[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M78V5DCKR09YRP0SQDP8F5BX":3,"skill-same:01M78V5DCKR09YRP0SQDP8F5BX":87},{"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":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":14,"repositoryHtmlUrl":52,"repositoryHomepageUrl":6,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":54,"install":55,"resources":68},"01M78V5DCKR09YRP0SQDP8F5BX","axolotl",null,"Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA\u002FQLoRA, DPO\u002FKTO\u002FORPO\u002FGRPO, multimodal support","mfrancime\u002Ftrident-archive","mfrancime","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F577553?v=4","01M75NH5W71WGY7NTS8YQ6VP8G","verzillion_kram","01M75NH5WTX2HBXJZWRP4DGAPK",0,"MIT","https:\u002F\u002Fgithub.com\u002Fmfrancime\u002Ftrident-archive\u002Fblob\u002Fmain\u002Frepos\u002Ftrident_hermes-agent_nousresearch_growing_ai\u002Fskills\u002Fmlops\u002Ftraining\u002Faxolotl\u002FSKILL.md",[],[],"2026-11-15 14:19:17",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\ndependencies: [axolotl, torch, transformers, datasets, peft, accelerate, deepspeed]\nmetadata:\n  hermes:\n    tags: [Fine-Tuning, Axolotl, LLM, LoRA, QLoRA, DPO, KTO, ORPO, GRPO, YAML, HuggingFace, DeepSpeed, Multimodal]\n\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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Use when designing, structuring, or packaging skills with scripts, references, and assets.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmfrancime\u002Ftrident-archive\u002Fblob\u002Fmain\u002Frepos\u002Ftrident_orcaclaw_sakana_personal_ai_assistant\u002Fskills\u002Fskill-creator\u002Flicense.txt","https:\u002F\u002Fgithub.com\u002Fmfrancime\u002Ftrident-archive\u002Fblob\u002Fmain\u002Frepos\u002Ftrident_orcaclaw_sakana_personal_ai_assistant\u002Fskills\u002Fskill-creator\u002FSKILL.md",[],[],{"skillNo":98,"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":101,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":19},"01M78VGSK0VQR8DFB181VS4MN8","prose","OpenProse VM skill pack. Activate on any `prose` command, .prose files, or OpenProse mentions; orchestrates multi-agent workflows.","https:\u002F\u002Fgithub.com\u002Fmfrancime\u002Ftrident-archive\u002Fblob\u002Fmain\u002Frepos\u002Ftrident_orcaclaw_sakana_personal_ai_assistant\u002Fextensions\u002Fopen-prose\u002Fskills\u002Fprose\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fmfrancime\u002Ftrident-archive\u002Fblob\u002Fmain\u002Frepos\u002Ftrident_orcaclaw_sakana_personal_ai_assistant\u002Fextensions\u002Fopen-prose\u002Fskills\u002Fprose\u002FSKILL.md",[],[],{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":109,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":19},"01M78VFJ4N00J9RMF7X5AVZY7C","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fmfrancime\u002Ftrident-archive\u002Fblob\u002Fmain\u002Frepos\u002Ftrident_kilo-marketplace_skills_mcp_catalog\u002Fskills\u002Fwebapp-testing\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fmfrancime\u002Ftrident-archive\u002Fblob\u002Fmain\u002Frepos\u002Ftrident_kilo-marketplace_skills_mcp_catalog\u002Fskills\u002Fwebapp-testing\u002FSKILL.md",[],[],{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":119,"publishedAt":19},"01M78VF9C8TQ37WGS6WBT77BVW","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React\u002FNext.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002Fmfrancime\u002Ftrident-archive\u002Fblob\u002Fmain\u002Frepos\u002Ftrident_kilo-marketplace_skills_mcp_catalog\u002Fskills\u002Fvercel-react-best-practices\u002FSKILL.md",[],[],{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":126,"publishedAt":19},"01M78VF5VFD2CA77AM9AVBXR7C","vercel-deploy","Deploy applications and websites to Vercel. Use this skill when the user requests deployment actions such as \"Deploy my app\", \"Deploy this to production\", \"Create a preview deployment\", \"Deploy and give me the link\", or \"Push this live\". No authentication required - returns preview URL and claimable deployment link.","https:\u002F\u002Fgithub.com\u002Fmfrancime\u002Ftrident-archive\u002Fblob\u002Fmain\u002Frepos\u002Ftrident_kilo-marketplace_skills_mcp_catalog\u002Fskills\u002Fvercel-deploy\u002FSKILL.md",[],[],{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":133,"publishedAt":19},"01M78VF0JF37J4KCM4Y30Z9E55","vercel-composition-patterns","React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.","https:\u002F\u002Fgithub.com\u002Fmfrancime\u002Ftrident-archive\u002Fblob\u002Fmain\u002Frepos\u002Ftrident_kilo-marketplace_skills_mcp_catalog\u002Fskills\u002Fvercel-composition-patterns\u002FSKILL.md",[],[]]