Hugging Face Paper Publisher V2

Overview

Overview

This public intake copy packages plugins/antigravity-awesome-skills/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

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

Use @hugging-face-paper-publisher-v2 to handle <task>. 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

Review @hugging-face-paper-publisher-v2 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

Use @hugging-face-paper-publisher-v2 for <task>. 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

Review @hugging-face-paper-publisher-v2 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/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.

• quick_reference.md
• example_usage.md
• paper_manager.py
• example_usage.md
• quick_reference.md
• 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:

uv run scripts/paper_manager.py index \
  --arxiv-id "2301.12345"

Check If Paper Exists:

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:

uv run scripts/paper_manager.py link \
  --repo-id "username/model-name" \
  --repo-type "model" \
  --arxiv-id "2301.12345"

Add to Dataset Card:

uv run scripts/paper_manager.py link \
  --repo-id "username/dataset-name" \
  --repo-type "dataset" \
  --arxiv-id "2301.12345"

Add Multiple Papers:

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:

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:<PAPER_ID> 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:

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:

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:

uv run scripts/paper_manager.py list-my-papers

Toggle Visibility:

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:

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:

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:

uv run scripts/paper_manager.py convert \
  --input "paper.md" \
  --output "paper.html" \
  --style "modern"

Paper Template Structure

Standard Research Paper Sections:

---
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:

uv run scripts/paper_manager.py index --arxiv-id "2301.12345"

Link to Repository:

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:

uv run scripts/paper_manager.py claim \
  --arxiv-id "2301.12345" \
  --email "your.email@edu"

Manage Visibility:

uv run scripts/paper_manager.py toggle-visibility \
  --arxiv-id "2301.12345" \
  --show true|false

Create Research Article:

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:

uv run scripts/paper_manager.py convert \
  --input "paper.md" \
  --output "paper.html" \
  [--style "modern|classic"]

Check Paper Status:

uv run scripts/paper_manager.py check --arxiv-id "2301.12345"

List Your Papers:

uv run scripts/paper_manager.py list-my-papers

Search Papers:

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:

---
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<n<100K
---

# Dataset Name

Dataset introduced in [Our Paper](https://arxiv.org/abs/2301.12345).

For more details, see the [paper page](https://huggingface.co/papers/2301.12345).

The Hub automatically extracts arXiv IDs from these links and creates arxiv:2301.12345 tags.

Integration Examples

Workflow 1: Publish New Research

# 1. Create research article
uv run scripts/paper_manager.py create \
  --template "modern" \
  --title "Novel Fine-Tuning Approach" \
  --output "paper.md"

# 2. Edit paper.md with your content

# 3. Submit to arXiv (external process)
# Upload to arxiv.org, get arXiv ID

# 4. Index on Hugging Face
uv run scripts/paper_manager.py index --arxiv-id "2301.12345"

# 5. Link to your model
uv run scripts/paper_manager.py link \
  --repo-id "your-username/your-model" \
  --repo-type "model" \
  --arxiv-id "2301.12345"

# 6. Claim authorship
uv run scripts/paper_manager.py claim \
  --arxiv-id "2301.12345" \
  --email "your.email@edu"

Workflow 2: Link Existing Paper

# 1. Check if paper exists
uv run scripts/paper_manager.py check --arxiv-id "2301.12345"

# 2. Index if needed
uv run scripts/paper_manager.py index --arxiv-id "2301.12345"

# 3. Link to multiple repositories
uv run scripts/paper_manager.py link \
  --repo-id "username/model-v1" \
  --repo-type "model" \
  --arxiv-id "2301.12345"

uv run scripts/paper_manager.py link \
  --repo-id "username/training-data" \
  --repo-type "dataset" \
  --arxiv-id "2301.12345"

uv run scripts/paper_manager.py link \
  --repo-id "username/demo-space" \
  --repo-type "space" \
  --arxiv-id "2301.12345"

Workflow 3: Update Model with Paper Reference

# 1. Get current README
hf download username/model-name README.md

# 2. Add paper link
uv run scripts/paper_manager.py link \
  --repo-id "username/model-name" \
  --repo-type "model" \
  --arxiv-id "2301.12345" \
  --citation "Full citation for the paper"

# The script will:
# - Add YAML metadata if missing
# - Insert arXiv link in README
# - Add formatted citation
# - Preserve existing content

Best Practices

1. Paper Indexing

   • Index papers as soon as they're published on arXiv
   • Include full citation information in model/dataset cards
   • Use consistent paper references across related repositories

2. Metadata Management

   • Add YAML frontmatter to all model/dataset cards
   • Include proper licensing information
   • Tag with relevant task categories and domains

3. Authorship

   • Claim authorship on papers where you're listed as author
   • Use institutional email addresses for verification
   • Keep paper visibility settings updated

4. Repository Linking

   • Link papers to all relevant models, datasets, and Spaces
   • Include paper context in README descriptions
   • Add BibTeX citations for easy reference

5. Research Articles

   • Use templates consistently within projects
   • Include code and data links in papers
   • Generate web-friendly HTML versions for sharing

Advanced Usage

Batch Link Papers:

# Link multiple papers to one repository
for arxiv_id in "2301.12345" "2302.67890" "2303.11111"; do
  uv run scripts/paper_manager.py link \
    --repo-id "username/model-name" \
    --repo-type "model" \
    --arxiv-id "$arxiv_id"
done

Extract Paper Info:

# Get paper metadata from arXiv
uv run scripts/paper_manager.py info \
  --arxiv-id "2301.12345" \
  --format "json"

Generate Citation:

# Create BibTeX citation
uv run scripts/paper_manager.py citation \
  --arxiv-id "2301.12345" \
  --format "bibtex"

Validate Links:

# Check all paper links in a repository
uv run scripts/paper_manager.py validate \
  --repo-id "username/model-name" \
  --repo-type "model"

Error Handling

• Paper Not Found: arXiv ID doesn't exist or isn't indexed yet
• Permission Denied: HF_TOKEN lacks write access to repository
• Invalid YAML: Malformed metadata in README frontmatter
• Authorship Failed: Email doesn't match paper author records
• Already Claimed: Another user has claimed authorship
• Rate Limiting: Too many API requests in short time

Troubleshooting

Issue: "Paper not found on Hugging Face"

• Solution: Visit hf.co/papers/{arxiv-id} to trigger indexing

Issue: "Authorship claim not verified"

• Solution: Wait for admin review or contact HF support with proof

Issue: "arXiv tag not appearing"

• Solution: Ensure README includes proper arXiv URL format

Issue: "Cannot link to repository"

• Solution: Verify HF_TOKEN has write permissions

Issue: "Template rendering errors"

• Solution: Check markdown syntax and YAML frontmatter format

Resources and References

• Hugging Face Paper Pages: hf.co/papers
• Model Cards Guide: hf.co/docs/hub/model-cards
• Dataset Cards Guide: hf.co/docs/hub/datasets-cards
• Research Article Template: tfrere/research-article-template
• arXiv Format Guide: arxiv.org/help/submit

Integration with tfrere's Research Template

This skill complements tfrere's research article template by providing:

• Automated paper indexing workflows
• Repository linking capabilities
• Metadata management tools
• Citation generation utilities

You can use tfrere's template for writing, then use this skill to publish and link the paper on Hugging Face Hub.

Common Patterns

Pattern 1: New Paper Publication

# Write → Publish → Index → Link
uv run scripts/paper_manager.py create --template modern --output paper.md
# (Submit to arXiv)
uv run scripts/paper_manager.py index --arxiv-id "2301.12345"
uv run scripts/paper_manager.py link --repo-id "user/model" --arxiv-id "2301.12345"

Pattern 2: Existing Paper Discovery

# Search → Check → Link
uv run scripts/paper_manager.py search --query "transformers"
uv run scripts/paper_manager.py check --arxiv-id "2301.12345"
uv run scripts/paper_manager.py link --repo-id "user/model" --arxiv-id "2301.12345"

Pattern 3: Author Portfolio Management

# Claim → Verify → Organize
uv run scripts/paper_manager.py claim --arxiv-id "2301.12345"
uv run scripts/paper_manager.py list-my-papers
uv run scripts/paper_manager.py toggle-visibility --arxiv-id "2301.12345" --show true

API Integration

Python Script Example:

from scripts.paper_manager import PaperManager

pm = PaperManager(hf_token="your_token")

# Index paper
pm.index_paper("2301.12345")

# Link to model
pm.link_paper(
    repo_id="username/model",
    repo_type="model",
    arxiv_id="2301.12345",
    citation="Full citation text"
)

# Check status
status = pm.check_paper("2301.12345")
print(status)

Future Enhancements

Planned features for future versions:

• Support for non-arXiv papers (conference proceedings, journals)
• Automatic citation formatting from DOI
• Paper comparison and versioning tools
• Collaborative paper writing features
• Integration with LaTeX workflows
• Automated figure and table extraction
• Paper metrics and impact tracking

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.