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.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
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.
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
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.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
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.
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
---
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
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:-enlicense:apache-2.0tags:-text-generation-transformers-llmlibrary_name:transformers---
# Model NameThismodelisbasedontheapproachdescribedin [OurPaper](https://arxiv.org/abs/2301.12345).#### Imported: Citation```bibtex@article{doe2023paper,title={YourPaperTitle},author={Doe,JaneandSmith,John},journal={arXivpreprintarXiv: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
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
Metadata Management
Add YAML frontmatter to all model/dataset cards
Include proper licensing information
Tag with relevant task categories and domains
Authorship
Claim authorship on papers where you're listed as author
Use institutional email addresses for verification
Keep paper visibility settings updated
Repository Linking
Link papers to all relevant models, datasets, and Spaces
Include paper context in README descriptions
Add BibTeX citations for easy reference
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 repositoryfor 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"
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.
