[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XG30REFJ011PH4ABWBE9HQ":3,"skill-same:01M6XG30REFJ011PH4ABWBE9HQ":85},{"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":18,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":14,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":60,"install":61,"resources":74},"01M6XG30REFJ011PH4ABWBE9HQ","hugging-face-cli",null,"Execute Hugging Face Hub operations using the `hf` CLI. Use when the user needs to download models\u002Fdatasets\u002Fspaces, upload files to Hub repositories, create repos, manage local cache, or run compute jobs on HF infrastructure. Covers authentication, file transfers, repository creation, cache operations, and cloud compute.","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-cli\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"cli","face","hugging face cli","hugging face","Cloud Compute","Model Download","when","transfers","synthetic","creation","local","files","model","Tools","Transformers","repository","authentication","run","create","infrastructure","repos","2026-11-10 10:12:30",false,"---\nname: hugging-face-cli\ndescription: Execute Hugging Face Hub operations using the `hf` CLI. Use when the user needs to download models\u002Fdatasets\u002Fspaces, upload files to Hub repositories, create repos, manage local cache, or run compute jobs on HF infrastructure. Covers authentication, file transfers, repository creation, cache operations, and cloud compute.\ncategory: llm-tools\nversion: 1.0.0\nauthor: Synthetic Sciences\nlicense: MIT\ntags: [Hugging Face, CLI, Model Download, Hub]\ndependencies: [huggingface-hub, transformers]\n---\n\n# Hugging Face CLI\n\nThe `hf` CLI provides direct terminal access to the Hugging Face Hub for downloading, uploading, and managing repositories, cache, and compute resources.\n\n## Quick Command Reference\n\n| Task | Command |\n|------|---------|\n| Login | `hf auth login` |\n| Download model | `hf download \u003Crepo_id>` |\n| Download to folder | `hf download \u003Crepo_id> --local-dir .\u002Fpath` |\n| Upload folder | `hf upload \u003Crepo_id> . .` |\n| Create repo | `hf repo create \u003Cname>` |\n| Create tag | `hf repo tag create \u003Crepo_id> \u003Ctag>` |\n| Delete files | `hf repo-files delete \u003Crepo_id> \u003Cfiles>` |\n| List cache | `hf cache ls` |\n| Remove from cache | `hf cache rm \u003Crepo_or_revision>` |\n| List models | `hf models ls` |\n| Get model info | `hf models info \u003Cmodel_id>` |\n| List datasets | `hf datasets ls` |\n| Get dataset info | `hf datasets info \u003Cdataset_id>` |\n| List spaces | `hf spaces ls` |\n| Get space info | `hf spaces info \u003Cspace_id>` |\n| List endpoints | `hf endpoints ls` |\n| Run GPU job | `hf jobs run --flavor a10g-small \u003Cimage> \u003Ccmd>` |\n| Environment info | `hf env` |\n\n\n## Credential Setup\n\nHuggingFace token is auto-injected by openscience when connected via the dashboard.\n\n```bash\n# Verify credentials\n[ -n \"$HF_TOKEN\" ] && echo \"HF_TOKEN set\" || echo \"NOT SET\"\n```\n\nIf not set: connect HuggingFace at https:\u002F\u002Fapp.syntheticsciences.ai -> Services, then restart openscience.\n\n## Core Commands\n\n### Authentication\n```bash\nhf auth login                    # Interactive login\nhf auth login --token $HF_TOKEN  # Non-interactive\nhf auth whoami                   # Check current user\nhf auth list                     # List stored tokens\nhf auth switch                   # Switch between tokens\nhf auth logout                   # Log out\n```\n\n### Download\n```bash\nhf download \u003Crepo_id>                              # Full repo to cache\nhf download \u003Crepo_id> file.safetensors             # Specific file\nhf download \u003Crepo_id> --local-dir .\u002Fmodels         # To local directory\nhf download \u003Crepo_id> --include \"*.safetensors\"    # Filter by pattern\nhf download \u003Crepo_id> --repo-type dataset          # Dataset\nhf download \u003Crepo_id> --revision v1.0              # Specific version\n```\n\n### Upload\n```bash\nhf upload \u003Crepo_id> . .                            # Current dir to root\nhf upload \u003Crepo_id> .\u002Fmodels \u002Fweights              # Folder to path\nhf upload \u003Crepo_id> model.safetensors              # Single file\nhf upload \u003Crepo_id> . . --repo-type dataset        # Dataset\nhf upload \u003Crepo_id> . . --create-pr                # Create PR\nhf upload \u003Crepo_id> . . --commit-message=\"msg\"     # Custom message\n```\n\n### Repository Management\n```bash\nhf repo create \u003Cname>                              # Create model repo\nhf repo create \u003Cname> --repo-type dataset          # Create dataset\nhf repo create \u003Cname> --private                    # Private repo\nhf repo create \u003Cname> --repo-type space --space_sdk gradio  # Gradio space\nhf repo delete \u003Crepo_id>                           # Delete repo\nhf repo move \u003Cfrom_id> \u003Cto_id>                     # Move repo to new namespace\nhf repo settings \u003Crepo_id> --private true          # Update repo settings\nhf repo list --repo-type model                     # List repos\nhf repo branch create \u003Crepo_id> release-v1         # Create branch\nhf repo branch delete \u003Crepo_id> release-v1         # Delete branch\nhf repo tag create \u003Crepo_id> v1.0                  # Create tag\nhf repo tag list \u003Crepo_id>                         # List tags\nhf repo tag delete \u003Crepo_id> v1.0                  # Delete tag\n```\n\n### Delete Files from Repo\n```bash\nhf repo-files delete \u003Crepo_id> folder\u002F             # Delete folder\nhf repo-files delete \u003Crepo_id> \"*.txt\"             # Delete with pattern\n```\n\n### Cache Management\n```bash\nhf cache ls                      # List cached repos\nhf cache ls --revisions          # Include individual revisions\nhf cache rm model\u002Fgpt2           # Remove cached repo\nhf cache rm \u003Crevision_hash>      # Remove cached revision\nhf cache prune                   # Remove detached revisions\nhf cache verify gpt2             # Verify checksums from cache\n```\n\n### Browse Hub\n```bash\n# Models\nhf models ls                                        # List top trending models\nhf models ls --search \"MiniMax\" --author MiniMaxAI  # Search models\nhf models ls --filter \"text-generation\" --limit 20  # Filter by task\nhf models info MiniMaxAI\u002FMiniMax-M2.1               # Get model info\n\n# Datasets\nhf datasets ls                                      # List top trending datasets\nhf datasets ls --search \"finepdfs\" --sort downloads # Search datasets\nhf datasets info HuggingFaceFW\u002Ffinepdfs             # Get dataset info\n\n# Spaces\nhf spaces ls                                        # List top trending spaces\nhf spaces ls --filter \"3d\" --limit 10               # Filter by 3D modeling spaces\nhf spaces info enzostvs\u002Fdeepsite                    # Get space info\n```\n\n### Jobs (Cloud Compute)\n```bash\nhf jobs run python:3.12 python script.py           # Run on CPU\nhf jobs run --flavor a10g-small \u003Cimage> \u003Ccmd>      # Run on GPU\nhf jobs run --secrets HF_TOKEN \u003Cimage> \u003Ccmd>       # With HF token\nhf jobs ps                                         # List jobs\nhf jobs logs \u003Cjob_id>                              # View logs\nhf jobs cancel \u003Cjob_id>                            # Cancel job\n```\n\n### Inference Endpoints\n```bash\nhf endpoints ls                                     # List endpoints\nhf endpoints deploy my-endpoint \\\n  --repo openai\u002Fgpt-oss-120b \\\n  --framework vllm \\\n  --accelerator gpu \\\n  --instance-size x4 \\\n  --instance-type nvidia-a10g \\\n  --region us-east-1 \\\n  --vendor aws\nhf endpoints describe my-endpoint                   # Show endpoint details\nhf endpoints pause my-endpoint                      # Pause endpoint\nhf endpoints resume my-endpoint                     # Resume endpoint\nhf endpoints scale-to-zero my-endpoint              # Scale to zero\nhf endpoints delete my-endpoint --yes               # Delete endpoint\n```\n**GPU Flavors:** `cpu-basic`, `cpu-upgrade`, `cpu-xl`, `t4-small`, `t4-medium`, `l4x1`, `l4x4`, `l40sx1`, `l40sx4`, `l40sx8`, `a10g-small`, `a10g-large`, `a10g-largex2`, `a10g-largex4`, `a100-large`, `h100`, `h100x8`\n\n## Common Patterns\n\n### Download and Use Model Locally\n```bash\n# Download to local directory for deployment\nhf download meta-llama\u002FLlama-3.2-1B-Instruct --local-dir .\u002Fmodel\n\n# Or use cache and get path\nMODEL_PATH=$(hf download meta-llama\u002FLlama-3.2-1B-Instruct --quiet)\n```\n\n### Publish Model\u002FDataset\n```bash\nhf repo create my-username\u002Fmy-model --private\nhf upload my-username\u002Fmy-model .\u002Foutput . --commit-message=\"Initial release\"\nhf repo tag create my-username\u002Fmy-model v1.0\n```\n\n### Sync Space with Local\n```bash\nhf upload my-username\u002Fmy-space . . --repo-type space \\\n  --exclude=\"logs\u002F*\" --delete=\"*\" --commit-message=\"Sync\"\n```\n\n### Check Cache Usage\n```bash\nhf cache ls                      # See all cached repos and sizes\nhf cache rm model\u002Fgpt2           # Remove a repo from cache\n```\n\n## Key Options\n\n- `--repo-type`: `model` (default), `dataset`, `space`\n- `--revision`: Branch, tag, or commit hash\n- `--token`: Override authentication\n- `--quiet`: Output only essential info (paths\u002FURLs)\n\n## References\n\n- **Complete command reference**: See [references\u002Fcommands.md](references\u002Fcommands.md)\n- **Workflow examples**: See [references\u002Fexamples.md](references\u002Fexamples.md)\n","# Hugging Face CLI\n\nThe `hf` CLI provides direct terminal access to the Hugging Face Hub for downloading, uploading, and managing repositories, cache, and compute resources.\n\n## Quick Command Reference\n\n| Task | Command |\n|------|---------|\n| Login | `hf auth login` |\n| Download model | `hf download \u003Crepo_id>` |\n| Download to folder | `hf download \u003Crepo_id> --local-dir .\u002Fpath` |\n| Upload folder | `hf upload \u003Crepo_id> . .` |\n| Create repo | `hf repo create \u003Cname>` |\n| Create tag | `hf repo tag create \u003Crepo_id> \u003Ctag>` |\n| Delete files | `hf repo-files delete \u003Crepo_id> \u003Cfiles>` |\n| List cache | `hf cache ls` |\n| Remove from cache | `hf cache rm \u003Crepo_or_revision>` |\n| List models | `hf models ls` |\n| Get model info | `hf models info \u003Cmodel_id>` |\n| List datasets | `hf datasets ls` |\n| Get dataset info | `hf datasets info \u003Cdataset_id>` |\n| List spaces | `hf spaces ls` |\n| Get space info | `hf spaces info \u003Cspace_id>` |\n| List endpoints | `hf endpoints ls` |\n| Run GPU job | `hf jobs run --flavor a10g-small \u003Cimage> \u003Ccmd>` |\n| Environment info | `hf env` |\n\n\n## Credential Setup\n\nHuggingFace token is auto-injected by openscience when connected via the dashboard.\n\n```bash\n# Verify credentials\n[ -n \"$HF_TOKEN\" ] && echo \"HF_TOKEN set\" || echo \"NOT SET\"\n```\n\nIf not set: connect HuggingFace at https:\u002F\u002Fapp.syntheticsciences.ai -> Services, then restart openscience.\n\n## Core Commands\n\n### Authentication\n```bash\nhf auth login                    # Interactive login\nhf auth login --token $HF_TOKEN  # Non-interactive\nhf auth whoami                   # Check current user\nhf auth list                     # List stored tokens\nhf auth switch                   # Switch between tokens\nhf auth logout                   # Log out\n```\n\n### Download\n```bash\nhf download \u003Crepo_id>                              # Full repo to cache\nhf download \u003Crepo_id> file.safetensors             # Specific file\nhf download \u003Crepo_id> --local-dir .\u002Fmodels         # To local directory\nhf download \u003Crepo_id> --include \"*.safetensors\"    # Filter by pattern\nhf download \u003Crepo_id> --repo-type dataset          # Dataset\nhf download \u003Crepo_id> --revision v1.0              # Specific version\n```\n\n### Upload\n```bash\nhf upload \u003Crepo_id> . .                            # Current dir to root\nhf upload \u003Crepo_id> .\u002Fmodels \u002Fweights              # Folder to path\nhf upload \u003Crepo_id> model.safetensors              # Single file\nhf upload \u003Crepo_id> . . --repo-type dataset        # Dataset\nhf upload \u003Crepo_id> . . --create-pr                # Create PR\nhf upload \u003Crepo_id> . . --commit-message=\"msg\"     # Custom message\n```\n\n### Repository Management\n```bash\nhf repo create \u003Cname>                              # Create model repo\nhf repo create \u003Cname> --repo-type dataset          # Create dataset\nhf repo create \u003Cname> --private                    # Private repo\nhf repo create \u003Cname> --repo-type space --space_sdk gradio  # Gradio space\nhf repo delete \u003Crepo_id>                           # Delete repo\nhf repo move \u003Cfrom_id> \u003Cto_id>                     # Move repo to new namespace\nhf repo settings \u003Crepo_id> --private true          # Update repo settings\nhf repo list --repo-type model                     # List repos\nhf repo branch create \u003Crepo_id> release-v1         # Create branch\nhf repo branch delete \u003Crepo_id> release-v1         # Delete branch\nhf repo tag create \u003Crepo_id> v1.0                  # Create tag\nhf repo tag list \u003Crepo_id>                         # List tags\nhf repo tag delete \u003Crepo_id> v1.0                  # Delete tag\n```\n\n### Delete Files from Repo\n```bash\nhf repo-files delete \u003Crepo_id> folder\u002F             # Delete folder\nhf repo-files delete \u003Crepo_id> \"*.txt\"             # Delete with pattern\n```\n\n### Cache Management\n```bash\nhf cache ls                      # List cached repos\nhf cache ls --revisions          # Include individual revisions\nhf cache rm model\u002Fgpt2           # Remove cached repo\nhf cache rm \u003Crevision_hash>      # Remove cached revision\nhf cache prune                   # Remove detached revisions\nhf cache verify gpt2             # Verify checksums from cache\n```\n\n### Browse Hub\n```bash\n# Models\nhf models ls                                        # List top trending models\nhf models ls --search \"MiniMax\" --author MiniMaxAI  # Search models\nhf models ls --filter \"text-generation\" --limit 20  # Filter by task\nhf models info MiniMaxAI\u002FMiniMax-M2.1               # Get model info\n\n# Datasets\nhf datasets ls                                      # List top trending datasets\nhf datasets ls --search \"finepdfs\" --sort downloads # Search datasets\nhf datasets info HuggingFaceFW\u002Ffinepdfs             # Get dataset info\n\n# Spaces\nhf spaces ls                                        # List top trending spaces\nhf spaces ls --filter \"3d\" --limit 10               # Filter by 3D modeling spaces\nhf spaces info enzostvs\u002Fdeepsite                    # Get space info\n```\n\n### Jobs (Cloud Compute)\n```bash\nhf jobs run python:3.12 python script.py           # Run on CPU\nhf jobs run --flavor a10g-small \u003Cimage> \u003Ccmd>      # Run on GPU\nhf jobs run --secrets HF_TOKEN \u003Cimage> \u003Ccmd>       # With HF token\nhf jobs ps                                         # List jobs\nhf jobs logs \u003Cjob_id>                              # View logs\nhf jobs cancel \u003Cjob_id>                            # Cancel job\n```\n\n### Inference Endpoints\n```bash\nhf endpoints ls                                     # List endpoints\nhf endpoints deploy my-endpoint \\\n  --repo openai\u002Fgpt-oss-120b \\\n  --framework vllm \\\n  --accelerator gpu \\\n  --instance-size x4 \\\n  --instance-type nvidia-a10g \\\n  --region us-east-1 \\\n  --vendor aws\nhf endpoints describe my-endpoint                   # Show endpoint details\nhf endpoints pause my-endpoint                      # Pause endpoint\nhf endpoints resume my-endpoint                     # Resume endpoint\nhf endpoints scale-to-zero my-endpoint              # Scale to zero\nhf endpoints delete my-endpoint --yes               # Delete endpoint\n```\n**GPU Flavors:** `cpu-basic`, `cpu-upgrade`, `cpu-xl`, `t4-small`, `t4-medium`, `l4x1`, `l4x4`, `l40sx1`, `l40sx4`, `l40sx8`, `a10g-small`, `a10g-large`, `a10g-largex2`, `a10g-largex4`, `a100-large`, `h100`, `h100x8`\n\n## Common Patterns\n\n### Download and Use Model Locally\n```bash\n# Download to local directory for deployment\nhf download meta-llama\u002FLlama-3.2-1B-Instruct --local-dir .\u002Fmodel\n\n# Or use cache and get path\nMODEL_PATH=$(hf download meta-llama\u002FLlama-3.2-1B-Instruct --quiet)\n```\n\n### Publish Model\u002FDataset\n```bash\nhf repo create my-username\u002Fmy-model --private\nhf upload my-username\u002Fmy-model .\u002Foutput . --commit-message=\"Initial release\"\nhf repo tag create my-username\u002Fmy-model v1.0\n```\n\n### Sync Space with Local\n```bash\nhf upload my-username\u002Fmy-space . . --repo-type space \\\n  --exclude=\"logs\u002F*\" --delete=\"*\" --commit-message=\"Sync\"\n```\n\n### Check Cache Usage\n```bash\nhf cache ls                      # See all cached repos and sizes\nhf cache rm model\u002Fgpt2           # Remove a repo from cache\n```\n\n## Key Options\n\n- `--repo-type`: `model` (default), `dataset`, `space`\n- `--revision`: Branch, tag, or commit hash\n- `--token`: Override authentication\n- `--quiet`: Output only essential info (paths\u002FURLs)\n\n## References\n\n- **Complete command reference**: See [references\u002Fcommands.md](references\u002Fcommands.md)\n- **Workflow examples**: See [references\u002Fexamples.md](references\u002Fexamples.md)\n",{"name":5,"tags":45,"author":49,"license":15,"version":50,"category":51,"description":7,"dependencies":52},[46,47,24,48],"Hugging Face","CLI","Hub","Synthetic Sciences","1.0.0","llm-tools",[53,54],"huggingface-hub","transformers","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhugging-face-cli","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add fadrienne\u002FMyGitHub --skill hugging-face-cli",{"tool":67,"command":68},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill hugging-face-cli",{"tool":70,"command":71},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill hugging-face-cli","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhugging-face-cli\u002FSKILL.md",true,[75,81],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fcommands.md","REFERENCE","参考文档",27188,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhugging-face-cli\u002Freferences\u002Fcommands.md",{"path":82,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fexamples.md",8542,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhugging-face-cli\u002Freferences\u002Fexamples.md",[86,117,144,178,205,231],{"skillNo":87,"name":88,"nameZh":6,"description":89,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":90,"categories":91,"keywords":97,"publishedAt":40},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. 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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. 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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",[150,153,154],{"categoryNo":151,"categoryName":152,"taxonomyType":95,"taxonomyTypeDesc":96},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":93,"categoryName":94,"taxonomyType":95,"taxonomyTypeDesc":96},{"categoryNo":155,"categoryName":156,"taxonomyType":157,"taxonomyTypeDesc":158},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[160,161,162,163,164,165,166,167,168,169,106,170,171,172,173,174,175,176,132,177],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":188,"publishedAt":40},"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",[184,187],{"categoryNo":185,"categoryName":186,"taxonomyType":95,"taxonomyTypeDesc":96},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":93,"categoryName":94,"taxonomyType":95,"taxonomyTypeDesc":96},[189,161,190,191,192,193,194,195,196,168,25,197,198,199,200,201,202,203,134,204],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":206,"name":207,"nameZh":6,"description":208,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":209,"categories":210,"keywords":217,"publishedAt":40},"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",[211,214],{"categoryNo":212,"categoryName":213,"taxonomyType":95,"taxonomyTypeDesc":96},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":215,"categoryName":216,"taxonomyType":157,"taxonomyTypeDesc":158},"01JBSEEDCAT000000000000030","开发工程师",[218,219,139,220,221,222,223,105,224,25,106,108,225,226,227,36,228,229,176,230],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","scripts","line","write",{"skillNo":232,"name":233,"nameZh":6,"description":234,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":235,"licenseFileUrl":6,"sourceHtmlUrl":236,"categories":237,"keywords":244,"publishedAt":40},"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",[238,241,242,243],{"categoryNo":239,"categoryName":240,"taxonomyType":95,"taxonomyTypeDesc":96},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":151,"categoryName":152,"taxonomyType":95,"taxonomyTypeDesc":96},{"categoryNo":93,"categoryName":94,"taxonomyType":95,"taxonomyTypeDesc":96},{"categoryNo":155,"categoryName":156,"taxonomyType":157,"taxonomyTypeDesc":158},[160,245,246,247,248,249,189,250,251,25,106,108,252,253,254,255,256,257,161,176],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]