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Supports creating paper pages, linking papers to models\u002Fdatasets, claiming authorship, and generating professional markdown-based research articles.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"huggingface","Hugging Face","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fhuggingface.png",[12,16,18,21],{"name":13,"slug":14,"type":15},"Research","research","tag",{"name":9,"slug":17,"type":15},"hugging-face",{"name":19,"slug":20,"type":15},"Writing","writing",{"name":22,"slug":23,"type":15},"Documentation","documentation",10861,"https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills","2026-04-06T18:25:45.282806",null,721,[],{"repoUrl":25,"stars":24,"forks":28,"topics":31,"description":32},[],"Give your agents the power of the Hugging Face ecosystem","https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Fhuggingface-paper-publisher","---\nname: huggingface-paper-publisher\ndescription: Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models\u002Fdatasets, claiming authorship, and generating professional markdown-based research articles.\n---\n\n# Overview\nThis skill provides comprehensive tools for AI engineers and researchers to publish, manage, and link research papers on the Hugging Face Hub. It streamlines the workflow from paper creation to publication, including integration with arXiv, model\u002Fdataset linking, and authorship management.\n\n## Integration with HF Ecosystem\n- **Paper Pages**: Index and discover papers on Hugging Face Hub\n- **arXiv Integration**: Automatic paper indexing from arXiv IDs\n- **Model\u002FDataset Linking**: Connect papers to relevant artifacts through metadata\n- **Authorship Verification**: Claim and verify paper authorship\n- **Research Article Template**: Generate professional, modern scientific papers\n\n# Version\n1.0.0\n\n# Dependencies\nThe included script uses PEP 723 inline dependencies. Prefer `uv run` over\nmanual environment setup.\n\n- huggingface_hub>=0.26.0\n- pyyaml>=6.0.3\n- requests>=2.32.5\n- markdown>=3.5.0\n- python-dotenv>=1.2.1\n\n# Core Capabilities\n\n## 1. Paper Page Management\n- **Index Papers**: Add papers to Hugging Face from arXiv\n- **Claim Authorship**: Verify and claim authorship on published papers\n- **Manage Visibility**: Control which papers appear on your profile\n- **Paper Discovery**: Find and explore papers in the HF ecosystem\n\n## 2. Link Papers to Artifacts\n- **Model Cards**: Add paper citations to model metadata\n- **Dataset Cards**: Link papers to datasets via README\n- **Automatic Tagging**: Hub auto-generates arxiv:\u003CPAPER_ID> tags\n- **Citation Management**: Maintain proper attribution and references\n\n## 3. Research Article Creation\n- **Markdown Templates**: Generate professional paper formatting\n- **Modern Design**: Clean, readable research article layouts\n- **Dynamic TOC**: Automatic table of contents generation\n- **Section Structure**: Standard scientific paper organization\n- **LaTeX Math**: Support for equations and technical notation\n\n## 4. Metadata Management\n- **YAML Frontmatter**: Proper model\u002Fdataset card metadata\n- **Citation Tracking**: Maintain paper references across repositories\n- **Version Control**: Track paper updates and revisions\n- **Multi-Paper Support**: Link multiple papers to single artifacts\n\n# Usage Instructions\n\nThe skill includes Python scripts in `scripts\u002F` for paper publishing operations.\n\n### Prerequisites\n- Run scripts with `uv run` (dependencies are resolved from the script header)\n- Set `HF_TOKEN` environment variable with Write-access token\n\n> **All paths are relative to the directory containing this SKILL.md\nfile.**\n> Before running any script, first `cd` to that directory or use the full\npath.\n\n\n### Method 1: Index Paper from arXiv\n\nAdd a paper to Hugging Face Paper Pages from arXiv.\n\n**Basic Usage:**\n```bash\nuv run scripts\u002Fpaper_manager.py index \\\n  --arxiv-id \"2301.12345\"\n```\n\n**Check If Paper Exists:**\n```bash\nuv run scripts\u002Fpaper_manager.py check \\\n  --arxiv-id \"2301.12345\"\n```\n\n**Direct URL Access:**\nYou can also visit `https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F{arxiv-id}` directly to index a paper.\n\n### Method 2: Link Paper to Model\u002FDataset\n\nAdd paper references to model or dataset README with proper YAML metadata.\n\n**Add to Model Card:**\n```bash\nuv run scripts\u002Fpaper_manager.py link \\\n  --repo-id \"username\u002Fmodel-name\" \\\n  --repo-type \"model\" \\\n  --arxiv-id \"2301.12345\"\n```\n\n**Add to Dataset Card:**\n```bash\nuv run scripts\u002Fpaper_manager.py link \\\n  --repo-id \"username\u002Fdataset-name\" \\\n  --repo-type \"dataset\" \\\n  --arxiv-id \"2301.12345\"\n```\n\n**Add Multiple Papers:**\n```bash\nuv run scripts\u002Fpaper_manager.py link \\\n  --repo-id \"username\u002Fmodel-name\" \\\n  --repo-type \"model\" \\\n  --arxiv-ids \"2301.12345,2302.67890,2303.11111\"\n```\n\n**With Custom Citation:**\n```bash\nuv run scripts\u002Fpaper_manager.py link \\\n  --repo-id \"username\u002Fmodel-name\" \\\n  --repo-type \"model\" \\\n  --arxiv-id \"2301.12345\" \\\n  --citation \"$(cat citation.txt)\"\n```\n\n#### How Linking Works\n\nWhen you add an arXiv paper link to a model or dataset README:\n1. The Hub extracts the arXiv ID from the link\n2. A tag `arxiv:\u003CPAPER_ID>` is automatically added to the repository\n3. Users can click the tag to view the Paper Page\n4. The Paper Page shows all models\u002Fdatasets citing this paper\n5. Papers are discoverable through filters and search\n\n### Method 3: Claim Authorship\n\nVerify your authorship on papers published on Hugging Face.\n\n**Start Claim Process:**\n```bash\nuv run scripts\u002Fpaper_manager.py claim \\\n  --arxiv-id \"2301.12345\" \\\n  --email \"your.email@institution.edu\"\n```\n\n**Manual Process:**\n1. Navigate to your paper's page: `https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F{arxiv-id}`\n2. Find your name in the author list\n3. Click your name and select \"Claim authorship\"\n4. Wait for admin team verification\n\n**Check Authorship Status:**\n```bash\nuv run scripts\u002Fpaper_manager.py check-authorship \\\n  --arxiv-id \"2301.12345\"\n```\n\n### Method 4: Manage Paper Visibility\n\nControl which verified papers appear on your public profile.\n\n**List Your Papers:**\n```bash\nuv run scripts\u002Fpaper_manager.py list-my-papers\n```\n\n**Toggle Visibility:**\n```bash\nuv run scripts\u002Fpaper_manager.py toggle-visibility \\\n  --arxiv-id \"2301.12345\" \\\n  --show true\n```\n\n**Manage in Settings:**\nNavigate to your account settings → Papers section to toggle \"Show on profile\" for each paper.\n\n### Method 5: Create Research Article\n\nGenerate a professional markdown-based research paper using modern templates.\n\n**Create from Template:**\n```bash\nuv run scripts\u002Fpaper_manager.py create \\\n  --template \"standard\" \\\n  --title \"Your Paper Title\" \\\n  --output \"paper.md\"\n```\n\n**Available Templates:**\n- `standard` - Traditional scientific paper structure\n- `modern` - Clean, web-friendly format inspired by Distill\n- `arxiv` - arXiv-style formatting\n- `ml-report` - Machine learning experiment report\n\n**Generate Complete Paper:**\n```bash\nuv run scripts\u002Fpaper_manager.py create \\\n  --template \"modern\" \\\n  --title \"Fine-Tuning Large Language Models with LoRA\" \\\n  --authors \"Jane Doe, John Smith\" \\\n  --abstract \"$(cat abstract.txt)\" \\\n  --output \"paper.md\"\n```\n\n**Convert to HTML:**\n```bash\nuv run scripts\u002Fpaper_manager.py convert \\\n  --input \"paper.md\" \\\n  --output \"paper.html\" \\\n  --style \"modern\"\n```\n\n### Paper Template Structure\n\n**Standard Research Paper Sections:**\n```markdown\n---\ntitle: Your Paper Title\nauthors: Jane Doe, John Smith\naffiliations: University X, Lab Y\ndate: 2025-01-15\narxiv: 2301.12345\ntags: [machine-learning, nlp, fine-tuning]\n---\n\n# Abstract\nBrief summary of the paper...\n\n# 1. Introduction\nBackground and motivation...\n\n# 2. Related Work\nPrevious research and context...\n\n# 3. Methodology\nApproach and implementation...\n\n# 4. Experiments\nSetup, datasets, and procedures...\n\n# 5. Results\nFindings and analysis...\n\n# 6. Discussion\nInterpretation and implications...\n\n# 7. Conclusion\nSummary and future work...\n\n# References\n```\n\n**Modern Template Features:**\n- Dynamic table of contents\n- Responsive design for web viewing\n- Code syntax highlighting\n- Interactive figures and charts\n- Math equation rendering (LaTeX)\n- Citation management\n- Author affiliation linking\n\n### Commands Reference\n\n**Index Paper:**\n```bash\nuv run scripts\u002Fpaper_manager.py index --arxiv-id \"2301.12345\"\n```\n\n**Link to Repository:**\n```bash\nuv run scripts\u002Fpaper_manager.py link \\\n  --repo-id \"username\u002Frepo-name\" \\\n  --repo-type \"model|dataset|space\" \\\n  --arxiv-id \"2301.12345\" \\\n  [--citation \"Full citation text\"] \\\n  [--create-pr]\n```\n\n**Claim Authorship:**\n```bash\nuv run scripts\u002Fpaper_manager.py claim \\\n  --arxiv-id \"2301.12345\" \\\n  --email \"your.email@edu\"\n```\n\n**Manage Visibility:**\n```bash\nuv run scripts\u002Fpaper_manager.py toggle-visibility \\\n  --arxiv-id \"2301.12345\" \\\n  --show true|false\n```\n\n**Create Research Article:**\n```bash\nuv run scripts\u002Fpaper_manager.py create \\\n  --template \"standard|modern|arxiv|ml-report\" \\\n  --title \"Paper Title\" \\\n  [--authors \"Author1, Author2\"] \\\n  [--abstract \"Abstract text\"] \\\n  [--output \"filename.md\"]\n```\n\n**Convert Markdown to HTML:**\n```bash\nuv run scripts\u002Fpaper_manager.py convert \\\n  --input \"paper.md\" \\\n  --output \"paper.html\" \\\n  [--style \"modern|classic\"]\n```\n\n**Check Paper Status:**\n```bash\nuv run scripts\u002Fpaper_manager.py check --arxiv-id \"2301.12345\"\n```\n\n**List Your Papers:**\n```bash\nuv run scripts\u002Fpaper_manager.py list-my-papers\n```\n\n**Search Papers:**\n```bash\nuv run scripts\u002Fpaper_manager.py search --query \"transformer attention\"\n```\n\n### YAML Metadata Format\n\nWhen linking papers to models or datasets, proper YAML frontmatter is required:\n\n**Model Card Example:**\n```yaml\n---\nlanguage:\n  - en\nlicense: apache-2.0\ntags:\n  - text-generation\n  - transformers\n  - llm\nlibrary_name: transformers\n---\n\n# Model Name\n\nThis model is based on the approach described in [Our Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.12345).\n\n## Citation\n\n```bibtex\n@article{doe2023paper,\n  title={Your Paper Title},\n  author={Doe, Jane and Smith, John},\n  journal={arXiv preprint arXiv:2301.12345},\n  year={2023}\n}\n```\n```\n\n**Dataset Card Example:**\n```yaml\n---\nlanguage:\n  - en\nlicense: cc-by-4.0\ntask_categories:\n  - text-generation\n  - question-answering\nsize_categories:\n  - 10K\u003Cn\u003C100K\n---\n\n# Dataset Name\n\nDataset introduced in [Our Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.12345).\n\nFor more details, see the [paper page](https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2301.12345).\n```\n\nThe Hub automatically extracts arXiv IDs from these links and creates `arxiv:2301.12345` tags.\n\n### Integration Examples\n\n**Workflow 1: Publish New Research**\n```bash\n# 1. Create research article\nuv run scripts\u002Fpaper_manager.py create \\\n  --template \"modern\" \\\n  --title \"Novel Fine-Tuning Approach\" \\\n  --output \"paper.md\"\n\n# 2. Edit paper.md with your content\n\n# 3. Submit to arXiv (external process)\n# Upload to arxiv.org, get arXiv ID\n\n# 4. Index on Hugging Face\nuv run scripts\u002Fpaper_manager.py index --arxiv-id \"2301.12345\"\n\n# 5. Link to your model\nuv run scripts\u002Fpaper_manager.py link \\\n  --repo-id \"your-username\u002Fyour-model\" \\\n  --repo-type \"model\" \\\n  --arxiv-id \"2301.12345\"\n\n# 6. Claim authorship\nuv run scripts\u002Fpaper_manager.py claim \\\n  --arxiv-id \"2301.12345\" \\\n  --email \"your.email@edu\"\n```\n\n**Workflow 2: Link Existing Paper**\n```bash\n# 1. Check if paper exists\nuv run scripts\u002Fpaper_manager.py check --arxiv-id \"2301.12345\"\n\n# 2. Index if needed\nuv run scripts\u002Fpaper_manager.py index --arxiv-id \"2301.12345\"\n\n# 3. Link to multiple repositories\nuv run scripts\u002Fpaper_manager.py link \\\n  --repo-id \"username\u002Fmodel-v1\" \\\n  --repo-type \"model\" \\\n  --arxiv-id \"2301.12345\"\n\nuv run scripts\u002Fpaper_manager.py link \\\n  --repo-id \"username\u002Ftraining-data\" \\\n  --repo-type \"dataset\" \\\n  --arxiv-id \"2301.12345\"\n\nuv run scripts\u002Fpaper_manager.py link \\\n  --repo-id \"username\u002Fdemo-space\" \\\n  --repo-type \"space\" \\\n  --arxiv-id \"2301.12345\"\n```\n\n**Workflow 3: Update Model with Paper Reference**\n```bash\n# 1. Get current README\nhf download username\u002Fmodel-name README.md\n\n# 2. Add paper link\nuv run scripts\u002Fpaper_manager.py link \\\n  --repo-id \"username\u002Fmodel-name\" \\\n  --repo-type \"model\" \\\n  --arxiv-id \"2301.12345\" \\\n  --citation \"Full citation for the paper\"\n\n# The script will:\n# - Add YAML metadata if missing\n# - Insert arXiv link in README\n# - Add formatted citation\n# - Preserve existing content\n```\n\n### Best Practices\n\n1. **Paper Indexing**\n   - Index papers as soon as they're published on arXiv\n   - Include full citation information in model\u002Fdataset cards\n   - Use consistent paper references across related repositories\n\n2. **Metadata Management**\n   - Add YAML frontmatter to all model\u002Fdataset cards\n   - Include proper licensing information\n   - Tag with relevant task categories and domains\n\n3. **Authorship**\n   - Claim authorship on papers where you're listed as author\n   - Use institutional email addresses for verification\n   - Keep paper visibility settings updated\n\n4. **Repository Linking**\n   - Link papers to all relevant models, datasets, and Spaces\n   - Include paper context in README descriptions\n   - Add BibTeX citations for easy reference\n\n5. **Research Articles**\n   - Use templates consistently within projects\n   - Include code and data links in papers\n   - Generate web-friendly HTML versions for sharing\n\n### Advanced Usage\n\n**Batch Link Papers:**\n```bash\n# Link multiple papers to one repository\nfor arxiv_id in \"2301.12345\" \"2302.67890\" \"2303.11111\"; do\n  uv run scripts\u002Fpaper_manager.py link \\\n    --repo-id \"username\u002Fmodel-name\" \\\n    --repo-type \"model\" \\\n    --arxiv-id \"$arxiv_id\"\ndone\n```\n\n**Extract Paper Info:**\n```bash\n# Get paper metadata from arXiv\nuv run scripts\u002Fpaper_manager.py info \\\n  --arxiv-id \"2301.12345\" \\\n  --format \"json\"\n```\n\n**Generate Citation:**\n```bash\n# Create BibTeX citation\nuv run scripts\u002Fpaper_manager.py citation \\\n  --arxiv-id \"2301.12345\" \\\n  --format \"bibtex\"\n```\n\n**Validate Links:**\n```bash\n# Check all paper links in a repository\nuv run scripts\u002Fpaper_manager.py validate \\\n  --repo-id \"username\u002Fmodel-name\" \\\n  --repo-type \"model\"\n```\n\n### Error Handling\n\n- **Paper Not Found**: arXiv ID doesn't exist or isn't indexed yet\n- **Permission Denied**: HF_TOKEN lacks write access to repository\n- **Invalid YAML**: Malformed metadata in README frontmatter\n- **Authorship Failed**: Email doesn't match paper author records\n- **Already Claimed**: Another user has claimed authorship\n- **Rate Limiting**: Too many API requests in short time\n\n### Troubleshooting\n\n**Issue**: \"Paper not found on Hugging Face\"\n- **Solution**: Visit `hf.co\u002Fpapers\u002F{arxiv-id}` to trigger indexing\n\n**Issue**: \"Authorship claim not verified\"\n- **Solution**: Wait for admin review or contact HF support with proof\n\n**Issue**: \"arXiv tag not appearing\"\n- **Solution**: Ensure README includes proper arXiv URL format\n\n**Issue**: \"Cannot link to repository\"\n- **Solution**: Verify HF_TOKEN has write permissions\n\n**Issue**: \"Template rendering errors\"\n- **Solution**: Check markdown syntax and YAML frontmatter format\n\n### Resources and References\n\n- **Hugging Face Paper Pages**: [hf.co\u002Fpapers](https:\u002F\u002Fhuggingface.co\u002Fpapers)\n- **Model Cards Guide**: [hf.co\u002Fdocs\u002Fhub\u002Fmodel-cards](https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fen\u002Fmodel-cards)\n- **Dataset Cards Guide**: [hf.co\u002Fdocs\u002Fhub\u002Fdatasets-cards](https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fen\u002Fdatasets-cards)\n- **Research Article Template**: [tfrere\u002Fresearch-article-template](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Ftfrere\u002Fresearch-article-template)\n- **arXiv Format Guide**: [arxiv.org\u002Fhelp\u002Fsubmit](https:\u002F\u002Farxiv.org\u002Fhelp\u002Fsubmit)\n\n### Integration with tfrere's Research Template\n\nThis skill complements [tfrere's research article template](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Ftfrere\u002Fresearch-article-template) by providing:\n\n- Automated paper indexing workflows\n- Repository linking capabilities\n- Metadata management tools\n- Citation generation utilities\n\nYou can use tfrere's template for writing, then use this skill to publish and link the paper on Hugging Face Hub.\n\n### Common Patterns\n\n**Pattern 1: New Paper Publication**\n```bash\n# Write → Publish → Index → Link\nuv run scripts\u002Fpaper_manager.py create --template modern --output paper.md\n# (Submit to arXiv)\nuv run scripts\u002Fpaper_manager.py index --arxiv-id \"2301.12345\"\nuv run scripts\u002Fpaper_manager.py link --repo-id \"user\u002Fmodel\" --arxiv-id \"2301.12345\"\n```\n\n**Pattern 2: Existing Paper Discovery**\n```bash\n# Search → Check → Link\nuv run scripts\u002Fpaper_manager.py search --query \"transformers\"\nuv run scripts\u002Fpaper_manager.py check --arxiv-id \"2301.12345\"\nuv run scripts\u002Fpaper_manager.py link --repo-id \"user\u002Fmodel\" --arxiv-id \"2301.12345\"\n```\n\n**Pattern 3: Author Portfolio Management**\n```bash\n# Claim → Verify → Organize\nuv run scripts\u002Fpaper_manager.py claim --arxiv-id \"2301.12345\"\nuv run scripts\u002Fpaper_manager.py list-my-papers\nuv run scripts\u002Fpaper_manager.py toggle-visibility --arxiv-id \"2301.12345\" --show true\n```\n\n### API Integration\n\n**Python Script Example:**\n```python\nfrom scripts.paper_manager import PaperManager\n\npm = PaperManager(hf_token=\"your_token\")\n\n# Index paper\npm.index_paper(\"2301.12345\")\n\n# Link to model\npm.link_paper(\n    repo_id=\"username\u002Fmodel\",\n    repo_type=\"model\",\n    arxiv_id=\"2301.12345\",\n    citation=\"Full citation text\"\n)\n\n# Check status\nstatus = pm.check_paper(\"2301.12345\")\nprint(status)\n```\n\n### Future Enhancements\n\nPlanned features for future versions:\n- Support for non-arXiv papers (conference proceedings, journals)\n- Automatic citation formatting from DOI\n- Paper comparison and versioning tools\n- Collaborative paper writing features\n- Integration with LaTeX workflows\n- Automated figure and table extraction\n- Paper metrics and impact 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Organize\n",{"type":41,"tag":496,"props":5745,"children":5746},{"class":498,"line":531},[5747,5751,5755,5759,5763,5767,5771,5775],{"type":41,"tag":496,"props":5748,"children":5749},{"style":503},[5750],{"type":47,"value":506},{"type":41,"tag":496,"props":5752,"children":5753},{"style":509},[5754],{"type":47,"value":512},{"type":41,"tag":496,"props":5756,"children":5757},{"style":509},[5758],{"type":47,"value":517},{"type":41,"tag":496,"props":5760,"children":5761},{"style":509},[5762],{"type":47,"value":1192},{"type":41,"tag":496,"props":5764,"children":5765},{"style":509},[5766],{"type":47,"value":2435},{"type":41,"tag":496,"props":5768,"children":5769},{"style":540},[5770],{"type":47,"value":543},{"type":41,"tag":496,"props":5772,"children":5773},{"style":509},[5774],{"type":47,"value":548},{"type":41,"tag":496,"props":5776,"children":5777},{"style":540},[5778],{"type":47,"value":553},{"type":41,"tag":496,"props":5780,"children":5781},{"class":498,"line":708},[5782,5786,5790,5794],{"type":41,"tag":496,"props":5783,"children":5784},{"style":503},[5785],{"type":47,"value":506},{"type":41,"tag":496,"props":5787,"children":5788},{"style":509},[5789],{"type":47,"value":512},{"type":41,"tag":496,"props":5791,"children":5792},{"style":509},[5793],{"type":47,"value":517},{"type":41,"tag":496,"props":5795,"children":5796},{"style":509},[5797],{"type":47,"value":1380},{"type":41,"tag":496,"props":5799,"children":5800},{"class":498,"line":734},[5801,5805,5809,5813,5817,5821,5825,5829,5833,5838],{"type":41,"tag":496,"props":5802,"children":5803},{"style":503},[5804],{"type":47,"value":506},{"type":41,"tag":496,"props":5806,"children":5807},{"style":509},[5808],{"type":47,"value":512},{"type":41,"tag":496,"props":5810,"children":5811},{"style":509},[5812],{"type":47,"value":517},{"type":41,"tag":496,"props":5814,"children":5815},{"style":509},[5816],{"type":47,"value":1415},{"type":41,"tag":496,"props":5818,"children":5819},{"style":509},[5820],{"type":47,"value":2435},{"type":41,"tag":496,"props":5822,"children":5823},{"style":540},[5824],{"type":47,"value":543},{"type":41,"tag":496,"props":5826,"children":5827},{"style":509},[5828],{"type":47,"value":548},{"type":41,"tag":496,"props":5830,"children":5831},{"style":540},[5832],{"type":47,"value":701},{"type":41,"tag":496,"props":5834,"children":5835},{"style":509},[5836],{"type":47,"value":5837}," 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pm.check_paper(\"2301.12345\")\nprint(status)\n","python",[5862],{"type":41,"tag":141,"props":5863,"children":5864},{"__ignoreMap":490},[5865,5873,5880,5888,5895,5903,5911,5918,5926,5934,5942,5950,5958,5966,5974,5981,5989,5997],{"type":41,"tag":496,"props":5866,"children":5867},{"class":498,"line":499},[5868],{"type":41,"tag":496,"props":5869,"children":5870},{},[5871],{"type":47,"value":5872},"from scripts.paper_manager import PaperManager\n",{"type":41,"tag":496,"props":5874,"children":5875},{"class":498,"line":531},[5876],{"type":41,"tag":496,"props":5877,"children":5878},{"emptyLinePlaceholder":2087},[5879],{"type":47,"value":2090},{"type":41,"tag":496,"props":5881,"children":5882},{"class":498,"line":708},[5883],{"type":41,"tag":496,"props":5884,"children":5885},{},[5886],{"type":47,"value":5887},"pm = 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var(--shiki-dark-text-decoration);}",{"items":6061,"total":6233},[6062,6083,6099,6114,6128,6141,6154,6169,6183,6193,6206,6220],{"slug":6063,"name":6063,"fn":6064,"description":6065,"org":6066,"tags":6067,"stars":6080,"repoUrl":6081,"updatedAt":6082},"train-sentence-transformers","train sentence-transformers models","Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder; dense or static embedding model; for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker; pair scoring for two-stage retrieval \u002F pair classification), and `SparseEncoder` (SPLADE, sparse embedding model; for learned-sparse retrieval). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6068,6071,6072,6075,6077],{"name":6069,"slug":6070,"type":15},"Deep Learning","deep-learning",{"name":9,"slug":17,"type":15},{"name":6073,"slug":6074,"type":15},"LLM","llm",{"name":6076,"slug":5860,"type":15},"Python",{"name":6078,"slug":6079,"type":15},"Search","search",18914,"https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fsentence-transformers","2026-05-08T05:09:16.820066",{"slug":6084,"name":6084,"fn":6085,"description":6086,"org":6087,"tags":6088,"stars":6096,"repoUrl":6097,"updatedAt":6098},"trl-training","train and fine-tune LLMs with TRL","Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6089,6092,6093,6094,6095],{"name":6090,"slug":6091,"type":15},"AI Infrastructure","ai-infrastructure",{"name":6069,"slug":6070,"type":15},{"name":9,"slug":17,"type":15},{"name":6073,"slug":6074,"type":15},{"name":6076,"slug":5860,"type":15},18850,"https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Ftrl","2026-04-06T18:25:32.746828",{"slug":6100,"name":6100,"fn":6101,"description":6102,"org":6103,"tags":6104,"stars":24,"repoUrl":25,"updatedAt":6113},"hf-cli","manage Hugging Face Hub resources via CLI","Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6105,6108,6111,6112],{"name":6106,"slug":6107,"type":15},"CLI","cli",{"name":6109,"slug":6110,"type":15},"Datasets","datasets",{"name":9,"slug":17,"type":15},{"name":6073,"slug":6074,"type":15},"2026-04-06T18:25:34.020855",{"slug":6115,"name":6115,"fn":6116,"description":6117,"org":6118,"tags":6119,"stars":24,"repoUrl":25,"updatedAt":6127},"hf-cloud-aws-context-discovery","discover local AWS environment context","Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. Use this skill before any other AWS work — deploying to SageMaker, creating resources, calling AWS APIs, or anything that touches an AWS account. Use it especially when the user has not specified a region or profile explicitly, when they say things like \"use my AWS account\", \"deploy to AWS\", \"use my profile\", or when about to make any AWS CLI or SDK call. Never guess the region or account ID — always use this skill to read it from the local configuration first.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6120,6123,6124],{"name":6121,"slug":6122,"type":15},"AWS","aws",{"name":6106,"slug":6107,"type":15},{"name":6125,"slug":6126,"type":15},"Configuration","configuration","2026-07-08T05:55:33.716099",{"slug":6129,"name":6129,"fn":6130,"description":6131,"org":6132,"tags":6133,"stars":24,"repoUrl":25,"updatedAt":6140},"hf-cloud-python-env-setup","set up Python environments for AWS","Set up an isolated Python environment for SageMaker \u002F AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to \"set up the environment\". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6134,6135,6136,6139],{"name":6090,"slug":6091,"type":15},{"name":6121,"slug":6122,"type":15},{"name":6137,"slug":6138,"type":15},"Engineering","engineering",{"name":6076,"slug":5860,"type":15},"2026-07-08T05:55:32.505017",{"slug":6142,"name":6142,"fn":6143,"description":6144,"org":6145,"tags":6146,"stars":24,"repoUrl":25,"updatedAt":6153},"hf-cloud-sagemaker-deployment-planner","plan model deployments to Amazon SageMaker","Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like \"deploy a model\", \"host this LLM on AWS\", \"serve this embedding model\", \"deploy a reranker\", \"deploy a text-to-image \u002F diffusion model\", \"host this for async inference\", \"create an endpoint\", \"serve my fine-tuned model\", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. \"I just want to get this running on AWS, you figure it out\"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image \u002F diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6147,6148,6149,6152],{"name":6090,"slug":6091,"type":15},{"name":6121,"slug":6122,"type":15},{"name":6150,"slug":6151,"type":15},"Deployment","deployment",{"name":9,"slug":17,"type":15},"2026-07-08T05:55:37.387689",{"slug":6155,"name":6155,"fn":6156,"description":6157,"org":6158,"tags":6159,"stars":24,"repoUrl":25,"updatedAt":6168},"hf-cloud-sagemaker-iam-preflight","configure SageMaker IAM roles","Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6160,6161,6162,6165],{"name":6090,"slug":6091,"type":15},{"name":6121,"slug":6122,"type":15},{"name":6163,"slug":6164,"type":15},"Permissions","permissions",{"name":6166,"slug":6167,"type":15},"Security","security","2026-07-08T05:55:34.948771",{"slug":6170,"name":6170,"fn":6171,"description":6172,"org":6173,"tags":6174,"stars":24,"repoUrl":25,"updatedAt":6182},"hf-cloud-sagemaker-production-defaults","create production-ready SageMaker endpoints","Create a SageMaker endpoint (real-time or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints and deploy_async.py for async endpoints (with genuine scale-to-zero support). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6175,6176,6177,6178,6179],{"name":6090,"slug":6091,"type":15},{"name":6121,"slug":6122,"type":15},{"name":6150,"slug":6151,"type":15},{"name":9,"slug":17,"type":15},{"name":6180,"slug":6181,"type":15},"Monitoring","monitoring","2026-07-08T05:55:38.664702",{"slug":6184,"name":6184,"fn":6185,"description":6186,"org":6187,"tags":6188,"stars":24,"repoUrl":25,"updatedAt":6192},"hf-cloud-serving-image-selection","select SageMaker serving containers","Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says \"deploy this LLM\", \"host this HuggingFace model\", \"serve this fine-tuned model\", \"deploy this embedding model\", \"host a reranker\", \"serve a sentence-transformers model\", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings\u002Fcross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6189,6190,6191],{"name":6090,"slug":6091,"type":15},{"name":6121,"slug":6122,"type":15},{"name":6150,"slug":6151,"type":15},"2026-07-08T05:55:36.173465",{"slug":6194,"name":6194,"fn":6195,"description":6196,"org":6197,"tags":6198,"stars":24,"repoUrl":25,"updatedAt":6205},"hf-mcp","access Hugging Face Hub via MCP","Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6199,6200,6201,6202],{"name":6109,"slug":6110,"type":15},{"name":9,"slug":17,"type":15},{"name":6073,"slug":6074,"type":15},{"name":6203,"slug":6204,"type":15},"MCP","mcp","2026-04-06T18:25:50.364185",{"slug":6207,"name":6207,"fn":6208,"description":6209,"org":6210,"tags":6211,"stars":24,"repoUrl":25,"updatedAt":6219},"hf-mem","estimate memory for Hugging Face models","Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6212,6213,6214,6215,6216],{"name":6090,"slug":6091,"type":15},{"name":6106,"slug":6107,"type":15},{"name":9,"slug":17,"type":15},{"name":6073,"slug":6074,"type":15},{"name":6217,"slug":6218,"type":15},"Performance","performance","2026-06-13T07:23:57.101435",{"slug":6221,"name":6221,"fn":6222,"description":6223,"org":6224,"tags":6225,"stars":24,"repoUrl":25,"updatedAt":6232},"huggingface-best","find and compare Hugging Face models","Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: \"best model for X\", \"what model should I use for\", \"top models for [task]\", \"which model runs on my laptop\u002Fmachine\u002Fdevice\", \"recommend a model for\", \"what LLM should I use for\", \"compare models for\", \"what's state of the art for\", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6226,6229,6230,6231],{"name":6227,"slug":6228,"type":15},"Analytics","analytics",{"name":9,"slug":17,"type":15},{"name":6073,"slug":6074,"type":15},{"name":13,"slug":14,"type":15},"2026-04-24T05:09:45.870658",37,{"items":6235,"total":2236},[6236,6243,6249,6256,6263,6270,6278],{"slug":6100,"name":6100,"fn":6101,"description":6102,"org":6237,"tags":6238,"stars":24,"repoUrl":25,"updatedAt":6113},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6239,6240,6241,6242],{"name":6106,"slug":6107,"type":15},{"name":6109,"slug":6110,"type":15},{"name":9,"slug":17,"type":15},{"name":6073,"slug":6074,"type":15},{"slug":6115,"name":6115,"fn":6116,"description":6117,"org":6244,"tags":6245,"stars":24,"repoUrl":25,"updatedAt":6127},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6246,6247,6248],{"name":6121,"slug":6122,"type":15},{"name":6106,"slug":6107,"type":15},{"name":6125,"slug":6126,"type":15},{"slug":6129,"name":6129,"fn":6130,"description":6131,"org":6250,"tags":6251,"stars":24,"repoUrl":25,"updatedAt":6140},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6252,6253,6254,6255],{"name":6090,"slug":6091,"type":15},{"name":6121,"slug":6122,"type":15},{"name":6137,"slug":6138,"type":15},{"name":6076,"slug":5860,"type":15},{"slug":6142,"name":6142,"fn":6143,"description":6144,"org":6257,"tags":6258,"stars":24,"repoUrl":25,"updatedAt":6153},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6259,6260,6261,6262],{"name":6090,"slug":6091,"type":15},{"name":6121,"slug":6122,"type":15},{"name":6150,"slug":6151,"type":15},{"name":9,"slug":17,"type":15},{"slug":6155,"name":6155,"fn":6156,"description":6157,"org":6264,"tags":6265,"stars":24,"repoUrl":25,"updatedAt":6168},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6266,6267,6268,6269],{"name":6090,"slug":6091,"type":15},{"name":6121,"slug":6122,"type":15},{"name":6163,"slug":6164,"type":15},{"name":6166,"slug":6167,"type":15},{"slug":6170,"name":6170,"fn":6171,"description":6172,"org":6271,"tags":6272,"stars":24,"repoUrl":25,"updatedAt":6182},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6273,6274,6275,6276,6277],{"name":6090,"slug":6091,"type":15},{"name":6121,"slug":6122,"type":15},{"name":6150,"slug":6151,"type":15},{"name":9,"slug":17,"type":15},{"name":6180,"slug":6181,"type":15},{"slug":6184,"name":6184,"fn":6185,"description":6186,"org":6279,"tags":6280,"stars":24,"repoUrl":25,"updatedAt":6192},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6281,6282,6283],{"name":6090,"slug":6091,"type":15},{"name":6121,"slug":6122,"type":15},{"name":6150,"slug":6151,"type":15}]