huggingface-paper-publisher

Overview 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. 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 - R…

)\n _ARXIV_ID_LEGACY = re.compile(r'^[a-zA-Z\\-]+/\\d{7}(v\\d+)?

Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.

)\n\n @staticmethod\n def _clean_arxiv_id(arxiv_id: str) -> str:\n \"\"\"Clean, normalize, and validate arXiv ID.\n\n Raises:\n ValueError: If the cleaned ID does not match a valid arXiv format.\n \"\"\"\n # Remove common prefixes and whitespace\n arxiv_id = arxiv_id.strip()\n arxiv_id = re.sub(r'^(arxiv:|arXiv:)', '', arxiv_id, flags=re.IGNORECASE)\n arxiv_id = re.sub(r'https?://arxiv\\.org/(abs|pdf)/', '', arxiv_id)\n arxiv_id = arxiv_id.replace('.pdf', '')\n\n # Validate format\n if not (PaperManager._ARXIV_ID_MODERN.match(arxiv_id)\n or PaperManager._ARXIV_ID_LEGACY.match(arxiv_id)):\n raise ValueError(\n f\"Invalid arXiv ID: {arxiv_id!r}. \"\n \"Expected format: YYMM.NNNNN[vN] or category/YYMMNNN[vN]\"\n )\n\n return arxiv_id\n\n @staticmethod\n def _escape_yaml_value(value: str) -> str:\n \"\"\"Escape a string for safe use as a YAML scalar value.\n\n Wraps in double quotes and escapes internal quotes and backslashes\n to prevent YAML injection via crafted titles/authors.\n \"\"\"\n value = value.replace('\\\\', '\\\\\\\\').replace('\"', '\\\\\"')\n return f'\"{value}\"'\n\n @staticmethod\n def _sanitize_text(text: str) -> str:\n \"\"\"Sanitize untrusted text for safe inclusion in Markdown/YAML output.\n\n Normalizes whitespace, strips control characters, and neutralizes\n markdown code-fence breakout and YAML document delimiters.\n \"\"\"\n # Remove control characters (keep newlines and tabs)\n text = re.sub(r'[\\x00-\\x08\\x0b\\x0c\\x0e-\\x1f\\x7f]', '', text)\n # Normalize whitespace runs (collapse multiple spaces/tabs, preserve single newlines)\n text = re.sub(r'[^\\S\\n]+', ' ', text)\n text = re.sub(r'\\n{3,}', '\\n\\n', text)\n # Neutralize markdown code fence breakout\n text = text.replace('```', r'\\`\\`\\`')\n # Neutralize YAML document delimiters at line start\n text = re.sub(r'^---', r'\\\\---', text, flags=re.MULTILINE)\n return text.strip()\n\n\ndef main():\n \"\"\"Main CLI entry point.\"\"\"\n parser = argparse.ArgumentParser(\n description=\"Paper Manager for Hugging Face Hub\",\n formatter_class=argparse.RawDescriptionHelpFormatter\n )\n\n subparsers = parser.add_subparsers(dest=\"command\", help=\"Command to execute\")\n\n # Index command\n index_parser = subparsers.add_parser(\"index\", help=\"Index a paper from arXiv\")\n index_parser.add_argument(\"--arxiv-id\", required=True, help=\"arXiv paper ID\")\n\n # Check command\n check_parser = subparsers.add_parser(\"check\", help=\"Check if paper exists\")\n check_parser.add_argument(\"--arxiv-id\", required=True, help=\"arXiv paper ID\")\n\n # Link command\n link_parser = subparsers.add_parser(\"link\", help=\"Link paper to repository\")\n link_parser.add_argument(\"--repo-id\", required=True, help=\"Repository ID\")\n link_parser.add_argument(\"--repo-type\", default=\"model\", choices=[\"model\", \"dataset\", \"space\"])\n link_parser.add_argument(\"--arxiv-id\", help=\"Single arXiv ID\")\n link_parser.add_argument(\"--arxiv-ids\", help=\"Comma-separated arXiv IDs\")\n link_parser.add_argument(\"--citation\", help=\"Full citation text\")\n link_parser.add_argument(\"--create-pr\", action=\"store_true\", help=\"Create PR instead of direct commit\")\n\n # Create command\n create_parser = subparsers.add_parser(\"create\", help=\"Create research article\")\n create_parser.add_argument(\"--template\", required=True, help=\"Template name\")\n create_parser.add_argument(\"--title\", required=True, help=\"Paper title\")\n create_parser.add_argument(\"--output\", required=True, help=\"Output filename\")\n create_parser.add_argument(\"--authors\", help=\"Comma-separated authors\")\n create_parser.add_argument(\"--abstract\", help=\"Abstract text\")\n\n # Info command\n info_parser = subparsers.add_parser(\"info\", help=\"Get paper information\")\n info_parser.add_argument(\"--arxiv-id\", required=True, help=\"arXiv paper ID\")\n info_parser.add_argument(\"--format\", default=\"json\", choices=[\"json\", \"text\"])\n\n # Citation command\n citation_parser = subparsers.add_parser(\"citation\", help=\"Generate citation\")\n citation_parser.add_argument(\"--arxiv-id\", required=True, help=\"arXiv paper ID\")\n citation_parser.add_argument(\"--format\", default=\"bibtex\", choices=[\"bibtex\", \"apa\", \"mla\"])\n\n # Search command\n search_parser = subparsers.add_parser(\"search\", help=\"Search papers\")\n search_parser.add_argument(\"--query\", required=True, help=\"Search query\")\n\n args = parser.parse_args()\n\n if not args.command:\n parser.print_help()\n sys.exit(1)\n\n # Initialize manager\n manager = PaperManager()\n\n # Execute command\n if args.command == \"index\":\n result = manager.index_paper(args.arxiv_id)\n print(json.dumps(result, indent=2))\n\n elif args.command == \"check\":\n result = manager.check_paper(args.arxiv_id)\n print(json.dumps(result, indent=2))\n\n elif args.command == \"link\":\n arxiv_ids = []\n if args.arxiv_id:\n arxiv_ids.append(args.arxiv_id)\n if args.arxiv_ids:\n arxiv_ids.extend([id.strip() for id in args.arxiv_ids.split(\",\")])\n\n if not arxiv_ids:\n print(\"Error: Must provide --arxiv-id or --arxiv-ids\")\n sys.exit(1)\n\n for arxiv_id in arxiv_ids:\n result = manager.link_paper_to_repo(\n repo_id=args.repo_id,\n arxiv_id=arxiv_id,\n repo_type=args.repo_type,\n citation=args.citation,\n create_pr=args.create_pr\n )\n print(json.dumps(result, indent=2))\n\n elif args.command == \"create\":\n result = manager.create_research_article(\n template=args.template,\n title=args.title,\n output=args.output,\n authors=args.authors,\n abstract=args.abstract\n )\n print(json.dumps(result, indent=2))\n\n elif args.command == \"info\":\n result = manager.get_arxiv_info(args.arxiv_id)\n if args.format == \"json\":\n print(json.dumps(result, indent=2))\n else:\n if \"error\" in result:\n print(f\"Error: {result['error']}\")\n else:\n print(f\"Title: {result.get('title')}\")\n print(f\"Authors: {', '.join(result.get('authors', []))}\")\n print(f\"arXiv URL: {result.get('arxiv_url')}\")\n print(f\"\\nAbstract:\\n{result.get('abstract')}\")\n\n elif args.command == \"citation\":\n citation = manager.generate_citation(args.arxiv_id, args.format)\n print(citation)\n\n elif args.command == \"search\":\n print(f\"Searching for: {args.query}\")\n print(\"Search functionality coming soon!\")\n print(f\"Visit: https://huggingface.co/papers?search={args.query}\")\n\n\nif __name__ == \"__main__\":\n main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":21926,"content_sha256":"94a4a662c0e9e13d5bb636527030ddfbd5489e9937e410d144d0276d326a9e05"},{"filename":"templates/arxiv.md","content":"---\ntitle: {{TITLE}}\nauthors: {{AUTHORS}}\ndate: {{DATE}}\narxiv:\nlayout: arxiv\n---\n\n# {{TITLE}}\n\n\u003cdiv class=\"arxiv-header\">\n\n**{{AUTHORS}}**\n\n*Submitted to arXiv: {{DATE}}*\n\n\u003c/div>\n\n---\n\n**Abstract**—{{ABSTRACT}}\n\n**Index Terms**—Machine Learning, Deep Learning, Neural Networks\n\n---\n\n## I. INTRODUCTION\n\n**T**HIS paper presents [brief overview of the contribution]. The main contributions of this work are:\n\n- Contribution 1: Description\n- Contribution 2: Description\n- Contribution 3: Description\n\nThe rest of this paper is organized as follows: Section II reviews related work, Section III describes the proposed methodology, Section IV presents experimental results, and Section V concludes the paper.\n\n## II. RELATED WORK\n\n### A. Subarea 1\n\nDiscussion of relevant prior work in subarea 1.\n\n### B. Subarea 2\n\nDiscussion of relevant prior work in subarea 2.\n\n### C. Comparison with Prior Art\n\nTable comparing existing methods:\n\n| Method | Year | Approach | Limitation |\n|--------|------|----------|------------|\n| Method A [1] | 2020 | Description | Issue |\n| Method B [2] | 2021 | Description | Issue |\n| Method C [3] | 2023 | Description | Issue |\n\n## III. METHODOLOGY\n\n### A. Problem Formulation\n\nLet $X = \\{x_1, x_2, ..., x_n\\}$ be the input space and $Y = \\{y_1, y_2, ..., y_m\\}$ be the output space. We aim to learn a function $f: X \\rightarrow Y$ that minimizes:\n\n$\n\\mathcal{L}(\\theta) = \\sum_{i=1}^{N} \\ell(f(x_i; \\theta), y_i) + \\lambda R(\\theta)\n$\n\nwhere $\\theta$ represents model parameters, $\\ell$ is the loss function, and $R(\\theta)$ is a regularization term.\n\n### B. Model Architecture\n\nDescribe the model architecture in detail.\n\n**Input Layer**: Description\n\n**Hidden Layers**: Let $h^{(l)}$ denote the activation of layer $l$:\n\n$\nh^{(l)} = \\sigma(W^{(l)}h^{(l-1)} + b^{(l)})\n$\n\nwhere $\\sigma$ is the activation function, $W^{(l)}$ is the weight matrix, and $b^{(l)}$ is the bias vector.\n\n**Output Layer**: Description\n\n### C. Training Algorithm\n\n**Algorithm 1**: Training Procedure\n\n```\n1: Input: Training data D = {(xi, yi)}\n2: Initialize parameters θ\n3: for epoch = 1 to max_epochs do\n4: for each mini-batch B ⊂ D do\n5: Compute loss: L(θ) = 1/|B| Σ ℓ(f(xi; θ), yi)\n6: Update: θ ← θ - η∇θL(θ)\n7: end for\n8: end for\n9: Return: Trained parameters θ*\n```\n\n### D. Complexity Analysis\n\n**Time Complexity**: The training algorithm has time complexity $O(NTE)$ where $N$ is the dataset size, $T$ is the number of epochs, and $E$ is the per-example computation cost.\n\n**Space Complexity**: The model requires $O(P)$ space where $P$ is the number of parameters.\n\n## IV. EXPERIMENTS\n\n### A. Experimental Setup\n\n**Datasets**: We evaluate on the following benchmarks:\n\n1. **Dataset A**: Description (size, splits, characteristics)\n2. **Dataset B**: Description\n3. **Dataset C**: Description\n\n**Baselines**: We compare against:\n\n- Baseline 1 [4]: Description\n- Baseline 2 [5]: Description\n- Baseline 3 [6]: Description\n\n**Evaluation Metrics**: Performance is measured using:\n\n- Metric 1: Definition\n- Metric 2: Definition\n- Metric 3: Definition\n\n**Implementation Details**: All experiments are conducted using:\n\n- Framework: PyTorch 2.0\n- Hardware: NVIDIA A100 GPUs\n- Hyperparameters: Learning rate $\\eta = 10^{-4}$, batch size $B = 32$, epochs $T = 100$\n\n### B. Quantitative Results\n\n**TABLE I: MAIN RESULTS**\n\n| Method | Dataset A | Dataset B | Dataset C | Average |\n|--------|-----------|-----------|-----------|---------|\n| Baseline 1 [4] | 82.3 | 78.5 | 80.1 | 80.3 |\n| Baseline 2 [5] | 85.7 | 82.1 | 83.9 | 83.9 |\n| Baseline 3 [6] | 88.1 | 85.3 | 86.7 | 86.7 |\n| **Ours** | **91.2** | **88.9** | **90.1** | **90.1** |\n\nOur method achieves state-of-the-art performance across all three benchmarks, with an average improvement of 3.4 percentage points over the previous best method.\n\n### C. Ablation Study\n\n**TABLE II: ABLATION STUDY RESULTS**\n\n| Configuration | Dataset A | Δ |\n|---------------|-----------|---|\n| Full Model | 91.2 | - |\n| w/o Component A | 88.7 | -2.5 |\n| w/o Component B | 89.4 | -1.8 |\n| w/o Component C | 90.5 | -0.7 |\n\nThe ablation study demonstrates that all components contribute to the final performance, with Component A having the largest impact.\n\n### D. Qualitative Analysis\n\n**Fig. 1**: Visualization of learned representations using t-SNE projection.\n\n**Fig. 2**: Example predictions showing correct classifications and failure cases.\n\n### E. Computational Efficiency\n\n**TABLE III: COMPUTATIONAL REQUIREMENTS**\n\n| Method | Parameters | FLOPs | Inference (ms) |\n|--------|------------|-------|----------------|\n| Baseline 1 [4] | 50M | 10G | 8.2 |\n| Baseline 2 [5] | 100M | 25G | 15.7 |\n| Baseline 3 [6] | 200M | 50G | 28.3 |\n| **Ours** | **80M** | **18G** | **12.1** |\n\nOur method achieves superior performance while maintaining reasonable computational costs.\n\n## V. DISCUSSION\n\n### A. Analysis of Results\n\nThe experimental results demonstrate that [analysis].\n\n### B. Limitations\n\nCurrent limitations include:\n\n1. Limitation 1: Description\n2. Limitation 2: Description\n3. Limitation 3: Description\n\n### C. Broader Impact\n\nPotential applications include:\n\n- Application 1: Description\n- Application 2: Description\n- Application 3: Description\n\n**Ethical Considerations**: [Discussion of potential risks and mitigation strategies]\n\n## VI. CONCLUSION\n\nThis paper presented {{TITLE}}, which achieves [main achievement]. The key contributions are:\n\n1. Contribution 1: Summary\n2. Contribution 2: Summary\n3. Contribution 3: Summary\n\nFuture work will focus on [future directions].\n\n## ACKNOWLEDGMENTS\n\nThe authors thank [acknowledgments]. This work was supported by [funding sources].\n\n## REFERENCES\n\n[1] Author A et al., \"Paper Title,\" *Conference Name*, 2020.\n\n[2] Author B et al., \"Paper Title,\" *Journal Name*, vol. X, no. Y, pp. Z-W, 2021.\n\n[3] Author C et al., \"Paper Title,\" *arXiv preprint arXiv:XXXX.XXXXX*, 2023.\n\n[4] Author D et al., \"Baseline 1 Paper,\" *Conference*, 2019.\n\n[5] Author E et al., \"Baseline 2 Paper,\" *Conference*, 2021.\n\n[6] Author F et al., \"Baseline 3 Paper,\" *Conference*, 2023.\n\n---\n\n## APPENDIX A: ADDITIONAL EXPERIMENTS\n\nSupplementary experimental results.\n\n## APPENDIX B: PROOF OF THEOREM\n\n**Theorem 1**: Statement of theorem.\n\n**Proof**: Detailed proof.\n\n## APPENDIX C: HYPERPARAMETERS\n\nComplete list of hyperparameters used in all experiments:\n\n| Hyperparameter | Value | Description |\n|----------------|-------|-------------|\n| Learning rate | $10^{-4}$ | Initial learning rate |\n| Batch size | 32 | Training batch size |\n| Epochs | 100 | Number of training epochs |\n| Optimizer | AdamW | Optimization algorithm |\n| Weight decay | 0.01 | L2 regularization coefficient |\n| Warmup steps | 1000 | LR warmup duration |\n| Dropout | 0.1 | Dropout probability |\n\n---\n\n\u003cstyle>\n.arxiv-header {\n text-align: center;\n margin-bottom: 2em;\n}\n\nbody {\n font-family: 'Computer Modern', serif;\n line-height: 1.6;\n}\n\nh1 {\n text-align: center;\n font-size: 1.8em;\n margin-top: 1em;\n}\n\nh2 {\n font-size: 1.3em;\n margin-top: 1.5em;\n font-weight: bold;\n}\n\nh3 {\n font-size: 1.1em;\n font-style: italic;\n margin-top: 1em;\n}\n\ntable {\n margin: 1em auto;\n border-collapse: collapse;\n}\n\nth, td {\n border: 1px solid #000;\n padding: 0.5em;\n text-align: center;\n}\n\u003c/style>\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":7318,"content_sha256":"37bf032a324008fec59b3f1f10412ffb9de8933bdf9ed03668459a2035f7894b"},{"filename":"templates/ml-report.md","content":"---\ntitle: {{TITLE}}\nauthors: {{AUTHORS}}\ndate: {{DATE}}\ntype: ml-experiment-report\ntags: [machine-learning, experiment-report]\n---\n\n# {{TITLE}}\n\n**Machine Learning Experiment Report**\n\n**Researchers**: {{AUTHORS}}\n**Date**: {{DATE}}\n**Status**: Draft / Final / In Review\n\n---\n\n## Executive Summary\n\n{{ABSTRACT}}\n\n### Key Findings\n- Finding 1\n- Finding 2\n- Finding 3\n\n### Recommendations\n- Recommendation 1\n- Recommendation 2\n\n---\n\n## 1. Objective\n\n### 1.1 Research Question\n\nWhat specific question are we trying to answer?\n\n### 1.2 Success Criteria\n\nHow will we measure success?\n\n- **Metric 1**: Target value\n- **Metric 2**: Target value\n- **Metric 3**: Target value\n\n### 1.3 Constraints\n\n- Computational budget\n- Time constraints\n- Data availability\n\n---\n\n## 2. Dataset\n\n### 2.1 Data Description\n\n| Property | Value |\n|----------|-------|\n| **Name** | Dataset name |\n| **Source** | Origin of data |\n| **Size** | Number of examples |\n| **Features** | Feature count and types |\n| **Target** | What we're predicting |\n| **License** | Usage rights |\n\n### 2.2 Data Splits\n\n| Split | Size | Percentage |\n|-------|------|------------|\n| Train | X examples | Y% |\n| Validation | X examples | Y% |\n| Test | X examples | Y% |\n\n### 2.3 Data Quality\n\n- **Missing Values**: Analysis and handling\n- **Outliers**: Detection and treatment\n- **Imbalance**: Class distribution\n- **Preprocessing**: Transformations applied\n\n### 2.4 Exploratory Analysis\n\nKey insights from data exploration:\n\n1. Pattern 1\n2. Pattern 2\n3. Pattern 3\n\n---\n\n## 3. Model\n\n### 3.1 Architecture\n\nDescribe the model architecture:\n\n```\nInput → Layer 1 → Layer 2 → ... → Output\n```\n\n### 3.2 Model Specifications\n\n| Component | Configuration |\n|-----------|--------------|\n| **Type** | Model family |\n| **Parameters** | Total count |\n| **Layers** | Number and types |\n| **Activation** | Functions used |\n| **Dropout** | Regularization rate |\n\n### 3.3 Baseline Models\n\nWhat are we comparing against?\n\n1. **Baseline 1**: Simple baseline (e.g., majority class)\n2. **Baseline 2**: Standard approach (e.g., logistic regression)\n3. **Baseline 3**: Previous best method\n\n---\n\n## 4. Training\n\n### 4.1 Hyperparameters\n\n| Hyperparameter | Value | Rationale |\n|----------------|-------|-----------|\n| Learning Rate | 1e-4 | Tuned via grid search |\n| Batch Size | 32 | GPU memory constraint |\n| Epochs | 100 | Based on validation |\n| Optimizer | AdamW | Standard for transformers |\n| Weight Decay | 0.01 | Regularization |\n| LR Schedule | Cosine | Smooth convergence |\n\n### 4.2 Training Process\n\n```python\n# Training pseudocode\nfor epoch in range(num_epochs):\n train_loss = train_one_epoch(model, train_loader)\n val_loss = validate(model, val_loader)\n if val_loss \u003c best_loss:\n save_checkpoint(model)\n```\n\n### 4.3 Computational Resources\n\n| Resource | Specification |\n|----------|--------------|\n| **Hardware** | GPU model and count |\n| **Memory** | RAM and VRAM |\n| **Training Time** | Hours/days |\n| **Cost** | Estimated compute cost |\n\n### 4.4 Training Curves\n\nInclude plots of:\n- Training loss over time\n- Validation loss over time\n- Learning rate schedule\n- Other relevant metrics\n\n---\n\n## 5. Results\n\n### 5.1 Quantitative Results\n\n| Model | Accuracy | Precision | Recall | F1 | AUC |\n|-------|----------|-----------|--------|-------|-----|\n| Baseline 1 | 0.65 | 0.64 | 0.66 | 0.65 | 0.70 |\n| Baseline 2 | 0.78 | 0.77 | 0.79 | 0.78 | 0.82 |\n| **Ours** | **0.89** | **0.88** | **0.90** | **0.89** | **0.93** |\n\n### 5.2 Statistical Significance\n\n- **P-value**: Statistical test results\n- **Confidence Intervals**: 95% CI for key metrics\n- **Multiple Runs**: Mean ± std over N runs\n\n### 5.3 Per-Class Performance\n\n| Class | Precision | Recall | F1 | Support |\n|-------|-----------|--------|-----|---------|\n| Class 1 | 0.90 | 0.88 | 0.89 | 500 |\n| Class 2 | 0.87 | 0.91 | 0.89 | 450 |\n| Class 3 | 0.88 | 0.89 | 0.88 | 550 |\n\n### 5.4 Qualitative Results\n\n#### Success Cases\n\nExamples where the model performs well.\n\n#### Failure Cases\n\nExamples where the model fails and why.\n\n---\n\n## 6. Analysis\n\n### 6.1 Ablation Study\n\n| Configuration | Score | Change |\n|---------------|-------|--------|\n| Full Model | 0.89 | - |\n| - Feature Set A | 0.85 | -0.04 |\n| - Feature Set B | 0.87 | -0.02 |\n| - Augmentation | 0.86 | -0.03 |\n\n### 6.2 Error Analysis\n\nWhat types of errors is the model making?\n\n1. **Error Type 1**: Frequency and cause\n2. **Error Type 2**: Frequency and cause\n3. **Error Type 3**: Frequency and cause\n\n### 6.3 Feature Importance\n\nWhich features matter most?\n\n| Feature | Importance | Notes |\n|---------|------------|-------|\n| Feature 1 | 0.35 | Most predictive |\n| Feature 2 | 0.28 | Secondary signal |\n| Feature 3 | 0.15 | Marginal impact |\n\n---\n\n## 7. Robustness\n\n### 7.1 Cross-Dataset Evaluation\n\nHow does the model generalize to other datasets?\n\n| Dataset | Score | Notes |\n|---------|-------|-------|\n| Original | 0.89 | Training distribution |\n| Dataset A | 0.82 | Similar domain |\n| Dataset B | 0.71 | Different domain |\n\n### 7.2 Adversarial Robustness\n\nPerformance under adversarial conditions.\n\n### 7.3 Fairness Analysis\n\nPerformance across demographic groups or sensitive attributes.\n\n---\n\n## 8. Deployment Considerations\n\n### 8.1 Model Size\n\n- **Parameters**: Total count\n- **Disk Size**: MB/GB on disk\n- **Memory**: Runtime memory usage\n\n### 8.2 Inference Speed\n\n| Batch Size | Latency | Throughput |\n|------------|---------|------------|\n| 1 | 10ms | 100 QPS |\n| 8 | 45ms | 178 QPS |\n| 32 | 150ms | 213 QPS |\n\n### 8.3 Production Requirements\n\n- **Dependencies**: Software requirements\n- **Infrastructure**: Hardware needs\n- **Monitoring**: What to track in production\n- **Fallback**: Backup strategy\n\n---\n\n## 9. Conclusions\n\n### 9.1 Summary\n\nKey takeaways from the experiment.\n\n### 9.2 Did We Meet Objectives?\n\n| Objective | Status | Notes |\n|-----------|--------|-------|\n| Objective 1 | ✅ Met | Achieved target |\n| Objective 2 | ⚠️ Partial | Close to target |\n| Objective 3 | ❌ Not Met | Needs more work |\n\n### 9.3 Lessons Learned\n\nWhat did we learn from this experiment?\n\n1. Lesson 1\n2. Lesson 2\n3. Lesson 3\n\n---\n\n## 10. Next Steps\n\n### 10.1 Short-term (1-2 weeks)\n\n- [ ] Task 1\n- [ ] Task 2\n- [ ] Task 3\n\n### 10.2 Medium-term (1-2 months)\n\n- [ ] Task 1\n- [ ] Task 2\n- [ ] Task 3\n\n### 10.3 Long-term (3+ months)\n\n- [ ] Task 1\n- [ ] Task 2\n- [ ] Task 3\n\n---\n\n## References\n\n1. Reference 1\n2. Reference 2\n3. Reference 3\n\n---\n\n## Appendix\n\n### A. Hyperparameter Search\n\nResults from hyperparameter tuning.\n\n### B. Additional Experiments\n\nSupplementary experiments not included in main text.\n\n### C. Code\n\nLinks to code repositories:\n- Training code: [link]\n- Evaluation code: [link]\n- Model checkpoint: [link]\n\n### D. Data Card\n\nDetailed data documentation following standard practices.\n\n### E. Model Card\n\nModel documentation following responsible AI practices.\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":6865,"content_sha256":"3c9d184cbafc5c66d4a8519502665ce300568762abb746d4ba14de03de99d872"},{"filename":"templates/modern.md","content":"---\ntitle: {{TITLE}}\nauthors: {{AUTHORS}}\ndate: {{DATE}}\narxiv:\ntags: [machine-learning, ai]\nlayout: modern\n---\n\n\u003cdiv class=\"header\">\n\n# {{TITLE}}\n\n\u003cdiv class=\"authors\">\n{{AUTHORS}}\n\u003c/div>\n\n\u003cdiv class=\"date\">\n{{DATE}}\n\u003c/div>\n\n\u003cdiv class=\"links\">\n[arXiv](#) · [PDF](#) · [Code](#) · [Demo](#)\n\u003c/div>\n\n\u003c/div>\n\n---\n\n## Abstract\n\n\u003cdiv class=\"abstract\">\n\n{{ABSTRACT}}\n\n\u003c/div>\n\n---\n\n## Introduction\n\nModern research requires clear, accessible communication. This template provides a clean, web-friendly format inspired by Distill and modern scientific publications.\n\n\u003cdiv class=\"key-insight\">\n💡 **Key Insight**: Present your main contribution upfront to engage readers immediately.\n\u003c/div>\n\n### Why This Matters\n\nExplain the significance of your work in plain language. What real-world problems does it solve?\n\n### Our Approach\n\nSummarize your methodology at a high level before diving into details.\n\n---\n\n## Background\n\n\u003cdiv class=\"definition\">\n**Definition**: Clearly define key terms and concepts early in the paper.\n\u003c/div>\n\nProvide context necessary to understand your contribution without overwhelming readers with details.\n\n### Problem Statement\n\nFormally state the problem you're addressing.\n\n### Challenges\n\nWhat makes this problem difficult?\n\n1. **Challenge 1**: Description\n2. **Challenge 2**: Description\n3. **Challenge 3**: Description\n\n---\n\n## Method\n\nPresent your approach with clear visual aids and intuitive explanations.\n\n\u003cdiv class=\"figure\">\n\n```\n[Diagram of your architecture goes here]\n```\n\n**Figure 1**: Overview of the proposed method. Caption explains the key components.\n\n\u003c/div>\n\n### Model Architecture\n\nDescribe your model systematically:\n\n```python\n# Pseudocode example\nclass YourModel:\n def __init__(self):\n self.encoder = Encoder()\n self.decoder = Decoder()\n\n def forward(self, x):\n z = self.encoder(x)\n output = self.decoder(z)\n return output\n```\n\n### Training Strategy\n\nExplain how you train the model, including:\n\n- **Objective Function**: Mathematical formulation\n- **Optimization**: Algorithm and hyperparameters\n- **Regularization**: Techniques to prevent overfitting\n\n---\n\n## Experiments\n\n### Setup\n\n\u003cdiv class=\"experiment-details\">\n\n| Component | Configuration |\n|-----------|--------------|\n| **Dataset** | Name, Size, Split |\n| **Hardware** | GPU Type, RAM |\n| **Framework** | PyTorch 2.0, Transformers |\n| **Training Time** | Hours/Days |\n\n\u003c/div>\n\n### Results\n\nPresent results clearly with tables and visualizations.\n\n\u003cdiv class=\"results-table\">\n\n| Model | Accuracy | F1 Score | Params | Speed |\n|-------|----------|----------|--------|-------|\n| Baseline | 85.2% | 0.84 | 100M | 100 tok/s |\n| **Ours** | **92.1%** | **0.91** | 120M | 95 tok/s |\n| SOTA | 90.5% | 0.89 | 300M | 60 tok/s |\n\n\u003c/div>\n\n\u003cdiv class=\"insight\">\n🔍 **Observation**: Our method achieves state-of-the-art performance with fewer parameters.\n\u003c/div>\n\n### Analysis\n\nDeep dive into what the results reveal:\n\n1. **Performance**: How does your method compare?\n2. **Efficiency**: What are the computational costs?\n3. **Robustness**: How does it perform across different scenarios?\n\n---\n\n## Ablation Study\n\nSystematically evaluate each component's contribution.\n\n\u003cdiv class=\"ablation-results\">\n\n| Configuration | Score | Δ |\n|---------------|-------|---|\n| Full Model | 92.1% | - |\n| - Component A | 89.3% | -2.8% |\n| - Component B | 90.1% | -2.0% |\n| - Component C | 91.5% | -0.6% |\n\n\u003c/div>\n\n**Conclusion**: All components contribute meaningfully, with Component A being most critical.\n\n---\n\n## Discussion\n\n### What We Learned\n\nSynthesize insights from your experiments.\n\n### Limitations\n\n\u003cdiv class=\"limitations\">\n\n⚠️ **Current Limitations**:\n\n1. Performance on domain X is limited\n2. Computational requirements are high\n3. Requires large training datasets\n\n\u003c/div>\n\n### Future Directions\n\nWhere should the community go next?\n\n- **Direction 1**: Description\n- **Direction 2**: Description\n- **Direction 3**: Description\n\n---\n\n## Related Work\n\nCompare and contrast with existing methods.\n\n### Prior Approaches\n\n| Method | Year | Key Idea | Limitation |\n|--------|------|----------|------------|\n| Method A | 2020 | Approach 1 | Issue X |\n| Method B | 2021 | Approach 2 | Issue Y |\n| Method C | 2023 | Approach 3 | Issue Z |\n\n### How We Differ\n\nClearly articulate what's novel about your work.\n\n---\n\n## Conclusion\n\n\u003cdiv class=\"conclusion\">\n\nWe presented **{{TITLE}}**, which achieves:\n\n1. ✅ **Main contribution 1**\n2. ✅ **Main contribution 2**\n3. ✅ **Main contribution 3**\n\nOur results demonstrate [key finding], opening new directions for [future work].\n\n\u003c/div>\n\n---\n\n## Reproducibility\n\n\u003cdiv class=\"reproducibility\">\n\n### Code & Data\n\n- **Code**: [github.com/username/repo](#)\n- **Models**: [huggingface.co/username/model](#)\n- **Datasets**: [huggingface.co/datasets/username/dataset](#)\n- **Demo**: [huggingface.co/spaces/username/demo](#)\n\n### Citation\n\n```bibtex\n@article{yourpaper2025,\n title={{{{TITLE}}}},\n author={{{{AUTHORS}}}},\n year={2025},\n journal={arXiv preprint}\n}\n```\n\n\u003c/div>\n\n---\n\n## Acknowledgments\n\nThank funding agencies, collaborators, and computing resources that made this work possible.\n\n---\n\n\u003cdiv class=\"appendix\">\n\n## Appendix\n\n### A. Additional Results\n\nSupplementary experiments and extended results.\n\n### B. Hyperparameters\n\nComplete training configuration:\n\n```yaml\nlearning_rate: 1e-4\nbatch_size: 32\nepochs: 100\noptimizer: AdamW\nscheduler: cosine\nwarmup_steps: 1000\n```\n\n### C. Dataset Details\n\nDetailed information about datasets used.\n\n\u003c/div>\n\n---\n\n\u003cstyle>\n.header { text-align: center; margin-bottom: 2em; }\n.authors { font-size: 1.2em; margin: 0.5em 0; }\n.date { color: #666; margin: 0.5em 0; }\n.links { margin-top: 1em; }\n.abstract { background: #f5f5f5; padding: 1.5em; border-radius: 8px; margin: 1em 0; }\n.key-insight, .insight { background: #e8f4f8; border-left: 4px solid #2196F3; padding: 1em; margin: 1em 0; }\n.definition { background: #fff3e0; border-left: 4px solid #ff9800; padding: 1em; margin: 1em 0; }\n.limitations { background: #ffebee; border-left: 4px solid #f44336; padding: 1em; margin: 1em 0; }\n.conclusion { background: #e8f5e9; border-left: 4px solid #4caf50; padding: 1.5em; margin: 1em 0; }\n.figure { text-align: center; margin: 2em 0; }\n.experiment-details, .results-table, .ablation-results { margin: 1em 0; }\n.reproducibility { background: #f5f5f5; padding: 1.5em; border-radius: 8px; margin: 2em 0; }\n\u003c/style>\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":6431,"content_sha256":"4fbe67f48898356bab2ccaae9f2a630aabdce75dde5faedd8203bba923397a2e"},{"filename":"templates/standard.md","content":"---\ntitle: {{TITLE}}\nauthors: {{AUTHORS}}\ndate: {{DATE}}\narxiv:\ntags: [machine-learning, deep-learning]\n---\n\n# {{TITLE}}\n\n**{{AUTHORS}}**\n\n*{{DATE}}*\n\n---\n\n## Abstract\n\n{{ABSTRACT}}\n\n---\n\n## 1. Introduction\n\nProvide background and motivation for your research. Explain:\n- What problem are you addressing?\n- Why is it important?\n- What is novel about your approach?\n\n### 1.1 Motivation\n\nDescribe the real-world context and importance of the problem.\n\n### 1.2 Contributions\n\nList the main contributions of your work:\n1. First contribution\n2. Second contribution\n3. Third contribution\n\n---\n\n## 2. Related Work\n\nSurvey previous research relevant to your work. Organize by:\n- Different approaches to the problem\n- Complementary methods\n- Alternative solutions\n\n### 2.1 Previous Approaches\n\nDiscuss earlier methods and their limitations.\n\n### 2.2 Recent Advances\n\nHighlight recent developments in the field.\n\n---\n\n## 3. Background\n\nProvide necessary technical background for understanding your work.\n\n### 3.1 Problem Formulation\n\nFormally define the problem you're solving.\n\n### 3.2 Preliminaries\n\nIntroduce key concepts, notation, and terminology.\n\n---\n\n## 4. Methodology\n\nDescribe your approach in detail.\n\n### 4.1 Overview\n\nProvide a high-level description of your method.\n\n### 4.2 Model Architecture\n\nDetail the technical components of your system.\n\n### 4.3 Training Procedure\n\nExplain how the model is trained.\n\n### 4.4 Implementation Details\n\nProvide reproducibility information:\n- Hyperparameters\n- Hardware requirements\n- Software dependencies\n\n---\n\n## 5. Experiments\n\nPresent your experimental setup and results.\n\n### 5.1 Datasets\n\nDescribe the datasets used for evaluation.\n\n### 5.2 Evaluation Metrics\n\nDefine the metrics used to assess performance.\n\n### 5.3 Baselines\n\nList comparison methods.\n\n### 5.4 Experimental Setup\n\nDetail the experimental configuration.\n\n---\n\n## 6. Results\n\nPresent and analyze your findings.\n\n### 6.1 Main Results\n\nReport primary experimental results.\n\n| Model | Dataset | Metric | Score |\n|-------|---------|--------|-------|\n| Baseline | Dataset A | Accuracy | 0.85 |\n| Ours | Dataset A | Accuracy | 0.92 |\n\n### 6.2 Ablation Studies\n\nAnalyze the contribution of different components.\n\n### 6.3 Qualitative Analysis\n\nProvide examples and case studies.\n\n---\n\n## 7. Discussion\n\nInterpret your results and discuss implications.\n\n### 7.1 Analysis\n\nWhat do the results tell us?\n\n### 7.2 Limitations\n\nAcknowledge limitations of your approach.\n\n### 7.3 Broader Impact\n\nDiscuss societal implications and potential applications.\n\n---\n\n## 8. Conclusion\n\nSummarize your work and contributions.\n\n### 8.1 Summary\n\nRecap the main findings.\n\n### 8.2 Future Work\n\nSuggest directions for future research.\n\n---\n\n## Acknowledgments\n\nThank collaborators, funding sources, and computational resources.\n\n---\n\n## References\n\n1. Author A, et al. \"Paper Title.\" Conference/Journal, Year.\n2. Author B, et al. \"Another Paper.\" Conference/Journal, Year.\n\n---\n\n## Appendix\n\n### A. Additional Experiments\n\nSupplementary experimental results.\n\n### B. Implementation Details\n\nCode snippets and configuration details.\n\n### C. Hyperparameters\n\nComplete list of hyperparameters used.\n","content_type":"text/markdown; charset=utf-8","language":"markdown","size":3180,"content_sha256":"a18e8d450b32d908aacfc626a19482920ea5d9ba470181d4352792d3600c0605"}],"content_json":{"type":"doc","content":[{"type":"heading","attrs":{"level":1},"content":[{"text":"Overview","type":"text"}]},{"type":"paragraph","content":[{"text":"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.","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Integration with HF Ecosystem","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Paper Pages","type":"text","marks":[{"type":"strong"}]},{"text":": Index and discover papers on Hugging Face Hub","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"arXiv Integration","type":"text","marks":[{"type":"strong"}]},{"text":": Automatic paper indexing from arXiv IDs","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Model/Dataset Linking","type":"text","marks":[{"type":"strong"}]},{"text":": Connect papers to relevant artifacts through metadata","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Authorship Verification","type":"text","marks":[{"type":"strong"}]},{"text":": Claim and verify paper authorship","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Research Article Template","type":"text","marks":[{"type":"strong"}]},{"text":": Generate professional, modern scientific papers","type":"text"}]}]}]},{"type":"heading","attrs":{"level":1},"content":[{"text":"Version","type":"text"}]},{"type":"paragraph","content":[{"text":"1.0.0","type":"text"}]},{"type":"heading","attrs":{"level":1},"content":[{"text":"Dependencies","type":"text"}]},{"type":"paragraph","content":[{"text":"The included script uses PEP 723 inline dependencies. Prefer ","type":"text"},{"text":"uv run","type":"text","marks":[{"type":"code_inline"}]},{"text":" over manual environment setup.","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"huggingface_hub>=0.26.0","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"pyyaml>=6.0.3","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"requests>=2.32.5","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"markdown>=3.5.0","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"python-dotenv>=1.2.1","type":"text"}]}]}]},{"type":"heading","attrs":{"level":1},"content":[{"text":"Core Capabilities","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"1. Paper Page Management","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Index Papers","type":"text","marks":[{"type":"strong"}]},{"text":": Add papers to Hugging Face from arXiv","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Claim Authorship","type":"text","marks":[{"type":"strong"}]},{"text":": Verify and claim authorship on published papers","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Manage Visibility","type":"text","marks":[{"type":"strong"}]},{"text":": Control which papers appear on your profile","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Paper Discovery","type":"text","marks":[{"type":"strong"}]},{"text":": Find and explore papers in the HF ecosystem","type":"text"}]}]}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"2. Link Papers to Artifacts","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Model Cards","type":"text","marks":[{"type":"strong"}]},{"text":": Add paper citations to model metadata","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Dataset Cards","type":"text","marks":[{"type":"strong"}]},{"text":": Link papers to datasets via README","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Automatic Tagging","type":"text","marks":[{"type":"strong"}]},{"text":": Hub auto-generates arxiv:\u003cPAPER_ID> tags","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Citation Management","type":"text","marks":[{"type":"strong"}]},{"text":": Maintain proper attribution and references","type":"text"}]}]}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"3. Research Article Creation","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Markdown Templates","type":"text","marks":[{"type":"strong"}]},{"text":": Generate professional paper formatting","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Modern Design","type":"text","marks":[{"type":"strong"}]},{"text":": Clean, readable research article layouts","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Dynamic TOC","type":"text","marks":[{"type":"strong"}]},{"text":": Automatic table of contents generation","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Section Structure","type":"text","marks":[{"type":"strong"}]},{"text":": Standard scientific paper organization","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"LaTeX Math","type":"text","marks":[{"type":"strong"}]},{"text":": Support for equations and technical notation","type":"text"}]}]}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"4. Metadata Management","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"YAML Frontmatter","type":"text","marks":[{"type":"strong"}]},{"text":": Proper model/dataset card metadata","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Citation Tracking","type":"text","marks":[{"type":"strong"}]},{"text":": Maintain paper references across repositories","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Version Control","type":"text","marks":[{"type":"strong"}]},{"text":": Track paper updates and revisions","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Multi-Paper Support","type":"text","marks":[{"type":"strong"}]},{"text":": Link multiple papers to single artifacts","type":"text"}]}]}]},{"type":"heading","attrs":{"level":1},"content":[{"text":"Usage Instructions","type":"text"}]},{"type":"paragraph","content":[{"text":"The skill includes Python scripts in ","type":"text"},{"text":"scripts/","type":"text","marks":[{"type":"code_inline"}]},{"text":" for paper publishing operations.","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Prerequisites","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Run scripts with ","type":"text"},{"text":"uv run","type":"text","marks":[{"type":"code_inline"}]},{"text":" (dependencies are resolved from the script header)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Set ","type":"text"},{"text":"HF_TOKEN","type":"text","marks":[{"type":"code_inline"}]},{"text":" environment variable with Write-access token","type":"text"}]}]}]},{"type":"blockquote","content":[{"type":"paragraph","content":[{"text":"All paths are relative to the directory containing this SKILL.md file.","type":"text","marks":[{"type":"strong"}]},{"text":" Before running any script, first ","type":"text"},{"text":"cd","type":"text","marks":[{"type":"code_inline"}]},{"text":" to that directory or use the full path.","type":"text"}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Method 1: Index Paper from arXiv","type":"text"}]},{"type":"paragraph","content":[{"text":"Add a paper to Hugging Face Paper Pages from arXiv.","type":"text"}]},{"type":"paragraph","content":[{"text":"Basic Usage:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py index \\\n --arxiv-id \"2301.12345\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Check If Paper Exists:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py check \\\n --arxiv-id \"2301.12345\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Direct URL Access:","type":"text","marks":[{"type":"strong"}]},{"text":" You can also visit ","type":"text"},{"text":"https://huggingface.co/papers/{arxiv-id}","type":"text","marks":[{"type":"code_inline"}]},{"text":" directly to index a paper.","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Method 2: Link Paper to Model/Dataset","type":"text"}]},{"type":"paragraph","content":[{"text":"Add paper references to model or dataset README with proper YAML metadata.","type":"text"}]},{"type":"paragraph","content":[{"text":"Add to Model Card:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py link \\\n --repo-id \"username/model-name\" \\\n --repo-type \"model\" \\\n --arxiv-id \"2301.12345\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Add to Dataset Card:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py link \\\n --repo-id \"username/dataset-name\" \\\n --repo-type \"dataset\" \\\n --arxiv-id \"2301.12345\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Add Multiple Papers:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py link \\\n --repo-id \"username/model-name\" \\\n --repo-type \"model\" \\\n --arxiv-ids \"2301.12345,2302.67890,2303.11111\"","type":"text"}]},{"type":"paragraph","content":[{"text":"With Custom Citation:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py link \\\n --repo-id \"username/model-name\" \\\n --repo-type \"model\" \\\n --arxiv-id \"2301.12345\" \\\n --citation \"$(cat citation.txt)\"","type":"text"}]},{"type":"heading","attrs":{"level":4},"content":[{"text":"How Linking Works","type":"text"}]},{"type":"paragraph","content":[{"text":"When you add an arXiv paper link to a model or dataset README:","type":"text"}]},{"type":"ordered_list","attrs":{"order":1,"listStyle":"number"},"content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"The Hub extracts the arXiv ID from the link","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"A tag ","type":"text"},{"text":"arxiv:\u003cPAPER_ID>","type":"text","marks":[{"type":"code_inline"}]},{"text":" is automatically added to the repository","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Users can click the tag to view the Paper Page","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"The Paper Page shows all models/datasets citing this paper","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Papers are discoverable through filters and search","type":"text"}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Method 3: Claim Authorship","type":"text"}]},{"type":"paragraph","content":[{"text":"Verify your authorship on papers published on Hugging Face.","type":"text"}]},{"type":"paragraph","content":[{"text":"Start Claim Process:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py claim \\\n --arxiv-id \"2301.12345\" \\\n --email \"[email protected]\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Manual Process:","type":"text","marks":[{"type":"strong"}]}]},{"type":"ordered_list","attrs":{"order":1,"listStyle":"number"},"content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Navigate to your paper's page: ","type":"text"},{"text":"https://huggingface.co/papers/{arxiv-id}","type":"text","marks":[{"type":"code_inline"}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Find your name in the author list","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Click your name and select \"Claim authorship\"","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Wait for admin team verification","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"Check Authorship Status:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py check-authorship \\\n --arxiv-id \"2301.12345\"","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Method 4: Manage Paper Visibility","type":"text"}]},{"type":"paragraph","content":[{"text":"Control which verified papers appear on your public profile.","type":"text"}]},{"type":"paragraph","content":[{"text":"List Your Papers:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py list-my-papers","type":"text"}]},{"type":"paragraph","content":[{"text":"Toggle Visibility:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py toggle-visibility \\\n --arxiv-id \"2301.12345\" \\\n --show true","type":"text"}]},{"type":"paragraph","content":[{"text":"Manage in Settings:","type":"text","marks":[{"type":"strong"}]},{"text":" Navigate to your account settings → Papers section to toggle \"Show on profile\" for each paper.","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Method 5: Create Research Article","type":"text"}]},{"type":"paragraph","content":[{"text":"Generate a professional markdown-based research paper using modern templates.","type":"text"}]},{"type":"paragraph","content":[{"text":"Create from Template:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py create \\\n --template \"standard\" \\\n --title \"Your Paper Title\" \\\n --output \"paper.md\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Available Templates:","type":"text","marks":[{"type":"strong"}]}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"standard","type":"text","marks":[{"type":"code_inline"}]},{"text":" - Traditional scientific paper structure","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"modern","type":"text","marks":[{"type":"code_inline"}]},{"text":" - Clean, web-friendly format inspired by Distill","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"arxiv","type":"text","marks":[{"type":"code_inline"}]},{"text":" - arXiv-style formatting","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"ml-report","type":"text","marks":[{"type":"code_inline"}]},{"text":" - Machine learning experiment report","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"Generate Complete Paper:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_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\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Convert to HTML:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py convert \\\n --input \"paper.md\" \\\n --output \"paper.html\" \\\n --style \"modern\"","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Paper Template Structure","type":"text"}]},{"type":"paragraph","content":[{"text":"Standard Research Paper Sections:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"markdown"},"content":[{"text":"---\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","type":"text"}]},{"type":"paragraph","content":[{"text":"Modern Template Features:","type":"text","marks":[{"type":"strong"}]}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Dynamic table of contents","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Responsive design for web viewing","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Code syntax highlighting","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Interactive figures and charts","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Math equation rendering (LaTeX)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Citation management","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Author affiliation linking","type":"text"}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Commands Reference","type":"text"}]},{"type":"paragraph","content":[{"text":"Index Paper:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py index --arxiv-id \"2301.12345\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Link to Repository:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py link \\\n --repo-id \"username/repo-name\" \\\n --repo-type \"model|dataset|space\" \\\n --arxiv-id \"2301.12345\" \\\n [--citation \"Full citation text\"] \\\n [--create-pr]","type":"text"}]},{"type":"paragraph","content":[{"text":"Claim Authorship:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py claim \\\n --arxiv-id \"2301.12345\" \\\n --email \"your.email@edu\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Manage Visibility:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py toggle-visibility \\\n --arxiv-id \"2301.12345\" \\\n --show true|false","type":"text"}]},{"type":"paragraph","content":[{"text":"Create Research Article:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_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\"]","type":"text"}]},{"type":"paragraph","content":[{"text":"Convert Markdown to HTML:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py convert \\\n --input \"paper.md\" \\\n --output \"paper.html\" \\\n [--style \"modern|classic\"]","type":"text"}]},{"type":"paragraph","content":[{"text":"Check Paper Status:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py check --arxiv-id \"2301.12345\"","type":"text"}]},{"type":"paragraph","content":[{"text":"List Your Papers:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py list-my-papers","type":"text"}]},{"type":"paragraph","content":[{"text":"Search Papers:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"uv run scripts/paper_manager.py search --query \"transformer attention\"","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"YAML Metadata Format","type":"text"}]},{"type":"paragraph","content":[{"text":"When linking papers to models or datasets, proper YAML frontmatter is required:","type":"text"}]},{"type":"paragraph","content":[{"text":"Model Card Example:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"yaml"},"content":[{"text":"---\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://arxiv.org/abs/2301.12345).\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}","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":""},"content":[{"text":"\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://arxiv.org/abs/2301.12345).\n\nFor more details, see the [paper page](https://huggingface.co/papers/2301.12345).","type":"text"}]},{"type":"paragraph","content":[{"text":"The Hub automatically extracts arXiv IDs from these links and creates ","type":"text"},{"text":"arxiv:2301.12345","type":"text","marks":[{"type":"code_inline"}]},{"text":" tags.","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Integration Examples","type":"text"}]},{"type":"paragraph","content":[{"text":"Workflow 1: Publish New Research","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# 1. Create research article\nuv run scripts/paper_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/paper_manager.py index --arxiv-id \"2301.12345\"\n\n# 5. Link to your model\nuv run scripts/paper_manager.py link \\\n --repo-id \"your-username/your-model\" \\\n --repo-type \"model\" \\\n --arxiv-id \"2301.12345\"\n\n# 6. Claim authorship\nuv run scripts/paper_manager.py claim \\\n --arxiv-id \"2301.12345\" \\\n --email \"your.email@edu\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Workflow 2: Link Existing Paper","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# 1. Check if paper exists\nuv run scripts/paper_manager.py check --arxiv-id \"2301.12345\"\n\n# 2. Index if needed\nuv run scripts/paper_manager.py index --arxiv-id \"2301.12345\"\n\n# 3. Link to multiple repositories\nuv run scripts/paper_manager.py link \\\n --repo-id \"username/model-v1\" \\\n --repo-type \"model\" \\\n --arxiv-id \"2301.12345\"\n\nuv run scripts/paper_manager.py link \\\n --repo-id \"username/training-data\" \\\n --repo-type \"dataset\" \\\n --arxiv-id \"2301.12345\"\n\nuv run scripts/paper_manager.py link \\\n --repo-id \"username/demo-space\" \\\n --repo-type \"space\" \\\n --arxiv-id \"2301.12345\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Workflow 3: Update Model with Paper Reference","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# 1. Get current README\nhf download username/model-name README.md\n\n# 2. Add paper link\nuv run scripts/paper_manager.py link \\\n --repo-id \"username/model-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","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Best Practices","type":"text"}]},{"type":"ordered_list","attrs":{"order":1,"listStyle":"number"},"content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Paper Indexing","type":"text","marks":[{"type":"strong"}]}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Index papers as soon as they're published on arXiv","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Include full citation information in model/dataset cards","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Use consistent paper references across related repositories","type":"text"}]}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Metadata Management","type":"text","marks":[{"type":"strong"}]}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Add YAML frontmatter to all model/dataset cards","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Include proper licensing information","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Tag with relevant task categories and domains","type":"text"}]}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Authorship","type":"text","marks":[{"type":"strong"}]}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Claim authorship on papers where you're listed as author","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Use institutional email addresses for verification","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Keep paper visibility settings updated","type":"text"}]}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Repository Linking","type":"text","marks":[{"type":"strong"}]}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Link papers to all relevant models, datasets, and Spaces","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Include paper context in README descriptions","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Add BibTeX citations for easy reference","type":"text"}]}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Research Articles","type":"text","marks":[{"type":"strong"}]}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Use templates consistently within projects","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Include code and data links in papers","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Generate web-friendly HTML versions for sharing","type":"text"}]}]}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Advanced Usage","type":"text"}]},{"type":"paragraph","content":[{"text":"Batch Link Papers:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# Link multiple papers to one repository\nfor arxiv_id in \"2301.12345\" \"2302.67890\" \"2303.11111\"; do\n uv run scripts/paper_manager.py link \\\n --repo-id \"username/model-name\" \\\n --repo-type \"model\" \\\n --arxiv-id \"$arxiv_id\"\ndone","type":"text"}]},{"type":"paragraph","content":[{"text":"Extract Paper Info:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# Get paper metadata from arXiv\nuv run scripts/paper_manager.py info \\\n --arxiv-id \"2301.12345\" \\\n --format \"json\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Generate Citation:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# Create BibTeX citation\nuv run scripts/paper_manager.py citation \\\n --arxiv-id \"2301.12345\" \\\n --format \"bibtex\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Validate Links:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# Check all paper links in a repository\nuv run scripts/paper_manager.py validate \\\n --repo-id \"username/model-name\" \\\n --repo-type \"model\"","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Error Handling","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Paper Not Found","type":"text","marks":[{"type":"strong"}]},{"text":": arXiv ID doesn't exist or isn't indexed yet","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Permission Denied","type":"text","marks":[{"type":"strong"}]},{"text":": HF_TOKEN lacks write access to repository","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Invalid YAML","type":"text","marks":[{"type":"strong"}]},{"text":": Malformed metadata in README frontmatter","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Authorship Failed","type":"text","marks":[{"type":"strong"}]},{"text":": Email doesn't match paper author records","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Already Claimed","type":"text","marks":[{"type":"strong"}]},{"text":": Another user has claimed authorship","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Rate Limiting","type":"text","marks":[{"type":"strong"}]},{"text":": Too many API requests in short time","type":"text"}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Troubleshooting","type":"text"}]},{"type":"paragraph","content":[{"text":"Issue","type":"text","marks":[{"type":"strong"}]},{"text":": \"Paper not found on Hugging Face\"","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Solution","type":"text","marks":[{"type":"strong"}]},{"text":": Visit ","type":"text"},{"text":"hf.co/papers/{arxiv-id}","type":"text","marks":[{"type":"code_inline"}]},{"text":" to trigger indexing","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"Issue","type":"text","marks":[{"type":"strong"}]},{"text":": \"Authorship claim not verified\"","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Solution","type":"text","marks":[{"type":"strong"}]},{"text":": Wait for admin review or contact HF support with proof","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"Issue","type":"text","marks":[{"type":"strong"}]},{"text":": \"arXiv tag not appearing\"","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Solution","type":"text","marks":[{"type":"strong"}]},{"text":": Ensure README includes proper arXiv URL format","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"Issue","type":"text","marks":[{"type":"strong"}]},{"text":": \"Cannot link to repository\"","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Solution","type":"text","marks":[{"type":"strong"}]},{"text":": Verify HF_TOKEN has write permissions","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"Issue","type":"text","marks":[{"type":"strong"}]},{"text":": \"Template rendering errors\"","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Solution","type":"text","marks":[{"type":"strong"}]},{"text":": Check markdown syntax and YAML frontmatter format","type":"text"}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Resources and References","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Hugging Face Paper Pages","type":"text","marks":[{"type":"strong"}]},{"text":": ","type":"text"},{"text":"hf.co/papers","type":"text","marks":[{"type":"link","attrs":{"href":"https://huggingface.co/papers","title":null}}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Model Cards Guide","type":"text","marks":[{"type":"strong"}]},{"text":": ","type":"text"},{"text":"hf.co/docs/hub/model-cards","type":"text","marks":[{"type":"link","attrs":{"href":"https://huggingface.co/docs/hub/en/model-cards","title":null}}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Dataset Cards Guide","type":"text","marks":[{"type":"strong"}]},{"text":": ","type":"text"},{"text":"hf.co/docs/hub/datasets-cards","type":"text","marks":[{"type":"link","attrs":{"href":"https://huggingface.co/docs/hub/en/datasets-cards","title":null}}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Research Article Template","type":"text","marks":[{"type":"strong"}]},{"text":": ","type":"text"},{"text":"tfrere/research-article-template","type":"text","marks":[{"type":"link","attrs":{"href":"https://huggingface.co/spaces/tfrere/research-article-template","title":null}}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"arXiv Format Guide","type":"text","marks":[{"type":"strong"}]},{"text":": ","type":"text"},{"text":"arxiv.org/help/submit","type":"text","marks":[{"type":"link","attrs":{"href":"https://arxiv.org/help/submit","title":null}}]}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Integration with tfrere's Research Template","type":"text"}]},{"type":"paragraph","content":[{"text":"This skill complements ","type":"text"},{"text":"tfrere's research article template","type":"text","marks":[{"type":"link","attrs":{"href":"https://huggingface.co/spaces/tfrere/research-article-template","title":null}}]},{"text":" by providing:","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Automated paper indexing workflows","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Repository linking capabilities","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Metadata management tools","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Citation generation utilities","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"You can use tfrere's template for writing, then use this skill to publish and link the paper on Hugging Face Hub.","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Common Patterns","type":"text"}]},{"type":"paragraph","content":[{"text":"Pattern 1: New Paper Publication","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# Write → Publish → Index → Link\nuv run scripts/paper_manager.py create --template modern --output paper.md\n# (Submit to arXiv)\nuv run scripts/paper_manager.py index --arxiv-id \"2301.12345\"\nuv run scripts/paper_manager.py link --repo-id \"user/model\" --arxiv-id \"2301.12345\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Pattern 2: Existing Paper Discovery","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# Search → Check → Link\nuv run scripts/paper_manager.py search --query \"transformers\"\nuv run scripts/paper_manager.py check --arxiv-id \"2301.12345\"\nuv run scripts/paper_manager.py link --repo-id \"user/model\" --arxiv-id \"2301.12345\"","type":"text"}]},{"type":"paragraph","content":[{"text":"Pattern 3: Author Portfolio Management","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# Claim → Verify → Organize\nuv run scripts/paper_manager.py claim --arxiv-id \"2301.12345\"\nuv run scripts/paper_manager.py list-my-papers\nuv run scripts/paper_manager.py toggle-visibility --arxiv-id \"2301.12345\" --show true","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"API Integration","type":"text"}]},{"type":"paragraph","content":[{"text":"Python Script Example:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"python"},"content":[{"text":"from 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/model\",\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)","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Future Enhancements","type":"text"}]},{"type":"paragraph","content":[{"text":"Planned features for future versions:","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Support for non-arXiv papers (conference proceedings, journals)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Automatic citation formatting from DOI","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Paper comparison and versioning tools","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Collaborative paper writing features","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Integration with LaTeX workflows","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Automated figure and table extraction","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Paper metrics and impact tracking","type":"text"}]}]}]}]},"metadata":{"date":"2026-11-02","name":"huggingface-paper-publisher","author":"@skillopedia","source":{"stars":11256,"repo_name":"skills","origin_url":"https://github.com/huggingface/skills/blob/HEAD/skills/huggingface-paper-publisher/SKILL.md","repo_owner":"huggingface","body_sha256":"04b59406cf88054ab365f5bbbc57fe2e2fcb369e942dff2841c4f4297318c353","cluster_key":"b67822542a1ad9df7d2cf9f45485dc00f80d59672d196f6d947bc4934394e978","clean_bundle":{"format":"clean-skill-bundle-v1","source":"huggingface/skills/skills/huggingface-paper-publisher/SKILL.md","text_bytes":58697,"source_commit":"25910b3d0ba31a2ba102f901cdc9e008c278a69c","skills_sh_rank":9407,"attachment_count":7,"text_attachments":7,"attachment_storage":"skill_attachments","binary_attachments":0},"cluster_size":1,"skill_md_path":"skills/huggingface-paper-publisher/SKILL.md","import_metadata":{"date":"2026-11-02","author":"@skillopedia","version":"v1","category":"education-research","category_label":"Education"},"exact_dupes_collapsed_into_this":0},"version":"v1","category":"education-research","import_tag":"skills-sh-top20k-2026-11","description":"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."}},"renderedAt":1793666648596}

Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.