[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EWQPQ2K7RETMSRA86V77T1":3,"skill-same:01M6EWQPQ2K7RETMSRA86V77T1":96},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":14,"repositoryHtmlUrl":59,"repositoryHomepageUrl":60,"ownerHtmlUrl":61,"ownerBlogUrl":6,"ownerDescription":62,"updatedAt":63,"install":64,"resources":77},"01M6EWQPQ2K7RETMSRA86V77T1","axolotl",null,"Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA\u002FQLoRA, DPO\u002FKTO\u002FORPO\u002FGRPO, multimodal support","henriquescastilho\u002Fmy-claude","henriquescastilho","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F153296945?v=4","01M6ESH7EXPXTH9CVQ4K16SRG8","Henrique Scheer","01M6ESH7JHEK2PTNNH4HEH787Z",0,"MIT","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-code-templates\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ffine-tuning-axolotl\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"Axolotl","ORPO","with","Accelerate","expert","datasets","models","tuning","Transformers","HuggingFace","GRPO","llms","KTO","DPO","QLoRA","LoRA","Fine-Tuning","DeepSpeed","support","research","2026-11-10 10:12:30",false,"---\nname: axolotl\ndescription: Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA\u002FQLoRA, DPO\u002FKTO\u002FORPO\u002FGRPO, multimodal support\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Fine-Tuning, Axolotl, LLM, LoRA, QLoRA, DPO, KTO, ORPO, GRPO, YAML, HuggingFace, DeepSpeed, Multimodal]\ndependencies: [axolotl, torch, transformers, datasets, peft, accelerate, deepspeed]\n---\n\n# Axolotl Skill\n\nComprehensive assistance with axolotl development, generated from official documentation.\n\n## When to Use This Skill\n\nThis skill should be triggered when:\n- Working with axolotl\n- Asking about axolotl features or APIs\n- Implementing axolotl solutions\n- Debugging axolotl code\n- Learning axolotl best practices\n\n## Quick Reference\n\n### Common Patterns\n\n**Pattern 1:** To validate that acceptable data transfer speeds exist for your training job, running NCCL Tests can help pinpoint bottlenecks, for example:\n\n```\n.\u002Fbuild\u002Fall_reduce_perf -b 8 -e 128M -f 2 -g 3\n```\n\n**Pattern 2:** Configure your model to use FSDP in the Axolotl yaml. For example:\n\n```\nfsdp_version: 2\nfsdp_config:\n  offload_params: true\n  state_dict_type: FULL_STATE_DICT\n  auto_wrap_policy: TRANSFORMER_BASED_WRAP\n  transformer_layer_cls_to_wrap: LlamaDecoderLayer\n  reshard_after_forward: true\n```\n\n**Pattern 3:** The context_parallel_size should be a divisor of the total number of GPUs. For example:\n\n```\ncontext_parallel_size\n```\n\n**Pattern 4:** For example: - With 8 GPUs and no sequence parallelism: 8 different batches processed per step - With 8 GPUs and context_parallel_size=4: Only 2 different batches processed per step (each split across 4 GPUs) - If your per-GPU micro_batch_size is 2, the global batch size decreases from 16 to 4\n\n```\ncontext_parallel_size=4\n```\n\n**Pattern 5:** Setting save_compressed: true in your configuration enables saving models in a compressed format, which: - Reduces disk space usage by approximately 40% - Maintains compatibility with vLLM for accelerated inference - Maintains compatibility with llmcompressor for further optimization (example: quantization)\n\n```\nsave_compressed: true\n```\n\n**Pattern 6:** Note It is not necessary to place your integration in the integrations folder. It can be in any location, so long as it’s installed in a package in your python env. See this repo for an example: https:\u002F\u002Fgithub.com\u002Faxolotl-ai-cloud\u002Fdiff-transformer\n\n```\nintegrations\n```\n\n**Pattern 7:** Handle both single-example and batched data. - single example: sample[‘input_ids’] is a list[int] - batched data: sample[‘input_ids’] is a list[list[int]]\n\n```\nutils.trainer.drop_long_seq(sample, sequence_len=2048, min_sequence_len=2)\n```\n\n### Example Code Patterns\n\n**Example 1** (python):\n```python\ncli.cloud.modal_.ModalCloud(config, app=None)\n```\n\n**Example 2** (python):\n```python\ncli.cloud.modal_.run_cmd(cmd, run_folder, volumes=None)\n```\n\n**Example 3** (python):\n```python\ncore.trainers.base.AxolotlTrainer(\n    *_args,\n    bench_data_collator=None,\n    eval_data_collator=None,\n    dataset_tags=None,\n    **kwargs,\n)\n```\n\n**Example 4** (python):\n```python\ncore.trainers.base.AxolotlTrainer.log(logs, start_time=None)\n```\n\n**Example 5** (python):\n```python\nprompt_strategies.input_output.RawInputOutputPrompter()\n```\n\n## Reference Files\n\nThis skill includes comprehensive documentation in `references\u002F`:\n\n- **api.md** - Api documentation\n- **dataset-formats.md** - Dataset-Formats documentation\n- **other.md** - Other documentation\n\nUse `view` to read specific reference files when detailed information is needed.\n\n## Working with This Skill\n\n### For Beginners\nStart with the getting_started or tutorials reference files for foundational concepts.\n\n### For Specific Features\nUse the appropriate category reference file (api, guides, etc.) for detailed information.\n\n### For Code Examples\nThe quick reference section above contains common patterns extracted from the official docs.\n\n## Resources\n\n### references\u002F\nOrganized documentation extracted from official sources. These files contain:\n- Detailed explanations\n- Code examples with language annotations\n- Links to original documentation\n- Table of contents for quick navigation\n\n### scripts\u002F\nAdd helper scripts here for common automation tasks.\n\n### assets\u002F\nAdd templates, boilerplate, or example projects here.\n\n## Notes\n\n- This skill was automatically generated from official documentation\n- Reference files preserve the structure and examples from source docs\n- Code examples include language detection for better syntax highlighting\n- Quick reference patterns are extracted from common usage examples in the docs\n\n## Updating\n\nTo refresh this skill with updated documentation:\n1. Re-run the scraper with the same configuration\n2. The skill will be rebuilt with the latest information\n\n\n","# Axolotl Skill\n\nComprehensive assistance with axolotl development, generated from official documentation.\n\n## When to Use This Skill\n\nThis skill should be triggered when:\n- Working with axolotl\n- Asking about axolotl features or APIs\n- Implementing axolotl solutions\n- Debugging axolotl code\n- Learning axolotl best practices\n\n## Quick Reference\n\n### Common Patterns\n\n**Pattern 1:** To validate that acceptable data transfer speeds exist for your training job, running NCCL Tests can help pinpoint bottlenecks, for example:\n\n```\n.\u002Fbuild\u002Fall_reduce_perf -b 8 -e 128M -f 2 -g 3\n```\n\n**Pattern 2:** Configure your model to use FSDP in the Axolotl yaml. For example:\n\n```\nfsdp_version: 2\nfsdp_config:\n  offload_params: true\n  state_dict_type: FULL_STATE_DICT\n  auto_wrap_policy: TRANSFORMER_BASED_WRAP\n  transformer_layer_cls_to_wrap: LlamaDecoderLayer\n  reshard_after_forward: true\n```\n\n**Pattern 3:** The context_parallel_size should be a divisor of the total number of GPUs. For example:\n\n```\ncontext_parallel_size\n```\n\n**Pattern 4:** For example: - With 8 GPUs and no sequence parallelism: 8 different batches processed per step - With 8 GPUs and context_parallel_size=4: Only 2 different batches processed per step (each split across 4 GPUs) - If your per-GPU micro_batch_size is 2, the global batch size decreases from 16 to 4\n\n```\ncontext_parallel_size=4\n```\n\n**Pattern 5:** Setting save_compressed: true in your configuration enables saving models in a compressed format, which: - Reduces disk space usage by approximately 40% - Maintains compatibility with vLLM for accelerated inference - Maintains compatibility with llmcompressor for further optimization (example: quantization)\n\n```\nsave_compressed: true\n```\n\n**Pattern 6:** Note It is not necessary to place your integration in the integrations folder. It can be in any location, so long as it’s installed in a package in your python env. See this repo for an example: https:\u002F\u002Fgithub.com\u002Faxolotl-ai-cloud\u002Fdiff-transformer\n\n```\nintegrations\n```\n\n**Pattern 7:** Handle both single-example and batched data. - single example: sample[‘input_ids’] is a list[int] - batched data: sample[‘input_ids’] is a list[list[int]]\n\n```\nutils.trainer.drop_long_seq(sample, sequence_len=2048, min_sequence_len=2)\n```\n\n### Example Code Patterns\n\n**Example 1** (python):\n```python\ncli.cloud.modal_.ModalCloud(config, app=None)\n```\n\n**Example 2** (python):\n```python\ncli.cloud.modal_.run_cmd(cmd, run_folder, volumes=None)\n```\n\n**Example 3** (python):\n```python\ncore.trainers.base.AxolotlTrainer(\n    *_args,\n    bench_data_collator=None,\n    eval_data_collator=None,\n    dataset_tags=None,\n    **kwargs,\n)\n```\n\n**Example 4** (python):\n```python\ncore.trainers.base.AxolotlTrainer.log(logs, start_time=None)\n```\n\n**Example 5** (python):\n```python\nprompt_strategies.input_output.RawInputOutputPrompter()\n```\n\n## Reference Files\n\nThis skill includes comprehensive documentation in `references\u002F`:\n\n- **api.md** - Api documentation\n- **dataset-formats.md** - Dataset-Formats documentation\n- **other.md** - Other documentation\n\nUse `view` to read specific reference files when detailed information is needed.\n\n## Working with This Skill\n\n### For Beginners\nStart with the getting_started or tutorials reference files for foundational concepts.\n\n### For Specific Features\nUse the appropriate category reference file (api, guides, etc.) for detailed information.\n\n### For Code Examples\nThe quick reference section above contains common patterns extracted from the official docs.\n\n## Resources\n\n### references\u002F\nOrganized documentation extracted from official sources. These files contain:\n- Detailed explanations\n- Code examples with language annotations\n- Links to original documentation\n- Table of contents for quick navigation\n\n### scripts\u002F\nAdd helper scripts here for common automation tasks.\n\n### assets\u002F\nAdd templates, boilerplate, or example projects here.\n\n## Notes\n\n- This skill was automatically generated from official documentation\n- Reference files preserve the structure and examples from source docs\n- Code examples include language detection for better syntax highlighting\n- Quick reference patterns are extracted from common usage examples in the docs\n\n## Updating\n\nTo refresh this skill with updated documentation:\n1. Re-run the scraper with the same configuration\n2. 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Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-scientific-writer\u002Fskills\u002Fmarkitdown\u002FSKILL.md",[103,108,111,114],{"categoryNo":104,"categoryName":105,"taxonomyType":106,"taxonomyTypeDesc":107},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",{"categoryNo":109,"categoryName":110,"taxonomyType":106,"taxonomyTypeDesc":107},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":112,"categoryName":113,"taxonomyType":106,"taxonomyTypeDesc":107},"01JBSEEDCAT000000000000004","电子表格",{"categoryNo":115,"categoryName":116,"taxonomyType":117,"taxonomyTypeDesc":118},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[99,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,21],"docx","PDF","pptx","xlsx","documents","csv","ocr","Audio","Bash","markdown","convert","read","write","edit","html","images","files","youtube",{"skillNo":139,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":146,"publishedAt":39},"01M6F06SBHR9F0SDWE282Y8075","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-scientific-writer\u002Fscientific_writer\u002F.claude\u002Fskills\u002Fmarkitdown\u002FSKILL.md",[142,143,144,145],{"categoryNo":104,"categoryName":105,"taxonomyType":106,"taxonomyTypeDesc":107},{"categoryNo":109,"categoryName":110,"taxonomyType":106,"taxonomyTypeDesc":107},{"categoryNo":112,"categoryName":113,"taxonomyType":106,"taxonomyTypeDesc":107},{"categoryNo":115,"categoryName":116,"taxonomyType":117,"taxonomyTypeDesc":118},[99,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,21],{"skillNo":148,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":155,"publishedAt":39},"01M6F02D4R14EJAPBWVDF9J9VQ","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-scientific-writer\u002F.claude\u002Fskills\u002Fmarkitdown\u002FSKILL.md",[151,152,153,154],{"categoryNo":104,"categoryName":105,"taxonomyType":106,"taxonomyTypeDesc":107},{"categoryNo":109,"categoryName":110,"taxonomyType":106,"taxonomyTypeDesc":107},{"categoryNo":112,"categoryName":113,"taxonomyType":106,"taxonomyTypeDesc":107},{"categoryNo":115,"categoryName":116,"taxonomyType":117,"taxonomyTypeDesc":118},[99,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,21],{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":162,"publishedAt":39},"01M6F000KS6YB4MKS774G4R8ND","zarr-python","Chunked N-D arrays for cloud storage. Compressed arrays, parallel I\u002FO, S3\u002FGCS integration, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-scientific-skills\u002Fscientific-skills\u002Fzarr-python\u002FSKILL.md",[],[163,164,165,166,167,168,169,170,171,172,173,174,175,176,177],"python","zarr python","storage","integration","s3","gcs","cloud","dask","parallel","pipelines","arrays","numpy","scientific computing","scale","scientific",{"skillNo":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":187,"publishedAt":39},"01M6EZZJWFXM1GR78KTW6J0DN8","what-if-oracle","Run structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like \"what if...\", \"what would happen if...\", \"what are the possibilities\", \"explore scenarios\", \"scenario analysis\", \"possibility space\", \"what could go wrong\", \"best case \u002F worst case\", \"risk analysis\", \"contingency planning\", \"strategic options\", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-scientific-skills\u002Fscientific-skills\u002Fwhat-if-oracle\u002FSKILL.md",[184],{"categoryNo":185,"categoryName":186,"taxonomyType":106,"taxonomyTypeDesc":107},"01JBSEEDCAT000000000000023","Skill与智能体开发",[188,189,190,191,192,193,194,195,196,197,198,131,132,199,200,201,202,203,204,205],"oracle","what if oracle","go","run","ux","futures","analysis","explore","decision","branch","question","idea","exploration","options","stress-test","strategies","scenario","stress",{"skillNo":207,"name":208,"nameZh":6,"description":209,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":210,"categories":211,"keywords":222,"publishedAt":39},"01M6EZZCXAEB49RXGQCJX5E8GB","venue-templates","Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-scientific-skills\u002Fscientific-skills\u002Fvenue-templates\u002FSKILL.md",[212,215,218,219],{"categoryNo":213,"categoryName":214,"taxonomyType":106,"taxonomyTypeDesc":107},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":216,"categoryName":217,"taxonomyType":106,"taxonomyTypeDesc":107},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":185,"categoryName":186,"taxonomyType":106,"taxonomyTypeDesc":107},{"categoryNo":220,"categoryName":221,"taxonomyType":117,"taxonomyTypeDesc":118},"01JBSEEDCAT000000000000036","内容创作者",[223,224,38,225,226,227,228,229,128,230,131,132,133,231,232,233,234,235,177,236],"templates","venue templates","guidelines","NeurIPS","ICML","LaTeX","science","access","proposals","Academic","requirements","formatting","journal","chi"]