--- name: hugging-face-paper-publisher description: "Overview workflow skill. Use this skill when the user needs Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off." version: "0.0.1" category: data-ai tags: ["hugging-face-paper-publisher", "publish", "and", "manage", "research", "papers", "hugging", "face"] complexity: advanced risk: safe tools: ["codex-cli", "claude-code", "cursor", "gemini-cli", "opencode"] source: community author: "sickn33" date_added: "2026-04-15" date_updated: "2026-04-25" --- # Overview ## Overview This public intake copy packages `plugins/antigravity-awesome-skills-claude/skills/hugging-face-paper-publisher` from `https://github.com/sickn33/antigravity-awesome-skills` into the native Omni Skills editorial shape without hiding its origin. Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow. This intake keeps the copied upstream files intact and uses the `external_source` block in `metadata.json` plus `ORIGIN.md` as the provenance anchor for review. # Overview Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Integration with HF Ecosystem, 1. Paper Page Management, 2. Link Papers to Artifacts, 3. Research Article Creation, 4. Metadata Management, Citation. ## When to Use This Skill Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request. - Use this skill when a user wants to publish, link, index, or manage research papers on the Hugging Face Hub. - This 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/dataset linking, and authorship management. - Use when the request clearly matches the imported source intent: Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles. - Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch. - Use when provenance needs to stay visible in the answer, PR, or review packet. ## Operating Table | Situation | Start here | Why it matters | | --- | --- | --- | | First-time use | `metadata.json` | Confirms repository, branch, commit, and imported path through the `external_source` block before touching the copied workflow | | Provenance review | `ORIGIN.md` | Gives reviewers a plain-language audit trail for the imported source | | Workflow execution | `references/quick_reference.md` | Starts with the smallest copied file that materially changes execution | | Supporting context | `examples/example_usage.md` | Adds the next most relevant copied source file without loading the entire package | | Handoff decision | `## Related Skills` | Helps the operator switch to a stronger native skill when the task drifts | ## Workflow This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow. 1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task. 2. Read the overview and provenance files before loading any copied upstream support files. 3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request. 4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes. 5. Validate the result against the upstream expectations and the evidence you can point to in the copied files. 6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity. 7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify. ### Imported Workflow Notes #### Imported: Integration with HF Ecosystem - **Paper Pages**: Index and discover papers on Hugging Face Hub - **arXiv Integration**: Automatic paper indexing from arXiv IDs - **Model/Dataset Linking**: Connect papers to relevant artifacts through metadata - **Authorship Verification**: Claim and verify paper authorship - **Research Article Template**: Generate professional, modern scientific papers # Version 1.0.0 # Dependencies The included script uses PEP 723 inline dependencies. Prefer `uv run` over manual environment setup. - huggingface_hub>=0.26.0 - pyyaml>=6.0.3 - requests>=2.32.5 - markdown>=3.5.0 - python-dotenv>=1.2.1 # Core Capabilities ## Examples ### Example 1: Ask for the upstream workflow directly ```text Use @hugging-face-paper-publisher to handle . Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer. ``` **Explanation:** This is the safest starting point when the operator needs the imported workflow, but not the entire repository. ### Example 2: Ask for a provenance-grounded review ```text Review @hugging-face-paper-publisher against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why. ``` **Explanation:** Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection. ### Example 3: Narrow the copied support files before execution ```text Use @hugging-face-paper-publisher for . Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding. ``` **Explanation:** This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default. ### Example 4: Build a reviewer packet ```text Review @hugging-face-paper-publisher using the copied upstream files plus provenance, then summarize any gaps before merge. ``` **Explanation:** This is useful when the PR is waiting for human review and you want a repeatable audit packet. ## Best Practices Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution. - Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support. - Prefer the smallest useful set of support files so the workflow stays auditable and fast to review. - Keep provenance, source commit, and imported file paths visible in notes and PR descriptions. - Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate. - Treat generated examples as scaffolding; adapt them to the concrete task before execution. - Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant. ## Troubleshooting ### Problem: The operator skipped the imported context and answered too generically **Symptoms:** The result ignores the upstream workflow in `plugins/antigravity-awesome-skills-claude/skills/hugging-face-paper-publisher`, fails to mention provenance, or does not use any copied source files at all. **Solution:** Re-open `metadata.json`, `ORIGIN.md`, and the most relevant copied upstream files. Check the `external_source` block first, then restate the provenance before continuing. ### Problem: The imported workflow feels incomplete during review **Symptoms:** Reviewers can see the generated `SKILL.md`, but they cannot quickly tell which references, examples, or scripts matter for the current task. **Solution:** Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it. ### Problem: The task drifted into a different specialization **Symptoms:** The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. **Solution:** Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind. ## Related Skills - `@00-andruia-consultant` - Use when the work is better handled by that native specialization after this imported skill establishes context. - `@00-andruia-consultant-v2` - Use when the work is better handled by that native specialization after this imported skill establishes context. - `@10-andruia-skill-smith` - Use when the work is better handled by that native specialization after this imported skill establishes context. - `@10-andruia-skill-smith-v2` - Use when the work is better handled by that native specialization after this imported skill establishes context. ## Additional Resources Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding. | Resource family | What it gives the reviewer | Example path | | --- | --- | --- | | `references` | copied reference notes, guides, or background material from upstream | `references/quick_reference.md` | | `examples` | worked examples or reusable prompts copied from upstream | `examples/example_usage.md` | | `scripts` | upstream helper scripts that change execution or validation | `scripts/paper_manager.py` | | `agents` | routing or delegation notes that are genuinely part of the imported package | `agents/n/a` | | `assets` | supporting assets or schemas copied from the source package | `assets/n/a` | - [quick_reference.md](references/quick_reference.md) - [example_usage.md](examples/example_usage.md) - [paper_manager.py](scripts/paper_manager.py) - [example_usage.md](examples/example_usage.md) - [quick_reference.md](references/quick_reference.md) - [paper_manager.py](scripts/paper_manager.py) ### Imported Reference Notes #### Imported: 1. Paper Page Management - **Index Papers**: Add papers to Hugging Face from arXiv - **Claim Authorship**: Verify and claim authorship on published papers - **Manage Visibility**: Control which papers appear on your profile - **Paper Discovery**: Find and explore papers in the HF ecosystem #### Imported: 2. Link Papers to Artifacts - **Model Cards**: Add paper citations to model metadata - **Dataset Cards**: Link papers to datasets via README - **Automatic Tagging**: Hub auto-generates arxiv: tags - **Citation Management**: Maintain proper attribution and references #### Imported: 3. Research Article Creation - **Markdown Templates**: Generate professional paper formatting - **Modern Design**: Clean, readable research article layouts - **Dynamic TOC**: Automatic table of contents generation - **Section Structure**: Standard scientific paper organization - **LaTeX Math**: Support for equations and technical notation #### Imported: 4. Metadata Management - **YAML Frontmatter**: Proper model/dataset card metadata - **Citation Tracking**: Maintain paper references across repositories - **Version Control**: Track paper updates and revisions - **Multi-Paper Support**: Link multiple papers to single artifacts # Usage Instructions The skill includes Python scripts in `scripts/` for paper publishing operations. ### Prerequisites - Run scripts with `uv run` (dependencies are resolved from the script header) - Set `HF_TOKEN` environment variable with Write-access token > **All paths are relative to the directory containing this SKILL.md file.** > Before running any script, first `cd` to that directory or use the full path. ### Method 1: Index Paper from arXiv Add a paper to Hugging Face Paper Pages from arXiv. **Basic Usage:** ```bash uv run scripts/paper_manager.py index \ --arxiv-id "2301.12345" ``` **Check If Paper Exists:** ```bash uv run scripts/paper_manager.py check \ --arxiv-id "2301.12345" ``` **Direct URL Access:** You can also visit `https://huggingface.co/papers/{arxiv-id}` directly to index a paper. ### Method 2: Link Paper to Model/Dataset Add paper references to model or dataset README with proper YAML metadata. **Add to Model Card:** ```bash uv run scripts/paper_manager.py link \ --repo-id "username/model-name" \ --repo-type "model" \ --arxiv-id "2301.12345" ``` **Add to Dataset Card:** ```bash uv run scripts/paper_manager.py link \ --repo-id "username/dataset-name" \ --repo-type "dataset" \ --arxiv-id "2301.12345" ``` **Add Multiple Papers:** ```bash uv run scripts/paper_manager.py link \ --repo-id "username/model-name" \ --repo-type "model" \ --arxiv-ids "2301.12345,2302.67890,2303.11111" ``` **With Custom Citation:** ```bash uv run scripts/paper_manager.py link \ --repo-id "username/model-name" \ --repo-type "model" \ --arxiv-id "2301.12345" \ --citation "$(cat citation.txt)" ``` #### How Linking Works When you add an arXiv paper link to a model or dataset README: 1. The Hub extracts the arXiv ID from the link 2. A tag `arxiv:` is automatically added to the repository 3. Users can click the tag to view the Paper Page 4. The Paper Page shows all models/datasets citing this paper 5. Papers are discoverable through filters and search ### Method 3: Claim Authorship Verify your authorship on papers published on Hugging Face. **Start Claim Process:** ```bash uv run scripts/paper_manager.py claim \ --arxiv-id "2301.12345" \ --email "your.email@institution.edu" ``` **Manual Process:** 1. Navigate to your paper's page: `https://huggingface.co/papers/{arxiv-id}` 2. Find your name in the author list 3. Click your name and select "Claim authorship" 4. Wait for admin team verification **Check Authorship Status:** ```bash uv run scripts/paper_manager.py check-authorship \ --arxiv-id "2301.12345" ``` ### Method 4: Manage Paper Visibility Control which verified papers appear on your public profile. **List Your Papers:** ```bash uv run scripts/paper_manager.py list-my-papers ``` **Toggle Visibility:** ```bash uv run scripts/paper_manager.py toggle-visibility \ --arxiv-id "2301.12345" \ --show true ``` **Manage in Settings:** Navigate to your account settings → Papers section to toggle "Show on profile" for each paper. ### Method 5: Create Research Article Generate a professional markdown-based research paper using modern templates. **Create from Template:** ```bash uv run scripts/paper_manager.py create \ --template "standard" \ --title "Your Paper Title" \ --output "paper.md" ``` **Available Templates:** - `standard` - Traditional scientific paper structure - `modern` - Clean, web-friendly format inspired by Distill - `arxiv` - arXiv-style formatting - `ml-report` - Machine learning experiment report **Generate Complete Paper:** ```bash uv run scripts/paper_manager.py create \ --template "modern" \ --title "Fine-Tuning Large Language Models with LoRA" \ --authors "Jane Doe, John Smith" \ --abstract "$(cat abstract.txt)" \ --output "paper.md" ``` **Convert to HTML:** ```bash uv run scripts/paper_manager.py convert \ --input "paper.md" \ --output "paper.html" \ --style "modern" ``` ### Paper Template Structure **Standard Research Paper Sections:** ```markdown --- title: Your Paper Title authors: Jane Doe, John Smith affiliations: University X, Lab Y date: 2025-01-15 arxiv: 2301.12345 tags: [machine-learning, nlp, fine-tuning] --- # Abstract Brief summary of the paper... # 1. Introduction Background and motivation... # 2. Related Work Previous research and context... # 3. Methodology Approach and implementation... # 4. Experiments Setup, datasets, and procedures... # 5. Results Findings and analysis... # 6. Discussion Interpretation and implications... # 7. Conclusion Summary and future work... # References ``` **Modern Template Features:** - Dynamic table of contents - Responsive design for web viewing - Code syntax highlighting - Interactive figures and charts - Math equation rendering (LaTeX) - Citation management - Author affiliation linking ### Commands Reference **Index Paper:** ```bash uv run scripts/paper_manager.py index --arxiv-id "2301.12345" ``` **Link to Repository:** ```bash uv run scripts/paper_manager.py link \ --repo-id "username/repo-name" \ --repo-type "model|dataset|space" \ --arxiv-id "2301.12345" \ [--citation "Full citation text"] \ [--create-pr] ``` **Claim Authorship:** ```bash uv run scripts/paper_manager.py claim \ --arxiv-id "2301.12345" \ --email "your.email@edu" ``` **Manage Visibility:** ```bash uv run scripts/paper_manager.py toggle-visibility \ --arxiv-id "2301.12345" \ --show true|false ``` **Create Research Article:** ```bash uv run scripts/paper_manager.py create \ --template "standard|modern|arxiv|ml-report" \ --title "Paper Title" \ [--authors "Author1, Author2"] \ [--abstract "Abstract text"] \ [--output "filename.md"] ``` **Convert Markdown to HTML:** ```bash uv run scripts/paper_manager.py convert \ --input "paper.md" \ --output "paper.html" \ [--style "modern|classic"] ``` **Check Paper Status:** ```bash uv run scripts/paper_manager.py check --arxiv-id "2301.12345" ``` **List Your Papers:** ```bash uv run scripts/paper_manager.py list-my-papers ``` **Search Papers:** ```bash uv run scripts/paper_manager.py search --query "transformer attention" ``` ### YAML Metadata Format When linking papers to models or datasets, proper YAML frontmatter is required: **Model Card Example:** ```yaml --- language: - en license: apache-2.0 tags: - text-generation - transformers - llm library_name: transformers --- # Model Name This model is based on the approach described in [Our Paper](https://arxiv.org/abs/2301.12345). #### Imported: Citation ```bibtex @article{doe2023paper, title={Your Paper Title}, author={Doe, Jane and Smith, John}, journal={arXiv preprint arXiv:2301.12345}, year={2023} } ``` ``` **Dataset Card Example:** ```yaml --- language: - en license: cc-by-4.0 task_categories: - text-generation - question-answering size_categories: - 10K