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LawrenceRiver/FigFox-Gen-skill
Scientific paper figure generation Skill for agents: web research-figure conventions, controlled colour, topology planning, and image generation. 科研论文图生成 Skill：联网参考、配色与拓扑规划。
History
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35GitHub stars
2stars in 7 days
1skill in this repository
0commits in 30 days
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Individual skills in this repository
How it works
FigFox-Gen-skill
When your agent uses it
Use when turning a scientific Methodology, with an optional reference image, into one evidence-grounded, human-editable final PNG figure.
What it loads into context
Frontmatter
52 tokensAlways available to the agent
SKILL.md
3,311 tokensWhen the skill is activated
Reference documents
10,538 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Core workflow
- Canonical visual examples
- 1. Context 1: recurring domain visual conventions
- 2. Context 2: content-to-visual plan
- 3. Context 3: construction evidence, palette groups, and taste
- 4. Creative Director: pre-PNG1 visual ideation
- 5. Prompt 1 and final PNG1
- 6. Completion and deterministic validation
Scripts executed separately
scientific_figure_workflow/__init__.pyscientific_figure_workflow/artifacts.pyscientific_figure_workflow/palette.pyscientific_figure_workflow/prompts.pyscientific_figure_workflow/reference_pack.pyscientific_figure_workflow/run_validation.pyscripts/check_installation.pyscripts/curate_figurebench_reference_pack.pyscripts/figure_workflow.pytests/test_artifacts.pytests/test_cli.pytests/test_installation.pytests/test_palette.pytests/test_prompts.pytests/test_reference_pack.pytests/test_reference_pack_cli.pytests/test_run_validation.py
Files
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View the complete source on GitHubSkills included
- FigFox-Gen-skill
Repository facts
- License
- No license detected
- Created
- Aug 23, 2026
- Last pushed
- Aug 26, 2026
- Forks
- 4
- Open issues
- 0
- Project activity
- Slowing
Before you install
Local scriptsAutomated signals, not a guarantee
Checked Nov 4, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
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- No broad tool permissions
- No dangerous shell commands
- 152 network referencesin 13 files: assets/figurebench-references/index.json:9 tests/test_reference_pack.py:22 README.md:101 and 10 more
- No hidden or obfuscated text
- No instruction overrides
- 17 executable scriptsin 17 files: scientific_figure_workflow/__init__.py:1 scientific_figure_workflow/artifacts.py:1 scientific_figure_workflow/palette.py:1 and 14 more
- 2 references to secretsin 2 files: .gitignore:9 tests/test_installation.py:186
- No requests to vote in Hot
Show all 171 signals by file
.gitignore
- Access to secretsline 9
[Credential-related source omitted]
README.md
- Network accessline 101
| Methodology | Generated result | |---|---| | **Latent Diffusion**<br>[Rombach et al.](https://arxiv.org/abs/2112.10752)<br>Image space → latent space → denoising U-Net → generated image | <img src="assets/generated-figures/02-latent-diffu - Network accessline 102
|---|---| | **Latent Diffusion**<br>[Rombach et al.](https://arxiv.org/abs/2112.10752)<br>Image space → latent space → denoising U-Net → generated image | <img src="assets/generated-figures/02-latent-diffusion.png" alt="Latent Diffusion gen - Network accessline 103
| **Latent Diffusion**<br>[Rombach et al.](https://arxiv.org/abs/2112.10752)<br>Image space → latent space → denoising U-Net → generated image | <img src="assets/generated-figures/02-latent-diffusion.png" alt="Latent Diffusion generated fig - Network accessline 104
| **MusiCoT**<br>[MusiCoT](https://arxiv.org/abs/2503.19611)<br>Text/audio inputs → CLAP/RVQ thought tokens → semantic LM → music sample | <img src="assets/generated-figures/03-musicot.png" alt="MusiCoT generated figure" width="620" /> | - Network accessline 105
| **AlphaFold 3**<br>[Abramson et al.](https://www.nature.com/articles/s41586-024-07487-w)<br>Chemical complex → pairformer/diffusion module → final structure | <img src="assets/generated-figures/04-alphafold3.png" alt="AlphaFold 3 generate - Network accessline 212
**Source:** Rombach et al., [*High-Resolution Image Synthesis with Latent Diffusion Models*](https://arxiv.org/abs/2112.10752), §3. The following is the recorded Methodology input; the source paper remains authoritative. - Network accessline 229
**Source:** [*MusiCoT: Analyzable Chain-of-Musical-Thought Prompting*](https://arxiv.org/abs/2503.19611), §4.1–§4.3. The following is the recorded Methodology input; the source paper remains authoritative. - Network accessline 246
**Source:** Abramson et al., [*Accurate structure prediction of biomolecular interactions with AlphaFold 3*](https://www.nature.com/articles/s41586-024-07487-w), “Network architecture and training.” The following is the recorded Methodology
README_ZH.md
- Network accessline 77
| Methodology | 生成效果 | |---|---| | **Latent Diffusion**<br>[Rombach et al.](https://arxiv.org/abs/2112.10752)<br>图像空间 → 潜空间 → 去噪 U-Net → 生成图像 | <img src="assets/generated-figures/02-latent-diffusion.png" alt="Latent Diffusion 生成图" width="62 - Network accessline 78
|---|---| | **Latent Diffusion**<br>[Rombach et al.](https://arxiv.org/abs/2112.10752)<br>图像空间 → 潜空间 → 去噪 U-Net → 生成图像 | <img src="assets/generated-figures/02-latent-diffusion.png" alt="Latent Diffusion 生成图" width="620" /> | | **MusiCoT**<b - Network accessline 79
| **Latent Diffusion**<br>[Rombach et al.](https://arxiv.org/abs/2112.10752)<br>图像空间 → 潜空间 → 去噪 U-Net → 生成图像 | <img src="assets/generated-figures/02-latent-diffusion.png" alt="Latent Diffusion 生成图" width="620" /> | - Network accessline 80
| **MusiCoT**<br>[MusiCoT](https://arxiv.org/abs/2503.19611)<br>文本/音频输入 → CLAP/RVQ 思维 token → 语义 LM → 音乐样本 | <img src="assets/generated-figures/03-musicot.png" alt="MusiCoT 生成图" width="620" /> | - Network accessline 81
| **AlphaFold 3**<br>[Abramson et al.](https://www.nature.com/articles/s41586-024-07487-w)<br>化学复合物 → Pairformer/扩散模块 → 最终结构 | <img src="assets/generated-figures/04-alphafold3.png" alt="AlphaFold 3 生成图" width="620" /> |
assets/figurebench-references/index.json
- Network accessline 9
"aligned panels" ], "attribution": "Original figure source: Rapid Model Architecture Adaption for Meta-Learning by Yiren Zhao, Xitong Gao, Ilia Shumailov, Nicolo Fusi, and Robert Mullins, arXiv:2109.04925; original-figure licens - Network accessline 10
], "attribution": "Original figure source: Rapid Model Architecture Adaption for Meta-Learning by Yiren Zhao, Xitong Gao, Ilia Shumailov, Nicolo Fusi, and Robert Mullins, arXiv:2109.04925; original-figure license: CC-BY-4.0; evidence: - Network accessline 11
"attribution": "Original figure source: Rapid Model Architecture Adaption for Meta-Learning by Yiren Zhao, Xitong Gao, Ilia Shumailov, Nicolo Fusi, and Robert Mullins, arXiv:2109.04925; original-figure license: CC-BY-4.0; evidence: https:// - Network accessline 30
"aligned modules" ], "attribution": "Original figure source: Class-Aware Adversarial Transformers for Medical Image Segmentation by Chenyu You, Ruihan Zhao, Fenglin Liu, Siyuan Dong, Sandeep Chinchali, Ufuk Topcu, Lawrence Staib - Network accessline 31
], "attribution": "Original figure source: Class-Aware Adversarial Transformers for Medical Image Segmentation by Chenyu You, Ruihan Zhao, Fenglin Liu, Siyuan Dong, Sandeep Chinchali, Ufuk Topcu, Lawrence Staib, and James S. Duncan, a - Network accessline 32
"attribution": "Original figure source: Class-Aware Adversarial Transformers for Medical Image Segmentation by Chenyu You, Ruihan Zhao, Fenglin Liu, Siyuan Dong, Sandeep Chinchali, Ufuk Topcu, Lawrence Staib, and James S. Duncan, arXiv:2201 - Network accessline 51
"dashed enclosures" ], "attribution": "Original figure source: A Variational Edge Partition Model for Supervised Graph Representation Learning by Yilin He, Chaojie Wang, Hao Zhang, Bo Chen, and Mingyuan Zhou, arXiv:2202.03233; o - Network accessline 52
], "attribution": "Original figure source: A Variational Edge Partition Model for Supervised Graph Representation Learning by Yilin He, Chaojie Wang, Hao Zhang, Bo Chen, and Mingyuan Zhou, arXiv:2202.03233; original-figure license: CC - Network accessline 53
"attribution": "Original figure source: A Variational Edge Partition Model for Supervised Graph Representation Learning by Yilin He, Chaojie Wang, Hao Zhang, Bo Chen, and Mingyuan Zhou, arXiv:2202.03233; original-figure license: CC-BY-4.0; - Network accessline 72
"dashed connectors" ], "attribution": "Original figure source: ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision by Anastasiia Sedova and Benjamin Roth, arXiv:2204.06863; original-figure - Network accessline 73
], "attribution": "Original figure source: ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision by Anastasiia Sedova and Benjamin Roth, arXiv:2204.06863; original-figure license: CC-BY-4.0; eviden - Network accessline 74
"attribution": "Original figure source: ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision by Anastasiia Sedova and Benjamin Roth, arXiv:2204.06863; original-figure license: CC-BY-4.0; evidence: https - Network accessline 93
"orthogonal connectors" ], "attribution": "Original figure source: ResT V2: Simpler, Faster and Stronger by Qing-Long Zhang and Yu-Bin Yang, arXiv:2204.07366; original-figure license: CC-BY-4.0; evidence: https://arxiv.org/abs/2 - Network accessline 94
], "attribution": "Original figure source: ResT V2: Simpler, Faster and Stronger by Qing-Long Zhang and Yu-Bin Yang, arXiv:2204.07366; original-figure license: CC-BY-4.0; evidence: https://arxiv.org/abs/2204.07366. FigureBench dataset - Network accessline 95
"attribution": "Original figure source: ResT V2: Simpler, Faster and Stronger by Qing-Long Zhang and Yu-Bin Yang, arXiv:2204.07366; original-figure license: CC-BY-4.0; evidence: https://arxiv.org/abs/2204.07366. FigureBench dataset license - Network accessline 114
"dashed enclosures" ], "attribution": "Original figure source: Unsupervised Cross-Task Generalization via Retrieval Augmentation by Bill Yuchen Lin, Kangmin Tan, Chris Miller, Beiwen Tian, and Xiang Ren, arXiv:2204.07937; origin - Network accessline 115
], "attribution": "Original figure source: Unsupervised Cross-Task Generalization via Retrieval Augmentation by Bill Yuchen Lin, Kangmin Tan, Chris Miller, Beiwen Tian, and Xiang Ren, arXiv:2204.07937; original-figure license: CC-BY-4 - Network accessline 116
"attribution": "Original figure source: Unsupervised Cross-Task Generalization via Retrieval Augmentation by Bill Yuchen Lin, Kangmin Tan, Chris Miller, Beiwen Tian, and Xiang Ren, arXiv:2204.07937; original-figure license: CC-BY-4.0; evide - Network accessline 135
"bounded sections" ], "attribution": "Original figure source: PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining by Yuting Gao, Jinfeng Liu, Zihan Xu, Jun Zhang, Ke Li, Rongrong Ji, and Chunhua She - Network accessline 136
], "attribution": "Original figure source: PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining by Yuting Gao, Jinfeng Liu, Zihan Xu, Jun Zhang, Ke Li, Rongrong Ji, and Chunhua Shen, arXiv:2204.14095; orig - Network accessline 137
"attribution": "Original figure source: PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining by Yuting Gao, Jinfeng Liu, Zihan Xu, Jun Zhang, Ke Li, Rongrong Ji, and Chunhua Shen, arXiv:2204.14095; original-figu - Network accessline 156
"simple arrows" ], "attribution": "Original figure source: Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation by Shiqi Yang, Yaxing Wang, Kai Wang, Shangling Jui, and Joost van de Weijer, arXiv:2205.0 - Network accessline 157
], "attribution": "Original figure source: Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation by Shiqi Yang, Yaxing Wang, Kai Wang, Shangling Jui, and Joost van de Weijer, arXiv:2205.04183; original-figure - Network accessline 158
"attribution": "Original figure source: Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation by Shiqi Yang, Yaxing Wang, Kai Wang, Shangling Jui, and Joost van de Weijer, arXiv:2205.04183; original-figure license: - Network accessline 177
"nested enclosures" ], "attribution": "Original figure source: Improving Multi-Task Generalization via Regularizing Spurious Correlation by Ziniu Hu, Zhe Zhao, Xinyang Yi, Tiansheng Yao, Lichan Hong, Yizhou Sun, and Ed H. Chi, a - Network accessline 178
], "attribution": "Original figure source: Improving Multi-Task Generalization via Regularizing Spurious Correlation by Ziniu Hu, Zhe Zhao, Xinyang Yi, Tiansheng Yao, Lichan Hong, Yizhou Sun, and Ed H. Chi, arXiv:2205.09797; original- - Network accessline 179
"attribution": "Original figure source: Improving Multi-Task Generalization via Regularizing Spurious Correlation by Ziniu Hu, Zhe Zhao, Xinyang Yi, Tiansheng Yao, Lichan Hong, Yizhou Sun, and Ed H. Chi, arXiv:2205.09797; original-figure li - Network accessline 198
"aligned rows" ], "attribution": "Original figure source: Knowledge Distillation from A Stronger Teacher by Tao Huang, Shan You, Fei Wang, Chen Qian, and Chang Xu, arXiv:2205.10536; original-figure license: CC-BY-4.0; evidence: - Network accessline 199
], "attribution": "Original figure source: Knowledge Distillation from A Stronger Teacher by Tao Huang, Shan You, Fei Wang, Chen Qian, and Chang Xu, arXiv:2205.10536; original-figure license: CC-BY-4.0; evidence: https://arxiv.org/abs - Network accessline 200
"attribution": "Original figure source: Knowledge Distillation from A Stronger Teacher by Tao Huang, Shan You, Fei Wang, Chen Qian, and Chang Xu, arXiv:2205.10536; original-figure license: CC-BY-4.0; evidence: https://arxiv.org/abs/2205.105 - Network accessline 219
"stacked modules" ], "attribution": "Original figure source: NS3: Neuro-Symbolic Semantic Code Search by Shushan Arakelyan, Anna Hakhverdyan, Miltiadis Allamanis, Luis Garcia, Christophe Hauser, and Xiang Ren, arXiv:2205.10674; - Network accessline 220
], "attribution": "Original figure source: NS3: Neuro-Symbolic Semantic Code Search by Shushan Arakelyan, Anna Hakhverdyan, Miltiadis Allamanis, Luis Garcia, Christophe Hauser, and Xiang Ren, arXiv:2205.10674; original-figure license: - Network accessline 221
"attribution": "Original figure source: NS3: Neuro-Symbolic Semantic Code Search by Shushan Arakelyan, Anna Hakhverdyan, Miltiadis Allamanis, Luis Garcia, Christophe Hauser, and Xiang Ren, arXiv:2205.10674; original-figure license: CC-BY-4. - Network accessline 240
"orthogonal connectors" ], "attribution": "Original figure source: Test-Time Robust Personalization for Federated Learning by Liangze Jiang and Tao Lin, arXiv:2205.10920; original-figure license: CC-BY-4.0; evidence: https://arx - Network accessline 241
], "attribution": "Original figure source: Test-Time Robust Personalization for Federated Learning by Liangze Jiang and Tao Lin, arXiv:2205.10920; original-figure license: CC-BY-4.0; evidence: https://arxiv.org/abs/2205.10920. FigureB - Network accessline 242
"attribution": "Original figure source: Test-Time Robust Personalization for Federated Learning by Liangze Jiang and Tao Lin, arXiv:2205.10920; original-figure license: CC-BY-4.0; evidence: https://arxiv.org/abs/2205.10920. FigureBench data - Network accessline 261
"repeated rows" ], "attribution": "Original figure source: Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling by Kaitao Song, Yichong Leng, Xu Tan, Yicheng Zou, Tao Qin, and Dongsheng Li, arXiv:2205.129 - Network accessline 262
], "attribution": "Original figure source: Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling by Kaitao Song, Yichong Leng, Xu Tan, Yicheng Zou, Tao Qin, and Dongsheng Li, arXiv:2205.12986; original-figure li - Network accessline 263
"attribution": "Original figure source: Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling by Kaitao Song, Yichong Leng, Xu Tan, Yicheng Zou, Tao Qin, and Dongsheng Li, arXiv:2205.12986; original-figure license: CC - Network accessline 282
"color-coded connectors" ], "attribution": "Original figure source: FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction by Yongxin Guo, Xiaoying Tang, and Tao Lin, arXiv:2205.13462; origin - Network accessline 283
], "attribution": "Original figure source: FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction by Yongxin Guo, Xiaoying Tang, and Tao Lin, arXiv:2205.13462; original-figure license: CC-BY-4.0; e - Network accessline 284
"attribution": "Original figure source: FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction by Yongxin Guo, Xiaoying Tang, and Tao Lin, arXiv:2205.13462; original-figure license: CC-BY-4.0; evidence: - Network accessline 303
"dashed enclosures" ], "attribution": "Original figure source: Masked Distillation with Receptive Tokens by Tao Huang, Yuan Zhang, Shan You, Fei Wang, Chen Qian, Jian Cao, and Chang Xu, arXiv:2205.14589; original-figure license: - Network accessline 304
], "attribution": "Original figure source: Masked Distillation with Receptive Tokens by Tao Huang, Yuan Zhang, Shan You, Fei Wang, Chen Qian, Jian Cao, and Chang Xu, arXiv:2205.14589; original-figure license: CC-BY-4.0; evidence: http - Network accessline 305
"attribution": "Original figure source: Masked Distillation with Receptive Tokens by Tao Huang, Yuan Zhang, Shan You, Fei Wang, Chen Qian, Jian Cao, and Chang Xu, arXiv:2205.14589; original-figure license: CC-BY-4.0; evidence: https://arxiv - Network accessline 324
"color-coded connectors" ], "attribution": "Original figure source: Prompt-aligned Gradient for Prompt Tuning by Beier Zhu, Yulei Niu, Yucheng Han, Yue Wu, and Hanwang Zhang, arXiv:2205.14865; original-figure license: CC-BY-4.0; - Network accessline 325
], "attribution": "Original figure source: Prompt-aligned Gradient for Prompt Tuning by Beier Zhu, Yulei Niu, Yucheng Han, Yue Wu, and Hanwang Zhang, arXiv:2205.14865; original-figure license: CC-BY-4.0; evidence: https://arxiv.org/ab - Network accessline 326
"attribution": "Original figure source: Prompt-aligned Gradient for Prompt Tuning by Beier Zhu, Yulei Niu, Yucheng Han, Yue Wu, and Hanwang Zhang, arXiv:2205.14865; original-figure license: CC-BY-4.0; evidence: https://arxiv.org/abs/2205.14 - Network accessline 345
"repeated stages" ], "attribution": "Original figure source: EfficientFormer: Vision Transformers at MobileNet Speed by Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, and Jian Ren, arXi - Network accessline 346
], "attribution": "Original figure source: EfficientFormer: Vision Transformers at MobileNet Speed by Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, and Jian Ren, arXiv:2206.01191; original-f - Network accessline 347
"attribution": "Original figure source: EfficientFormer: Vision Transformers at MobileNet Speed by Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, and Jian Ren, arXiv:2206.01191; original-figure lic - Network accessline 366
"dashed enclosures" ], "attribution": "Original figure source: Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering by Yongyi Su, Xun Xu, and Kui Jia, arXiv:2206.02721; original-fig - Network accessline 367
], "attribution": "Original figure source: Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering by Yongyi Su, Xun Xu, and Kui Jia, arXiv:2206.02721; original-figure license: CC-BY-4.0; ev - Network accessline 368
"attribution": "Original figure source: Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering by Yongyi Su, Xun Xu, and Kui Jia, arXiv:2206.02721; original-figure license: CC-BY-4.0; evidence: h - Network accessline 387
"bounded sections" ], "attribution": "Original figure source: Neuro-Symbolic Procedural Planning with Commonsense Prompting by Yujie Lu, Weixi Feng, Wanrong Zhu, Wenda Xu, Xin Eric Wang, Miguel Eckstein, and William Yang Wang, a - Network accessline 388
], "attribution": "Original figure source: Neuro-Symbolic Procedural Planning with Commonsense Prompting by Yujie Lu, Weixi Feng, Wanrong Zhu, Wenda Xu, Xin Eric Wang, Miguel Eckstein, and William Yang Wang, arXiv:2206.02928; original - Network accessline 389
"attribution": "Original figure source: Neuro-Symbolic Procedural Planning with Commonsense Prompting by Yujie Lu, Weixi Feng, Wanrong Zhu, Wenda Xu, Xin Eric Wang, Miguel Eckstein, and William Yang Wang, arXiv:2206.02928; original-figure l - Network accessline 408
"dashed enclosures" ], "attribution": "Original figure source: Masked Unsupervised Self-training for Label-free Image Classification by Junnan Li, Silvio Savarese, and Steven C. H. Hoi, arXiv:2206.02967; original-figure license: - Network accessline 409
], "attribution": "Original figure source: Masked Unsupervised Self-training for Label-free Image Classification by Junnan Li, Silvio Savarese, and Steven C. H. Hoi, arXiv:2206.02967; original-figure license: CC-BY-4.0; evidence: http - Network accessline 410
"attribution": "Original figure source: Masked Unsupervised Self-training for Label-free Image Classification by Junnan Li, Silvio Savarese, and Steven C. H. Hoi, arXiv:2206.02967; original-figure license: CC-BY-4.0; evidence: https://arxiv - Network accessline 429
"symmetric branches" ], "attribution": "Original figure source: Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models by Yang Shu, Zhangjie Cao, Ziyang Zhang, Jianmin Wang, and Mingsheng Long, arXiv:2206.03726; origina - Network accessline 430
], "attribution": "Original figure source: Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models by Yang Shu, Zhangjie Cao, Ziyang Zhang, Jianmin Wang, and Mingsheng Long, arXiv:2206.03726; original-figure license: CC-BY-4.0 - Network accessline 431
"attribution": "Original figure source: Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models by Yang Shu, Zhangjie Cao, Ziyang Zhang, Jianmin Wang, and Mingsheng Long, arXiv:2206.03726; original-figure license: CC-BY-4.0; evidenc - Network accessline 450
"mirrored modules" ], "attribution": "Original figure source: Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone by Zi-Yi Dou, Aishwarya Kamath, Zhe Gan, Pengchuan Zhang, Jianfeng Wang, Linjie Li, Zicheng Li - Network accessline 451
], "attribution": "Original figure source: Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone by Zi-Yi Dou, Aishwarya Kamath, Zhe Gan, Pengchuan Zhang, Jianfeng Wang, Linjie Li, Zicheng Liu, Ce Liu, Yann LeCun, Na - Network accessline 452
"attribution": "Original figure source: Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone by Zi-Yi Dou, Aishwarya Kamath, Zhe Gan, Pengchuan Zhang, Jianfeng Wang, Linjie Li, Zicheng Liu, Ce Liu, Yann LeCun, Nanyun Peng - Network accessline 471
"orthogonal connectors" ], "attribution": "Original figure source: Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks by Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, and Aniruddha Kembhavi, - Network accessline 472
], "attribution": "Original figure source: Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks by Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, and Aniruddha Kembhavi, arXiv:2206.08916; original-fi - Network accessline 473
"attribution": "Original figure source: Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks by Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, and Aniruddha Kembhavi, arXiv:2206.08916; original-figure lice - Network accessline 492
"repeated layers" ], "attribution": "Original figure source: ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning by Junting Pan, Ziyi Lin, Xiatian Zhu, Jing Shao, and Hongsheng Li, arXiv:2206.13559; original-figure - Network accessline 493
], "attribution": "Original figure source: ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning by Junting Pan, Ziyi Lin, Xiatian Zhu, Jing Shao, and Hongsheng Li, arXiv:2206.13559; original-figure license: CC-BY-4.0; evid - Network accessline 494
"attribution": "Original figure source: ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning by Junting Pan, Ziyi Lin, Xiatian Zhu, Jing Shao, and Hongsheng Li, arXiv:2206.13559; original-figure license: CC-BY-4.0; evidence: htt - Network accessline 513
"repeated node-link grammar" ], "attribution": "Original figure source: Graph Generative Model for Benchmarking Graph Neural Networks by Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, and Ruslan Salakhutdinov, arXiv:2207.043 - Network accessline 514
], "attribution": "Original figure source: Graph Generative Model for Benchmarking Graph Neural Networks by Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, and Ruslan Salakhutdinov, arXiv:2207.04396; original-figure license: CC-BY- - Network accessline 515
"attribution": "Original figure source: Graph Generative Model for Benchmarking Graph Neural Networks by Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, and Ruslan Salakhutdinov, arXiv:2207.04396; original-figure license: CC-BY-4.0; evid - Network accessline 534
"linear sequence" ], "attribution": "Original figure source: LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval by Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Xiaolong Huang, Binxing Jiao, Linjun Yang, and Daxin - Network accessline 535
], "attribution": "Original figure source: LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval by Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Xiaolong Huang, Binxing Jiao, Linjun Yang, and Daxin Jiang, arXiv:2208.14754 - Network accessline 536
"attribution": "Original figure source: LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval by Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Xiaolong Huang, Binxing Jiao, Linjun Yang, and Daxin Jiang, arXiv:2208.14754; origina - Network accessline 555
"bounded sections" ], "attribution": "Original figure source: GAMA: Generative Adversarial Multi-Object Scene Attacks by Abhishek Aich, Calvin-Khang Ta, Akash Gupta, Chengyu Song, Srikanth V. Krishnamurthy, M. Salman Asif, and A - Network accessline 556
], "attribution": "Original figure source: GAMA: Generative Adversarial Multi-Object Scene Attacks by Abhishek Aich, Calvin-Khang Ta, Akash Gupta, Chengyu Song, Srikanth V. Krishnamurthy, M. Salman Asif, and Amit K. Roy-Chowdhury, arX - Network accessline 557
"attribution": "Original figure source: GAMA: Generative Adversarial Multi-Object Scene Attacks by Abhishek Aich, Calvin-Khang Ta, Akash Gupta, Chengyu Song, Srikanth V. Krishnamurthy, M. Salman Asif, and Amit K. Roy-Chowdhury, arXiv:2209.0 - Network accessline 576
"aligned stages" ], "attribution": "Original figure source: Promptagator: Few-shot Dense Retrieval From 8 Examples by Zhuyun Dai, Vincent Y. Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith B. Hall, and - Network accessline 577
], "attribution": "Original figure source: Promptagator: Few-shot Dense Retrieval From 8 Examples by Zhuyun Dai, Vincent Y. Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith B. Hall, and Ming-Wei Chang, arXiv: - Network accessline 578
"attribution": "Original figure source: Promptagator: Few-shot Dense Retrieval From 8 Examples by Zhuyun Dai, Vincent Y. Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith B. Hall, and Ming-Wei Chang, arXiv:2209.1175 - Network accessline 597
"bounded sections" ], "attribution": "Original figure source: Exploring Example Influence in Continual Learning by Qing Sun, Fan Lyu, Fanhua Shang, Wei Feng, and Liang Wan, arXiv:2209.12241; original-figure license: CC-BY-4.0; e - Network accessline 598
], "attribution": "Original figure source: Exploring Example Influence in Continual Learning by Qing Sun, Fan Lyu, Fanhua Shang, Wei Feng, and Liang Wan, arXiv:2209.12241; original-figure license: CC-BY-4.0; evidence: https://arxiv.or - Network accessline 599
"attribution": "Original figure source: Exploring Example Influence in Continual Learning by Qing Sun, Fan Lyu, Fanhua Shang, Wei Feng, and Liang Wan, arXiv:2209.12241; original-figure license: CC-BY-4.0; evidence: https://arxiv.org/abs/220 - Network accessline 618
"orthogonal connectors" ], "attribution": "Original figure source: Multi-dataset Training of Transformers for Robust Action Recognition by Junwei Liang, Enwei Zhang, Jun Zhang, and Chunhua Shen, arXiv:2209.12362; original-figure - Network accessline 619
], "attribution": "Original figure source: Multi-dataset Training of Transformers for Robust Action Recognition by Junwei Liang, Enwei Zhang, Jun Zhang, and Chunhua Shen, arXiv:2209.12362; original-figure license: CC-BY-4.0; evidence: - Network accessline 620
"attribution": "Original figure source: Multi-dataset Training of Transformers for Robust Action Recognition by Junwei Liang, Enwei Zhang, Jun Zhang, and Chunhua Shen, arXiv:2209.12362; original-figure license: CC-BY-4.0; evidence: https://
references/artifact-schemas.md
- Network accessline 164
"path": "references/web/crops/creative-director/intermediate-shape-language.png", "target_component_id": "audio", "source_url": "https://arxiv.org/html/2402.14285", - Network accessline 165
"target_component_id": "audio", "source_url": "https://arxiv.org/html/2402.14285", "evidence_url": "https://arxiv.org/html/2402.14285v4/figure.svg", - Network accessline 166
"source_url": "https://arxiv.org/html/2402.14285", - Network accessline 167
"evidence_url": "https://arxiv.org/html/2402.14285v4/figure.svg",
scientific_figure_workflow/__init__.py
- Executable scriptline 1
Executable file
scientific_figure_workflow/artifacts.py
- Executable scriptline 1
Executable file
scientific_figure_workflow/palette.py
- Executable scriptline 1
Executable file
scientific_figure_workflow/prompts.py
- Executable scriptline 1
Executable file
scientific_figure_workflow/reference_pack.py
- Executable scriptline 1
Executable file - Network accessline 34
"FigureBench dataset license (separate; does not determine original-figure rights): " "CC-BY-4.0; metadata: " "https://huggingface.co/datasets/WestlakeNLP/FigureBench/blob/main/README.md." - Network accessline 35
"CC-BY-4.0; metadata: " "https://huggingface.co/datasets/WestlakeNLP/FigureBench/blob/main/README.md." ) - Network accessline 36
"https://huggingface.co/datasets/WestlakeNLP/FigureBench/blob/main/README.md."
scientific_figure_workflow/run_validation.py
- Executable scriptline 1
Executable file
scripts/check_installation.py
- Executable scriptline 1
#!/usr/bin/env python3
scripts/curate_figurebench_reference_pack.py
- Executable scriptline 1
#!/usr/bin/env python3
scripts/figure_workflow.py
- Executable scriptline 1
#!/usr/bin/env python3
tests/fixtures/editable.svg
- Network accessline 1
<svg xmlns="http://www.w3.org/2000/svg" width="160" height="100" viewBox="0 0 160 100">
tests/fixtures/raster-wrapper.svg
- Network accessline 1
<svg xmlns="http://www.w3.org/2000/svg" width="20" height="20">
tests/test_artifacts.py
- Executable scriptline 1
Executable file - Network accessline 179
"path": "references/web/crops/creative-director/idea.png", "target_component_id": "encoder", "source_url": "https://arxiv.org/abs/1234.5678", - Network accessline 180
"target_component_id": "encoder", "source_url": "https://arxiv.org/abs/1234.5678", "evidence_url": "https://arxiv.org/html/1234.5678/figure.svg", - Network accessline 181
"source_url": "https://arxiv.org/abs/1234.5678", - Network accessline 182
"evidence_url": "https://arxiv.org/html/1234.5678/figure.svg", - Network accessline 188
}) self.assertEqual(validate_creative_director(brief, root, {"encoder", "audio"})["svg_evidence_status"], "paper_svg_crops_verified") brief["ideas"][0]["svg_crops"][0]["source_url"] = "http://example.test" - Network accessline 189
self.assertEqual(validate_creative_director(brief, root, {"encoder", "audio"})["svg_evidence_status"], "paper_svg_crops_verified") brief["ideas"][0]["svg_crops"][0]["source_url"] = "http://example.test" with self.ass - Network accessline 190
brief["ideas"][0]["svg_crops"][0]["source_url"] = "http://example.test"
tests/test_cli.py
- Executable scriptline 1
Executable file - Network accessline 55
"format": "scholarly-domain-figure-manifest-v1", "sources": [ {"id": "paper-1", "title": "Fixture one", "figure": "Figure 1", "source_url": "https://arxiv.org/abs/0001.00001", "evidence_url": "https://arxiv.org/html/0001 - Network accessline 56
"sources": [ {"id": "paper-1", "title": "Fixture one", "figure": "Figure 1", "source_url": "https://arxiv.org/abs/0001.00001", "evidence_url": "https://arxiv.org/html/0001.00001/figure.svg", "crop_path": "references/web/crops/pi - Network accessline 57
{"id": "paper-1", "title": "Fixture one", "figure": "Figure 1", "source_url": "https://arxiv.org/abs/0001.00001", "evidence_url": "https://arxiv.org/html/0001.00001/figure.svg", "crop_path": "references/web/crops/piano-roll.png", "inspectio - Network accessline 58
{"id": "paper-2", "title": "Fixture two", "figure": "Figure 2", "source_url": "https://arxiv.org/abs/0001.00002", "evidence_url": "https://arxiv.org/html/0001.00002/figure.svg", "crop_path": "references/web/crops/paper-2.png", "inspection": - Network accessline 59
{"id": "paper-3", "title": "Fixture three", "figure": "Figure 3", "source_url": "https://arxiv.org/abs/0001.00003", "evidence_url": "https://arxiv.org/html/0001.00003/figure.svg", "crop_path": "references/web/crops/paper-3.png", "inspection
tests/test_installation.py
tests/test_palette.py
- Executable scriptline 1
Executable file - Network accessline 43
"rgb": [110, 143, 163], "relationship": "analogous_neighbour", "evidence_url": "https://color.adobe.com/create/color-wheel", - Network accessline 44
"relationship": "analogous_neighbour", "evidence_url": "https://color.adobe.com/create/color-wheel", "evidence_summary": "Blue-grey adjacent tone compatible with the base blue family.", - Network accessline 45
"evidence_url": "https://color.adobe.com/create/color-wheel", - Network accessline 118
("role", "", "non-empty role"), ("relationship", "unrelated", "relationship"), ("evidence_url", "http://example.test/evidence", "HTTPS evidence_url"), - Network accessline 119
("relationship", "unrelated", "relationship"), ("evidence_url", "http://example.test/evidence", "HTTPS evidence_url"), ("evidence_summary", "", "evidence_summary"), - Network accessline 120
("evidence_url", "http://example.test/evidence", "HTTPS evidence_url"),
tests/test_prompts.py
- Executable scriptline 1
Executable file - Network accessline 113
"path": "references/web/crops/creative-director/intermediate-shape-language.png", "target_component_id": "audio", "source_url": "https://arxiv.org/html/2402.14285", - Network accessline 114
"target_component_id": "audio", "source_url": "https://arxiv.org/html/2402.14285", "evidence_url": "https://arxiv.org/html/2402.14285v4/figure.svg", - Network accessline 115
"source_url": "https://arxiv.org/html/2402.14285", - Network accessline 116
"evidence_url": "https://arxiv.org/html/2402.14285v4/figure.svg",
tests/test_reference_pack.py
- Executable scriptline 1
Executable file - Network accessline 22
return ( f"Original figure source: arXiv:{source_id}; original-figure license: CC-BY-4.0; " f"evidence: https://arxiv.org/abs/{source_id}. " - Network accessline 23
f"Original figure source: arXiv:{source_id}; original-figure license: CC-BY-4.0; " f"evidence: https://arxiv.org/abs/{source_id}. " "FigureBench dataset license (separate; does not determine original-figure rights): " - Network accessline 24
f"evidence: https://arxiv.org/abs/{source_id}. " - Network accessline 25
"FigureBench dataset license (separate; does not determine original-figure rights): " "CC-BY-4.0; metadata: " "https://huggingface.co/datasets/WestlakeNLP/FigureBench/blob/main/README.md." - Network accessline 26
"CC-BY-4.0; metadata: " "https://huggingface.co/datasets/WestlakeNLP/FigureBench/blob/main/README.md." ) - Network accessline 27
"https://huggingface.co/datasets/WestlakeNLP/FigureBench/blob/main/README.md." - Network accessline 126
record["attribution"] = ( f"Original figure source: {record['source_id']}; original-figure license: " "CC-BY-4.0; evidence: https://proceedings.mlr.press/v202/example.html. " - Network accessline 127
f"Original figure source: {record['source_id']}; original-figure license: " "CC-BY-4.0; evidence: https://proceedings.mlr.press/v202/example.html. " "FigureBench dataset license (separate; does not de - Network accessline 128
"CC-BY-4.0; evidence: https://proceedings.mlr.press/v202/example.html. " - Network accessline 129
"FigureBench dataset license (separate; does not determine original-figure " "rights): CC-BY-4.0; metadata: " "https://huggingface.co/datasets/WestlakeNLP/FigureBench/blob/main/README.md." - Network accessline 130
"rights): CC-BY-4.0; metadata: " "https://huggingface.co/datasets/WestlakeNLP/FigureBench/blob/main/README.md." ) - Network accessline 131
"https://huggingface.co/datasets/WestlakeNLP/FigureBench/blob/main/README.md."
tests/test_reference_pack_cli.py
- Executable scriptline 1
Executable file - Network accessline 21
return ( f"Original figure source: arXiv:{source_id}; original-figure license: CC-BY-4.0; " f"evidence: https://arxiv.org/abs/{source_id}. " - Network accessline 22
f"Original figure source: arXiv:{source_id}; original-figure license: CC-BY-4.0; " f"evidence: https://arxiv.org/abs/{source_id}. " "FigureBench dataset license (separate; does not determine original-figure rights): " - Network accessline 23
f"evidence: https://arxiv.org/abs/{source_id}. " - Network accessline 24
"FigureBench dataset license (separate; does not determine original-figure rights): " "CC-BY-4.0; metadata: " "https://huggingface.co/datasets/WestlakeNLP/FigureBench/blob/main/README.md." - Network accessline 25
"CC-BY-4.0; metadata: " "https://huggingface.co/datasets/WestlakeNLP/FigureBench/blob/main/README.md." ) - Network accessline 26
"https://huggingface.co/datasets/WestlakeNLP/FigureBench/blob/main/README.md."
tests/test_run_validation.py
- Executable scriptline 1
Executable file
How the signals fit together
Facts from a static read of the files at the pinned commit. They describe what the code does, not why.
Some files could not be read; the facts below still hold.
Declared and executed: 1 fact
- Contacts huggingface.co, which SKILL.md, the README and the plugin manifest do not mentionscientific_figure_workflow/reference_pack.py:36High confidence
Signal confidence: 78 facts
- Network access in prose of documentationREADME.md:101Low confidence
- Network access in prose of documentationREADME.md:102Low confidence
- Network access in prose of documentationREADME.md:103Low confidence
- Network access in prose of documentationREADME.md:104Low confidence
- Network access in prose of documentationREADME.md:105Low confidence
- Network access in prose of documentationREADME.md:212Low confidence
- Network access in prose of documentationREADME.md:229Low confidence
- Network access in prose of documentationREADME.md:246Low confidence
- Network access in prose of documentationREADME_ZH.md:77Low confidence
- Network access in prose of documentationREADME_ZH.md:78Low confidence
- Network access in prose of documentationREADME_ZH.md:79Low confidence
- Network access in prose of documentationREADME_ZH.md:80Low confidence
- Network access in prose of documentationREADME_ZH.md:81Low confidence
- Network access in code of documentationreferences/artifact-schemas.md:164Low confidence
- Network access in code of documentationreferences/artifact-schemas.md:165Low confidence
- Network access in code of documentationreferences/artifact-schemas.md:166Low confidence
- Network access in code of documentationreferences/artifact-schemas.md:167Low confidence
- Executable script in code of a file that runsscientific_figure_workflow/__init__.py:1High confidence
- Executable script in prose of a file that runsscientific_figure_workflow/artifacts.py:1Low confidence
- Executable script in prose of a file that runsscientific_figure_workflow/palette.py:1Low confidence
- Executable script in prose of a file that runsscientific_figure_workflow/prompts.py:1Low confidence
- Executable script in prose of a file that runsscientific_figure_workflow/reference_pack.py:1Low confidence
- Network access in code of a file that runsscientific_figure_workflow/reference_pack.py:34High confidence
- Network access in code of a file that runsscientific_figure_workflow/reference_pack.py:35High confidence
- Network access in code of a file that runsscientific_figure_workflow/reference_pack.py:36High confidence
- Executable script in prose of a file that runsscientific_figure_workflow/run_validation.py:1Low confidence
- Executable script in code of a file that runsscripts/check_installation.py:1High confidence
- Executable script in code of a file that runsscripts/curate_figurebench_reference_pack.py:1High confidence
- Executable script in code of a file that runsscripts/figure_workflow.py:1High confidence
- Executable script in code of a testtests/test_artifacts.py:1Low confidence
- Network access in code of a testtests/test_artifacts.py:179Low confidence
- Network access in code of a testtests/test_artifacts.py:180Low confidence
- Network access in code of a testtests/test_artifacts.py:181Low confidence
- Network access in code of a testtests/test_artifacts.py:182Low confidence
- Network access in code of a testtests/test_artifacts.py:188Low confidence
- Network access in code of a testtests/test_artifacts.py:189Low confidence
- Network access in code of a testtests/test_artifacts.py:190Low confidence
- Executable script in code of a testtests/test_cli.py:1Low confidence
- Network access in code of a testtests/test_cli.py:55Low confidence
- Network access in code of a testtests/test_cli.py:56Low confidence
- Network access in code of a testtests/test_cli.py:57Low confidence
- Network access in code of a testtests/test_cli.py:58Low confidence
- Network access in code of a testtests/test_cli.py:59Low confidence
- Executable script in code of a testtests/test_installation.py:1Low confidence
- Access to secrets in code of a test: a variable, placeholder or call, not a stored valuetests/test_installation.py:186Low confidence
- Executable script in code of a testtests/test_palette.py:1Low confidence
- Network access in code of a testtests/test_palette.py:43Low confidence
- Network access in code of a testtests/test_palette.py:44Low confidence
- Network access in code of a testtests/test_palette.py:45Low confidence
- Network access in code of a testtests/test_palette.py:118Low confidence
and 28 more
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Unknown | No evidence | |
| Codex | Unknown | No evidence | |
| Cursor | Unknown | No evidence | |
| Gemini CLI | Unknown | No evidence | |
| GitHub Copilot | Unknown | No evidence | |
| OpenCode | Unknown | No evidence |
- Native The repository ships a manifest, folder or frontmatter field for this agent.
- Compatible A standard SKILL.md that this agent can read.
- Unknown No evidence either way.
Derived automatically from paths, manifests and frontmatter. Not tested on each agent.
