[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7165BA88N1E3C4FCW2Q883G":3,"skill-same:01M7165BA88N1E3C4FCW2Q883G":107},{"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":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":64,"commitSha":65,"skillDirectory":66,"forksCount":14,"repositoryHtmlUrl":67,"repositoryHomepageUrl":6,"ownerHtmlUrl":68,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":69,"install":70,"resources":82},"01M7165BA88N1E3C4FCW2Q883G","exa-search",null,"Web toolkit powered by Exa, tuned for scientific and technical content. Use this skill when the user needs to search the web or fetch\u002Fextract URL content. Covers: web search (semantic lookups, research, current info — with optional research-paper category and academic domain filtering) and URL extraction (fetching pages, articles, academic PDFs in batch). Use this skill for web-related tasks when the user wants high-quality search or scholarly filtering via category=research paper. Triggers on requests to search, look up, fetch a page, or extract an article.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fexa-search\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"exa search","exa","search","for","domain","full","article","scientific","info","batch","api","tasks","URL","when","access","internet","up","web search","python sdk","fetch","2026-11-10 10:12:30",false,"---\nname: exa-search\ndescription: \"Web toolkit powered by Exa, tuned for scientific and technical content. Use this skill when the user needs to search the web or fetch\u002Fextract URL content. Covers: web search (semantic lookups, research, current info — with optional research-paper category and academic domain filtering) and URL extraction (fetching pages, articles, academic PDFs in batch). Use this skill for web-related tasks when the user wants high-quality search or scholarly filtering via category=research paper. Triggers on requests to search, look up, fetch a page, or extract an article.\"\ncompatibility: Requires exa-py Python SDK, an EXA_API_KEY, and internet access.\nlicense: MIT\nrequired_environment_variables: [{\"name\": \"EXA_API_KEY\", \"prompt\": \"Exa search API key.\", \"required_for\": \"full functionality\"}]\nmetadata: {\"version\": \"1.1\", \"skill-author\": \"Exa\", \"website\": \"https:\u002F\u002Fexa.ai\", \"docs\": \"https:\u002F\u002Fexa.ai\u002Fdocs\", \"openclaw\": {\"primaryEnv\": \"EXA_API_KEY\", \"envVars\": [{\"name\": \"EXA_API_KEY\", \"required\": true, \"description\": \"Exa search API key.\"}]}}\n---\n\n# Exa Web Toolkit\n\nA skill for web-powered research tasks backed by [Exa](https:\u002F\u002Fexa.ai): web search and URL extraction. Exa's index combines high-quality keyword and semantic retrieval, which makes it well-suited to scientific, technical, and conceptual queries.\n\n## Routing — pick the right capability\n\nRead the user's request and match it to one of the capabilities below. Read the corresponding reference file for detailed instructions before running commands.\n\n| User wants to... | Capability | Where |\n|---|---|---|\n| Look something up, research a topic, find current info | **Web Search** | `references\u002Fweb-search.md` |\n| Fetch content from a specific URL (webpage, article, PDF) | **Web Extract** | `references\u002Fweb-extract.md` |\n| Install or authenticate | **Setup** | Below |\n\n### Decision guide\n\n- **Default to Web Search** for topic lookups, research questions, or \"what is X?\" queries. When the topic is scientific or technical, pass `--category \"research paper\"` to bias toward scholarly sources, and\u002For an academic `--include-domains` allowlist. See `references\u002Fweb-search.md` for the two-pass academic strategy.\n- **Use Web Extract** when the user provides a URL or asks you to read\u002Ffetch a specific page. Prefer this over the built-in WebFetch for batch extraction (multiple URLs in one call) and for academic PDFs.\n\n### Academic source priority\n\nFor technical or scientific queries, prefer academic and scientific sources:\n- Peer-reviewed journal articles and conference proceedings over blog posts or news\n- Preprints (arXiv, bioRxiv, medRxiv) when peer-reviewed versions aren't available\n- Institutional and government sources (NIH, WHO, NASA, NIST) over commercial sites\n- Primary research over secondary summaries\n\nTwo levers to steer Exa toward scholarly content:\n1. `--category \"research paper\"` biases retrieval toward scholarly sources.\n2. `--include-domains` with a scholarly allowlist (arxiv.org, nature.com, pubmed.ncbi.nlm.nih.gov, etc.) restricts the domain pool.\n\nCombine both for strictly academic results. See `references\u002Fweb-search.md` for the full pattern.\n\nWhen citing academic sources, include author names and publication year where available (e.g., [Smith et al., 2025](url)) in addition to the standard citation format. If a DOI is present, prefer the DOI link.\n\n---\n\n## Setup\n\nThis skill uses the [`exa-py`](https:\u002F\u002Fgithub.com\u002Fexa-labs\u002Fexa-py) Python SDK. The scripts in `scripts\u002F` declare their dependencies via PEP 723 inline metadata, so you can run them directly with `uv run` without a separate install step:\n\n```bash\nuv run --with exa-py python \"$SKILL_PATH\u002Fscripts\u002Fexa_search.py\" --help\n```\n\nIf you prefer a persistent install:\n\n```bash\nuv pip install \"exa-py>=1.14.0\"\n```\n\n### Authentication\n\nAll commands read the API key from the `EXA_API_KEY` environment variable. Get your Exa API key at [dashboard.exa.ai\u002Fapi-keys](https:\u002F\u002Fdashboard.exa.ai\u002Fapi-keys).\n\nFirst, check if a `.env` file exists in the project root and contains `EXA_API_KEY`. If so, load it:\n\n```bash\ndotenv -f .env run -- uv run --with exa-py python \"$SKILL_PATH\u002Fscripts\u002Fexa_search.py\" \"your query\"\n```\n\nIf `dotenv` isn't available, install it: `pip install python-dotenv[cli]` or `uv pip install python-dotenv[cli]`.\n\nIf there's no `.env`, export the key for the session:\n\n```bash\nexport EXA_API_KEY=\"your-key\"\n```\n\nVerify by running any script with `--help` — it will exit cleanly if the key is set and auth-check runs only when a real query is made.\n\n### Tracking header\n\nEvery script in this skill sets the `x-exa-integration` request header to `k-dense-ai--scientific-agent-skills` so Exa can attribute usage from the K-Dense AI scientific-agent-skills repo to this integration. Do not remove or rename this header when adapting the scripts.\n\n---\n\n## Files in this skill\n\n- `SKILL.md` — this file (routing and setup)\n- `references\u002Fweb-search.md` — detailed web search reference with academic strategy\n- `references\u002Fweb-extract.md` — URL content extraction reference\n- `scripts\u002Fexa_search.py` — CLI wrapper around `client.search_and_contents`\n- `scripts\u002Fexa_extract.py` — CLI wrapper around `client.get_contents`\n","# Exa Web Toolkit\n\nA skill for web-powered research tasks backed by [Exa](https:\u002F\u002Fexa.ai): web search and URL extraction. Exa's index combines high-quality keyword and semantic retrieval, which makes it well-suited to scientific, technical, and conceptual queries.\n\n## Routing — pick the right capability\n\nRead the user's request and match it to one of the capabilities below. Read the corresponding reference file for detailed instructions before running commands.\n\n| User wants to... | Capability | Where |\n|---|---|---|\n| Look something up, research a topic, find current info | **Web Search** | `references\u002Fweb-search.md` |\n| Fetch content from a specific URL (webpage, article, PDF) | **Web Extract** | `references\u002Fweb-extract.md` |\n| Install or authenticate | **Setup** | Below |\n\n### Decision guide\n\n- **Default to Web Search** for topic lookups, research questions, or \"what is X?\" queries. When the topic is scientific or technical, pass `--category \"research paper\"` to bias toward scholarly sources, and\u002For an academic `--include-domains` allowlist. See `references\u002Fweb-search.md` for the two-pass academic strategy.\n- **Use Web Extract** when the user provides a URL or asks you to read\u002Ffetch a specific page. Prefer this over the built-in WebFetch for batch extraction (multiple URLs in one call) and for academic PDFs.\n\n### Academic source priority\n\nFor technical or scientific queries, prefer academic and scientific sources:\n- Peer-reviewed journal articles and conference proceedings over blog posts or news\n- Preprints (arXiv, bioRxiv, medRxiv) when peer-reviewed versions aren't available\n- Institutional and government sources (NIH, WHO, NASA, NIST) over commercial sites\n- Primary research over secondary summaries\n\nTwo levers to steer Exa toward scholarly content:\n1. `--category \"research paper\"` biases retrieval toward scholarly sources.\n2. `--include-domains` with a scholarly allowlist (arxiv.org, nature.com, pubmed.ncbi.nlm.nih.gov, etc.) restricts the domain pool.\n\nCombine both for strictly academic results. See `references\u002Fweb-search.md` for the full pattern.\n\nWhen citing academic sources, include author names and publication year where available (e.g., [Smith et al., 2025](url)) in addition to the standard citation format. If a DOI is present, prefer the DOI link.\n\n---\n\n## Setup\n\nThis skill uses the [`exa-py`](https:\u002F\u002Fgithub.com\u002Fexa-labs\u002Fexa-py) Python SDK. The scripts in `scripts\u002F` declare their dependencies via PEP 723 inline metadata, so you can run them directly with `uv run` without a separate install step:\n\n```bash\nuv run --with exa-py python \"$SKILL_PATH\u002Fscripts\u002Fexa_search.py\" --help\n```\n\nIf you prefer a persistent install:\n\n```bash\nuv pip install \"exa-py>=1.14.0\"\n```\n\n### Authentication\n\nAll commands read the API key from the `EXA_API_KEY` environment variable. Get your Exa API key at [dashboard.exa.ai\u002Fapi-keys](https:\u002F\u002Fdashboard.exa.ai\u002Fapi-keys).\n\nFirst, check if a `.env` file exists in the project root and contains `EXA_API_KEY`. If so, load it:\n\n```bash\ndotenv -f .env run -- uv run --with exa-py python \"$SKILL_PATH\u002Fscripts\u002Fexa_search.py\" \"your query\"\n```\n\nIf `dotenv` isn't available, install it: `pip install python-dotenv[cli]` or `uv pip install python-dotenv[cli]`.\n\nIf there's no `.env`, export the key for the session:\n\n```bash\nexport EXA_API_KEY=\"your-key\"\n```\n\nVerify by running any script with `--help` — it will exit cleanly if the key is set and auth-check runs only when a real query is made.\n\n### Tracking header\n\nEvery script in this skill sets the `x-exa-integration` request header to `k-dense-ai--scientific-agent-skills` so Exa can attribute usage from the K-Dense AI scientific-agent-skills repo to this integration. Do not remove or rename this header when adapting the scripts.\n\n---\n\n## Files in this skill\n\n- `SKILL.md` — this file (routing and setup)\n- `references\u002Fweb-search.md` — detailed web search reference with academic strategy\n- `references\u002Fweb-extract.md` — URL content extraction reference\n- `scripts\u002Fexa_search.py` — CLI wrapper around `client.search_and_contents`\n- `scripts\u002Fexa_extract.py` — CLI wrapper around `client.get_contents`\n",{"name":5,"license":15,"metadata":49,"description":7,"compatibility":60,"required_environment_variables":61},{"docs":50,"version":51,"website":52,"openclaw":53,"skill-author":59},"https:\u002F\u002Fexa.ai\u002Fdocs","1.1","https:\u002F\u002Fexa.ai",{"envVars":54,"primaryEnv":56},[55],{"name":56,"required":57,"description":58},"EXA_API_KEY",true,"Exa search API key.","Exa","Requires exa-py Python SDK, an EXA_API_KEY, and internet access.",[62],{"name":56,"prompt":58,"required_for":63},"full functionality","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","scientific-agent-skills\u002Fskills\u002Fexa-search","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":71,"prompt":81,"zipAvailable":57},[72,75,78],{"tool":73,"command":74},"npx","npx skills add fadrienne\u002FMyGitHub --skill exa-search",{"tool":76,"command":77},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill exa-search",{"tool":79,"command":80},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill exa-search","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fexa-search\u002FSKILL.md",[83,89,93,99,103],{"path":84,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Fweb-extract.md","REFERENCE","参考文档",2028,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fexa-search\u002Freferences\u002Fweb-extract.md",{"path":90,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Fweb-search.md",5366,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fexa-search\u002Freferences\u002Fweb-search.md",{"path":94,"resourceType":95,"resourceTypeDesc":96,"sizeBytes":97,"sourceHtmlUrl":98},"scripts\u002Fexa_extract.py","SCRIPT","脚本",3529,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fexa-search\u002Fscripts\u002Fexa_extract.py",{"path":100,"resourceType":95,"resourceTypeDesc":96,"sizeBytes":101,"sourceHtmlUrl":102},"scripts\u002Fexa_search.py",5979,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fexa-search\u002Fscripts\u002Fexa_search.py",{"path":104,"resourceType":95,"resourceTypeDesc":96,"sizeBytes":105,"sourceHtmlUrl":106},"tests\u002Ftest_exa_search.py",8260,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fexa-search\u002Ftests\u002Ftest_exa_search.py",[108,137,163,195,222,247],{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":117,"publishedAt":44},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[114],{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[118,119,120,121,122,123,124,125,37,126,127,128,129,130,131,132,133,134,135,136],"power","statistical power","effect","scipy","regression","choose","design","uv","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":146,"publishedAt":44},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[143],{"categoryNo":144,"categoryName":145,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[128,147,118,148,149,150,37,151,152,153,27,154,155,156,157,158,159,160,161,162],"statistical analysis","academic research","implementing","selection","you","reporting","data","tests","use","with","test","research","results","statsmodels","models","help",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":178,"publishedAt":44},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[169,172,173],{"categoryNo":170,"categoryName":171,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":174,"categoryName":175,"taxonomyType":176,"taxonomyTypeDesc":177},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[179,31,180,181,182,183,184,185,186,187,126,34,188,189,190,191,192,193,152,194],"writing","scientific writing","Citations","deep","deep research","never","papers","journal","false","prisma","structure","openrouter","Outlines","core skill","read","features",{"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":205,"publishedAt":44},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[201,204],{"categoryNo":202,"categoryName":203,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},[206,31,207,208,209,210,211,212,213,186,37,214,215,216,217,218,219,220,27,221],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":223,"name":224,"nameZh":6,"description":225,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":226,"categories":227,"keywords":232,"publishedAt":44},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[228,229],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":230,"categoryName":231,"taxonomyType":176,"taxonomyTypeDesc":177},"01JBSEEDCAT000000000000030","开发工程师",[233,234,158,235,236,237,238,125,239,37,126,128,240,241,242,243,244,245,193,246],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":248,"name":249,"nameZh":6,"description":250,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":251,"licenseFileUrl":6,"sourceHtmlUrl":252,"categories":253,"keywords":260,"publishedAt":44},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[254,257,258,259],{"categoryNo":255,"categoryName":256,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":170,"categoryName":171,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":174,"categoryName":175,"taxonomyType":176,"taxonomyTypeDesc":177},[179,261,262,263,264,265,206,266,267,37,126,128,268,269,270,271,272,273,31,193],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]