitations\n2. **Be systematic**: Follow structured template\n3. **Be specific**: Include numbers, statistics, effect sizes where available\n4. **Be clear**: Use clear headings, logical flow, thematic organization\n\n## Common Pitfalls to Avoid\n\n1. **Single database search**: Misses relevant papers; always search multiple databases\n2. **No search documentation**: Makes review irreproducible; document all searches\n3. **Study-by-study summary**: Lacks synthesis; organize thematically instead\n4. **Unverified citations**: Leads to errors; always run verify_citations.py\n5. **Too broad search**: Yields thousands of irrelevant results; refine with specific terms\n6. **Too narrow search**: Misses relevant papers; include synonyms and related terms\n7. **Ignoring preprints**: Misses latest findings; include bioRxiv, medRxiv, arXiv\n8. **No quality assessment**: Treats all evidence equally; assess and report quality\n9. **Publication bias**: Only positive results published; note potential bias\n10. **Outdated search**: Field evolves rapidly; clearly state search date\n\n## Integration with Other Skills\n\nThis skill works seamlessly with other scientific skills:\n\n### Web Search & Extraction (parallel-web skill — PRIMARY)\n- **parallel-cli search**: Broad academic and general web search with domain filtering — use for initial scoping, finding papers, citation chaining, and supplementary searches\n- **parallel-cli extract**: Fetch full content from paper URLs, journal websites, and preprint servers — use for reading abstracts, extracting reference lists, and verifying paper details\n- **parallel-cli search --include-domains**: Academic-focused search across scholarly domains (arxiv.org, pubmed, nature.com, etc.)\n\n### Database Access Skills\n- **gget**: PubMed, bioRxiv, COSMIC, AlphaFold, Ensembl, UniProt\n- **bioservices**: ChEMBL, KEGG, Reactome, UniProt, PubChem\n- **datacommons-client**: Demographics, economics, health statistics\n\n### Analysis Skills\n- **pydeseq2**: RNA-seq differential expression (for methods sections)\n- **scanpy**: Single-cell analysis (for methods sections)\n- **anndata**: Single-cell data (for methods sections)\n- **biopython**: Sequence analysis (for background sections)\n\n### Visualization Skills\n- **matplotlib**: Generate figures and plots for review\n- **seaborn**: Statistical visualizations\n\n### Writing Skills\n- **brand-guidelines**: Apply institutional branding to PDF\n- **internal-comms**: Adapt review for different audiences\n- **venue-templates**: Access venue-specific writing style guides when preparing reviews for publication\n\n### Venue-Specific Writing Styles\n\nWhen preparing a literature review for a specific journal, consult the **venue-templates** skill for writing style guidance:\n- `venue_writing_styles.md`: Master style comparison across venues\n- `nature_science_style.md`: Nature\u002FScience flowing abstract style, story-driven structure\n- `cell_press_style.md`: Cell Press graphical abstracts, Highlights format\n- `medical_journal_styles.md`: NEJM\u002FLancet\u002FJAMA structured abstracts, PRISMA compliance\n\nThese guides help adapt your review's tone, abstract format, and structure to match the target venue's expectations.\n\n## Resources\n\n### Bundled Resources\n\n**Scripts:**\n- `scripts\u002Fverify_citations.py`: Verify DOIs and generate formatted citations\n- `scripts\u002Fgenerate_pdf.py`: Convert markdown to professional PDF\n- `scripts\u002Fsearch_databases.py`: Process, deduplicate, and format search results\n\n**References:**\n- `references\u002Fcitation_styles.md`: Detailed citation formatting guide (APA, Nature, Vancouver, Chicago, IEEE)\n- `references\u002Fdatabase_strategies.md`: Comprehensive database search strategies\n\n**Assets:**\n- `assets\u002Freview_template.md`: Complete literature review template with all sections\n\n### External Resources\n\n**Guidelines:**\n- PRISMA (Systematic Reviews): http:\u002F\u002Fwww.prisma-statement.org\u002F\n- Cochrane Handbook: https:\u002F\u002Ftraining.cochrane.org\u002Fhandbook\n- AMSTAR 2 (Review Quality): https:\u002F\u002Famstar.ca\u002F\n\n**Tools:**\n- MeSH Browser: https:\u002F\u002Fmeshb.nlm.nih.gov\u002Fsearch\n- PubMed Advanced Search: https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002Fadvanced\u002F\n- Boolean Search Guide: https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fbooks\u002FNBK3827\u002F\n\n**Citation Styles:**\n- APA Style: https:\u002F\u002Fapastyle.apa.org\u002F\n- Nature Portfolio: https:\u002F\u002Fwww.nature.com\u002Fnature-portfolio\u002Feditorial-policies\u002Freporting-standards\n- NLM\u002FVancouver: https:\u002F\u002Fwww.nlm.nih.gov\u002Fbsd\u002Funiform_requirements.html\n\n## Dependencies\n\n### Required CLI Tools\n```bash\n# parallel-cli (PRIMARY — for web search and URL extraction)\ncurl -fsSL https:\u002F\u002Fparallel.ai\u002Finstall.sh | bash\n# Or: uv tool install \"parallel-web-tools[cli]\"\n# Authenticate: parallel-cli auth\n```\n\n### Required Python Packages\n```bash\nuv pip install requests  # For citation verification\n```\n\n### Required System Tools\n```bash\n# For PDF generation\nbrew install pandoc  # macOS\napt-get install pandoc  # Linux\n\n# For LaTeX (PDF generation)\nbrew install --cask mactex  # macOS\napt-get install texlive-xetex  # Linux\n```\n\nCheck dependencies:\n```bash\npython scripts\u002Fgenerate_pdf.py --check-deps\n```\n\n## Summary\n\nThis literature-review skill provides:\n\n1. **Systematic methodology** following academic best practices\n2. **Parallel-web powered search** using `parallel-cli search` for fast, broad academic literature discovery with scholarly domain filtering\n3. **Multi-database integration** via existing scientific skills (gget, bioservices, datacommons-client)\n4. **Citation verification** ensuring accuracy and credibility\n5. **Professional output** in markdown and PDF formats\n6. **Comprehensive guidance** covering the entire review process\n7. **Quality assurance** with verification and validation tools\n8. **Reproducibility** through detailed documentation requirements\n\nConduct thorough, rigorous literature reviews that meet academic standards and provide comprehensive synthesis of current knowledge in any domain.\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm the research question, databases, date range and inclusion criteria.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Run the search on one database with a narrow query and check that the results are relevant.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Every reference is retrieved from a real record with a resolvable identifier; counts and search strings are recorded.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| An API rate-limits or returns errors | Back off and retry, reduce the batch size, or switch database, and report the gap. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Never summarize a paper you have not retrieved; never cite from memory.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `systematic-review-prisma`: Plan, run and report systematic reviews and meta-analyses to PRISMA 2020 standards.\n- `citation-management`: Searches OpenAlex, PubMed, and Google Scholar, extracts metadata from DOIs, PMIDs, PMCIDs, arXiv IDs, and URLs via CrossRef, PubMed, and ar…\n- `paper-lookup`: Search 18 scholarly APIs for papers, preprints, citations, open-access full text, repository records, and journal OA status, and return res…\n",{"name":5,"license":15,"metadata":60,"description":7,"allowed-tools":69},{"tags":61,"version":62,"category":5,"openclaw":63,"maintainer":68},"literature review, systematic review, PubMed, arXiv, evidence synthesis, citations","1.8",{"envVars":64,"primaryEnv":66},[65],{"name":66,"required":56,"description":67},"OPENROUTER_API_KEY","OpenRouter API key for the skill's LLM-powered steps.","Kalaris Labs","Read Write Edit Bash","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Fkal-literature-review","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6","https:\u002F\u002Fdisha6.6.thenitishkr.in\u002F","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh","www.thenitishkr","thenitishkr | AI • Cyber-security • Research\r\n","2026-11-08 03:33:25",{"source":8,"skillName":5,"commands":80,"prompt":90,"zipAvailable":91},[81,84,87],{"tool":82,"command":83},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill literature-review",{"tool":85,"command":86},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill literature-review",{"tool":88,"command":89},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill literature-review","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-literature-review\u002FSKILL.md",true,[93,99,105,109,113,117,121,127,131,135,139],{"path":94,"resourceType":95,"resourceTypeDesc":96,"sizeBytes":97,"sourceHtmlUrl":98},"assets\u002Freview_template.md","ASSET","资产文件",12982,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-literature-review\u002Fassets\u002Freview_template.md",{"path":100,"resourceType":101,"resourceTypeDesc":102,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002Fcitation_styles.md","REFERENCE","参考文档",6112,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-literature-review\u002Freferences\u002Fcitation_styles.md",{"path":106,"resourceType":101,"resourceTypeDesc":102,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002Fcore_workflow.md",9923,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-literature-review\u002Freferences\u002Fcore_workflow.md",{"path":110,"resourceType":101,"resourceTypeDesc":102,"sizeBytes":111,"sourceHtmlUrl":112},"references\u002Fdatabase_strategies.md",15491,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-literature-review\u002Freferences\u002Fdatabase_strategies.md",{"path":114,"resourceType":101,"resourceTypeDesc":102,"sizeBytes":115,"sourceHtmlUrl":116},"references\u002Fexample_workflow.md",2332,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-literature-review\u002Freferences\u002Fexample_workflow.md",{"path":118,"resourceType":101,"resourceTypeDesc":102,"sizeBytes":119,"sourceHtmlUrl":120},"references\u002Fsearch_and_citation.md",5698,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-literature-review\u002Freferences\u002Fsearch_and_citation.md",{"path":122,"resourceType":123,"resourceTypeDesc":124,"sizeBytes":125,"sourceHtmlUrl":126},"scripts\u002Fgenerate_pdf.py","SCRIPT","脚本",5646,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-literature-review\u002Fscripts\u002Fgenerate_pdf.py",{"path":128,"resourceType":123,"resourceTypeDesc":124,"sizeBytes":129,"sourceHtmlUrl":130},"scripts\u002Fgenerate_schematic_ai.py",38770,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-literature-review\u002Fscripts\u002Fgenerate_schematic_ai.py",{"path":132,"resourceType":123,"resourceTypeDesc":124,"sizeBytes":133,"sourceHtmlUrl":134},"scripts\u002Fgenerate_schematic.py",7836,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-literature-review\u002Fscripts\u002Fgenerate_schematic.py",{"path":136,"resourceType":123,"resourceTypeDesc":124,"sizeBytes":137,"sourceHtmlUrl":138},"scripts\u002Fsearch_databases.py",9272,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-literature-review\u002Fscripts\u002Fsearch_databases.py",{"path":140,"resourceType":123,"resourceTypeDesc":124,"sizeBytes":141,"sourceHtmlUrl":142},"scripts\u002Fverify_citations.py",7050,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-literature-review\u002Fscripts\u002Fverify_citations.py",[144,172,195,220,245,269],{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":153,"publishedAt":55},"01M6QRD07D4TGNDK6VJ4EHF8BV","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[150],{"categoryNo":151,"categoryName":152,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[154,155,156,157,53,158,159,160,161,162,163,164,165,166,167,168,40,169,170,171],"youtube full","youtube","full","transcript","monitoring","data","Video","Audio","list","files","do","get","age","trigger","URL","browsing","request","youtube transcripts",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":178,"publishedAt":55},"01M6QRCJ3112VYPA97YG7KGMC2","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[179,180,181,182,53,183,184,185,52,186,187,188,49,189,190,191,192,193,194,40],"twitter","x twitter growth","growth","research","content","engineering","plan","competitors","analyze","engine","strategy","use","platform","grow","posting","competitive",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":199,"categories":200,"keywords":202,"publishedAt":55},"01M6QRBN3EHQNJY42RERP7A149","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[201],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[203,49,204,205,206,207,208,209,190,210,211,70,212,213,214,215,216,217,218,219],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":221,"name":222,"nameZh":6,"description":223,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":224,"categories":225,"keywords":226,"publishedAt":55},"01M6QRB6G8P33PNGAF9Y69EJ9T","wrap-up","Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says \"wrap up\", \"close this out\", \"what do I own now\", \"give me the summary\", \"recap the agent\", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained .\u002Fmy-agent\u002Fagent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[227,228,229,230,231,232,233,234,235,190,236,164,237,238,239,240,241,242,243,244],"wrap up","up","wrap","gemini","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","directions","phase",{"skillNo":246,"name":247,"nameZh":6,"description":248,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":249,"categories":250,"keywords":252,"publishedAt":55},"01M6QRA5AYC1RBFZ39GTWGS3N8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[251],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[253,254,255,256,257,205,232,258,259,260,261,262,49,263,163,264,265,266,267,268],"workflow","workflow builder","builder","design","workflows","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":270,"name":271,"nameZh":6,"description":272,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":273,"categories":274,"keywords":275,"publishedAt":55},"01M6QR7QMCZESKQA0ERW1E657M","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[35,276,277,278,279,280,281,282,283,261,284,285,286,287,288,289,165,290,291,244],"weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]