[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XG3648QV2GNERDTSZ2YYPR":3,"skill-same:01M6XG3648QV2GNERDTSZ2YYPR":113},{"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":26,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":61,"commitSha":62,"skillDirectory":63,"forksCount":14,"repositoryHtmlUrl":64,"repositoryHomepageUrl":6,"ownerHtmlUrl":65,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":66,"install":67,"resources":80},"01M6XG3648QV2GNERDTSZ2YYPR","hugging-face-tool-builder",null,"Use this skill when the user wants to build tool\u002Fscripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated\u002Fautomated. This Skill creates a reusable script to fetch, enrich or process data.","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\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhugging-face-tool-builder\u002FSKILL.md",[18,23],{"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},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"tool","hugging face","face","builder","hugging face tool builder","build","useful","when","achieve","calls","enrich","synthetic","task","MCP","reusable","use","process","fetch","api","HuggingFace","2026-11-10 10:12:30",false,"---\nname: hugging-face-tool-builder\ndescription: Use this skill when the user wants to build tool\u002Fscripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated\u002Fautomated. This Skill creates a reusable script to fetch, enrich or process data.\ncategory: llm-tools\nversion: 1.0.0\nauthor: Synthetic Sciences\nlicense: MIT\ntags: [Hugging Face, Tools, Agents, MCP]\ndependencies: [huggingface-hub]\n---\n\n# Hugging Face API Tool Builder\n\nYour purpose is now is to create reusable command line scripts and utilities for using the Hugging Face API, allowing chaining, piping and intermediate processing where helpful. You can access the API directly, as well as use the `hf` command line tool. Model and Dataset cards can be accessed from repositories directly.\n\n## Script Rules\n\nMake sure to follow these rules:\n - Scripts must take a `--help` command line argument to describe their inputs and outputs\n - Non-destructive scripts should be tested before handing over to the User\n - Shell scripts are preferred, but use Python or TSX if complexity or user need requires it.\n - IMPORTANT: Use the `HF_TOKEN` environment variable as an Authorization header. For example: `curl -H \"Authorization: Bearer ${HF_TOKEN}\" https:\u002F\u002Fhuggingface.co\u002Fapi\u002F`. This provides higher rate limits and appropriate authorization for data access.\n - Investigate the shape of the API results before commiting to a final design; make use of piping and chaining where composability would be an advantage - prefer simple solutions where possible.\n - Share usage examples once complete.\n\nBe sure to confirm User preferences where there are questions or clarifications needed.\n\n## Sample Scripts\n\nPaths below are relative to this skill directory.\n\nReference examples:\n- `references\u002Fhf_model_papers_auth.sh` — uses `HF_TOKEN` automatically and chains trending → model metadata → model card parsing with fallbacks; it demonstrates multi-step API usage plus auth hygiene for gated\u002Fprivate content.\n- `references\u002Ffind_models_by_paper.sh` — optional `HF_TOKEN` usage via `--token`, consistent authenticated search, and a retry path when arXiv-prefixed searches are too narrow; it shows resilient query strategy and clear user-facing help.\n- `references\u002Fhf_model_card_frontmatter.sh` — uses the `hf` CLI to download model cards, extracts YAML frontmatter, and emits NDJSON summaries (license, pipeline tag, tags, gated prompt flag) for easy filtering.\n\nBaseline examples (ultra-simple, minimal logic, raw JSON output with `HF_TOKEN` header):\n- `references\u002Fbaseline_hf_api.sh` — bash\n- `references\u002Fbaseline_hf_api.py` — python\n- `references\u002Fbaseline_hf_api.tsx` — typescript executable\n\nComposable utility (stdin → NDJSON):\n- `references\u002Fhf_enrich_models.sh` — reads model IDs from stdin, fetches metadata per ID, emits one JSON object per line for streaming pipelines.\n\nComposability through piping (shell-friendly JSON output):\n- `references\u002Fbaseline_hf_api.sh 25 | jq -r '.[].id' | references\u002Fhf_enrich_models.sh | jq -s 'sort_by(.downloads) | reverse | .[:10]'`\n- `references\u002Fbaseline_hf_api.sh 50 | jq '[.[] | {id, downloads}] | sort_by(.downloads) | reverse | .[:10]'`\n- `printf '%s\\n' openai\u002Fgpt-oss-120b meta-llama\u002FMeta-Llama-3.1-8B | references\u002Fhf_model_card_frontmatter.sh | jq -s 'map({id, license, has_extra_gated_prompt})'`\n\n## High Level Endpoints\n\nThe following are the main API endpoints available at `https:\u002F\u002Fhuggingface.co`\n\n```\n\u002Fapi\u002Fdatasets\n\u002Fapi\u002Fmodels\n\u002Fapi\u002Fspaces\n\u002Fapi\u002Fcollections\n\u002Fapi\u002Fdaily_papers\n\u002Fapi\u002Fnotifications\n\u002Fapi\u002Fsettings\n\u002Fapi\u002Fwhoami-v2\n\u002Fapi\u002Ftrending\n\u002Foauth\u002Fuserinfo\n```\n\n## Accessing the API\n\nThe API is documented with the OpenAPI standard at `https:\u002F\u002Fhuggingface.co\u002F.well-known\u002Fopenapi.json`.\n\n**IMPORTANT:** DO NOT ATTEMPT to read `https:\u002F\u002Fhuggingface.co\u002F.well-known\u002Fopenapi.json` directly as it is too large to process. \n\n**IMPORTANT** Use `jq` to query and extract relevant parts. For example, \n\n Command to Get All 160 Endpoints\n\n```bash\ncurl -s \"https:\u002F\u002Fhuggingface.co\u002F.well-known\u002Fopenapi.json\" | jq '.paths | keys | sort'\n```\n\nModel Search Endpoint Details\n\n```bash\ncurl -s \"https:\u002F\u002Fhuggingface.co\u002F.well-known\u002Fopenapi.json\" | jq '.paths[\"\u002Fapi\u002Fmodels\"]'\n```\n\nYou can also query endpoints to see the shape of the data. When doing so constrain results to low numbers to make them easy to process, yet representative.\n\n## Using the HF command line tool\n\nThe `hf` command line tool gives you further access to Hugging Face repository content and infrastructure. \n\n```bash\n❯ hf --help\nUsage: hf [OPTIONS] COMMAND [ARGS]...\n\n  Hugging Face Hub CLI\n\nOptions:\n  --help                Show this message and exit.\n\nCommands:\n  auth                 Manage authentication (login, logout, etc.).\n  cache                Manage local cache directory.\n  download             Download files from the Hub.\n  endpoints            Manage Hugging Face Inference Endpoints.\n  env                  Print information about the environment.\n  jobs                 Run and manage Jobs on the Hub.\n  repo                 Manage repos on the Hub.\n  repo-files           Manage files in a repo on the Hub.\n  upload               Upload a file or a folder to the Hub.\n  upload-large-folder  Upload a large folder to the Hub.\n  version              Print information about the hf version.\n```\n\nThe `hf` CLI command has replaced the now deprecated `huggingface_hub` CLI command.\n","# Hugging Face API Tool Builder\n\nYour purpose is now is to create reusable command line scripts and utilities for using the Hugging Face API, allowing chaining, piping and intermediate processing where helpful. You can access the API directly, as well as use the `hf` command line tool. Model and Dataset cards can be accessed from repositories directly.\n\n## Script Rules\n\nMake sure to follow these rules:\n - Scripts must take a `--help` command line argument to describe their inputs and outputs\n - Non-destructive scripts should be tested before handing over to the User\n - Shell scripts are preferred, but use Python or TSX if complexity or user need requires it.\n - IMPORTANT: Use the `HF_TOKEN` environment variable as an Authorization header. For example: `curl -H \"Authorization: Bearer ${HF_TOKEN}\" https:\u002F\u002Fhuggingface.co\u002Fapi\u002F`. This provides higher rate limits and appropriate authorization for data access.\n - Investigate the shape of the API results before commiting to a final design; make use of piping and chaining where composability would be an advantage - prefer simple solutions where possible.\n - Share usage examples once complete.\n\nBe sure to confirm User preferences where there are questions or clarifications needed.\n\n## Sample Scripts\n\nPaths below are relative to this skill directory.\n\nReference examples:\n- `references\u002Fhf_model_papers_auth.sh` — uses `HF_TOKEN` automatically and chains trending → model metadata → model card parsing with fallbacks; it demonstrates multi-step API usage plus auth hygiene for gated\u002Fprivate content.\n- `references\u002Ffind_models_by_paper.sh` — optional `HF_TOKEN` usage via `--token`, consistent authenticated search, and a retry path when arXiv-prefixed searches are too narrow; it shows resilient query strategy and clear user-facing help.\n- `references\u002Fhf_model_card_frontmatter.sh` — uses the `hf` CLI to download model cards, extracts YAML frontmatter, and emits NDJSON summaries (license, pipeline tag, tags, gated prompt flag) for easy filtering.\n\nBaseline examples (ultra-simple, minimal logic, raw JSON output with `HF_TOKEN` header):\n- `references\u002Fbaseline_hf_api.sh` — bash\n- `references\u002Fbaseline_hf_api.py` — python\n- `references\u002Fbaseline_hf_api.tsx` — typescript executable\n\nComposable utility (stdin → NDJSON):\n- `references\u002Fhf_enrich_models.sh` — reads model IDs from stdin, fetches metadata per ID, emits one JSON object per line for streaming pipelines.\n\nComposability through piping (shell-friendly JSON output):\n- `references\u002Fbaseline_hf_api.sh 25 | jq -r '.[].id' | references\u002Fhf_enrich_models.sh | jq -s 'sort_by(.downloads) | reverse | .[:10]'`\n- `references\u002Fbaseline_hf_api.sh 50 | jq '[.[] | {id, downloads}] | sort_by(.downloads) | reverse | .[:10]'`\n- `printf '%s\\n' openai\u002Fgpt-oss-120b meta-llama\u002FMeta-Llama-3.1-8B | references\u002Fhf_model_card_frontmatter.sh | jq -s 'map({id, license, has_extra_gated_prompt})'`\n\n## High Level Endpoints\n\nThe following are the main API endpoints available at `https:\u002F\u002Fhuggingface.co`\n\n```\n\u002Fapi\u002Fdatasets\n\u002Fapi\u002Fmodels\n\u002Fapi\u002Fspaces\n\u002Fapi\u002Fcollections\n\u002Fapi\u002Fdaily_papers\n\u002Fapi\u002Fnotifications\n\u002Fapi\u002Fsettings\n\u002Fapi\u002Fwhoami-v2\n\u002Fapi\u002Ftrending\n\u002Foauth\u002Fuserinfo\n```\n\n## Accessing the API\n\nThe API is documented with the OpenAPI standard at `https:\u002F\u002Fhuggingface.co\u002F.well-known\u002Fopenapi.json`.\n\n**IMPORTANT:** DO NOT ATTEMPT to read `https:\u002F\u002Fhuggingface.co\u002F.well-known\u002Fopenapi.json` directly as it is too large to process. \n\n**IMPORTANT** Use `jq` to query and extract relevant parts. For example, \n\n Command to Get All 160 Endpoints\n\n```bash\ncurl -s \"https:\u002F\u002Fhuggingface.co\u002F.well-known\u002Fopenapi.json\" | jq '.paths | keys | sort'\n```\n\nModel Search Endpoint Details\n\n```bash\ncurl -s \"https:\u002F\u002Fhuggingface.co\u002F.well-known\u002Fopenapi.json\" | jq '.paths[\"\u002Fapi\u002Fmodels\"]'\n```\n\nYou can also query endpoints to see the shape of the data. When doing so constrain results to low numbers to make them easy to process, yet representative.\n\n## Using the HF command line tool\n\nThe `hf` command line tool gives you further access to Hugging Face repository content and infrastructure. \n\n```bash\n❯ hf --help\nUsage: hf [OPTIONS] COMMAND [ARGS]...\n\n  Hugging Face Hub CLI\n\nOptions:\n  --help                Show this message and exit.\n\nCommands:\n  auth                 Manage authentication (login, logout, etc.).\n  cache                Manage local cache directory.\n  download             Download files from the Hub.\n  endpoints            Manage Hugging Face Inference Endpoints.\n  env                  Print information about the environment.\n  jobs                 Run and manage Jobs on the Hub.\n  repo                 Manage repos on the Hub.\n  repo-files           Manage files in a repo on the Hub.\n  upload               Upload a file or a folder to the Hub.\n  upload-large-folder  Upload a large folder to the Hub.\n  version              Print information about the hf version.\n```\n\nThe `hf` CLI command has replaced the now deprecated `huggingface_hub` CLI command.\n",{"name":5,"tags":52,"author":56,"license":15,"version":57,"category":58,"description":7,"dependencies":59},[53,54,55,40],"Hugging Face","Tools","Agents","Synthetic Sciences","1.0.0","llm-tools",[60],"huggingface-hub","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhugging-face-tool-builder","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":68,"prompt":78,"zipAvailable":79},[69,72,75],{"tool":70,"command":71},"npx","npx skills add fadrienne\u002FMyGitHub --skill hugging-face-tool-builder",{"tool":73,"command":74},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill hugging-face-tool-builder",{"tool":76,"command":77},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill hugging-face-tool-builder","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhugging-face-tool-builder\u002FSKILL.md",true,[81,87,91,97,101,105,109],{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fbaseline_hf_api.py","SCRIPT","脚本",1369,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhugging-face-tool-builder\u002Freferences\u002Fbaseline_hf_api.py",{"path":88,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Fbaseline_hf_api.sh",707,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhugging-face-tool-builder\u002Freferences\u002Fbaseline_hf_api.sh",{"path":92,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Fbaseline_hf_api.tsx","REFERENCE","参考文档",1265,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhugging-face-tool-builder\u002Freferences\u002Fbaseline_hf_api.tsx",{"path":98,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Ffind_models_by_paper.sh",7695,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhugging-face-tool-builder\u002Freferences\u002Ffind_models_by_paper.sh",{"path":102,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002Fhf_enrich_models.sh",2267,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhugging-face-tool-builder\u002Freferences\u002Fhf_enrich_models.sh",{"path":106,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002Fhf_model_card_frontmatter.sh",4548,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhugging-face-tool-builder\u002Freferences\u002Fhf_model_card_frontmatter.sh",{"path":110,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":111,"sourceHtmlUrl":112},"references\u002Fhf_model_papers_auth.sh",4840,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhugging-face-tool-builder\u002Freferences\u002Fhf_model_papers_auth.sh",[114,141,167,200,227,254],{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":121,"publishedAt":47},"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",[120],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[122,123,124,125,126,127,128,129,34,130,131,132,133,134,135,136,137,138,139,140],"power","statistical power","effect","scipy","regression","choose","design","uv","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":150,"publishedAt":47},"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",[147],{"categoryNo":148,"categoryName":149,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[132,151,122,152,153,154,34,155,156,157,158,159,42,160,161,162,163,164,165,166],"statistical analysis","academic research","implementing","selection","you","reporting","data","for","tests","with","test","research","results","statsmodels","models","help",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":182,"publishedAt":47},"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",[173,176,177],{"categoryNo":174,"categoryName":175,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":178,"categoryName":179,"taxonomyType":180,"taxonomyTypeDesc":181},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[183,184,185,186,187,188,189,190,191,192,130,45,193,194,195,196,197,198,156,199],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":204,"categories":205,"keywords":210,"publishedAt":47},"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",[206,209],{"categoryNo":207,"categoryName":208,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[211,184,212,213,214,215,216,217,218,191,34,219,220,221,222,223,224,225,158,226],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":228,"name":229,"nameZh":6,"description":230,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":231,"categories":232,"keywords":239,"publishedAt":47},"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",[233,236],{"categoryNo":234,"categoryName":235,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":237,"categoryName":238,"taxonomyType":180,"taxonomyTypeDesc":181},"01JBSEEDCAT000000000000030","开发工程师",[240,241,162,242,243,244,245,129,246,34,130,132,247,248,249,250,251,252,198,253],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":255,"name":256,"nameZh":6,"description":257,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":258,"licenseFileUrl":6,"sourceHtmlUrl":259,"categories":260,"keywords":267,"publishedAt":47},"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",[261,264,265,266],{"categoryNo":262,"categoryName":263,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":174,"categoryName":175,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":178,"categoryName":179,"taxonomyType":180,"taxonomyTypeDesc":181},[183,268,269,270,271,272,211,273,274,34,130,132,275,276,277,278,279,280,184,198],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]