[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QKBTF6YZCCC8NGDKJZC8VF":3,"skill-same:01M6QKBTF6YZCCC8NGDKJZC8VF":99},{"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":58,"commitSha":59,"skillDirectory":60,"forksCount":14,"repositoryHtmlUrl":61,"repositoryHomepageUrl":62,"ownerHtmlUrl":63,"ownerBlogUrl":64,"ownerDescription":65,"updatedAt":66,"install":67,"resources":80},"01M6QKBTF6YZCCC8NGDKJZC8VF","axolotl",null,"Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA\u002FQLoRA, DPO\u002FKTO\u002FORPO\u002FGRPO, multimodal support","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Forch-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","for","orchestra","with","Accelerate","ai-research","expert","tuning","Transformers","GRPO","llms","KTO","DPO","QLoRA","LoRA","Fine-Tuning","DeepSpeed","multimodal","support","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]\ncategory: \"ai-research\"\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. The skill will be rebuilt with the latest information\n\n\n",{"name":5,"tags":44,"author":49,"license":15,"version":50,"category":25,"description":7,"dependencies":51},[35,19,45,34,33,32,31,20,29,46,47,36,48],"LLM","YAML","HuggingFace","Multimodal","Orchestra Research","1.0.0",[5,52,53,54,55,56,57],"torch","transformers","datasets","peft","accelerate","deepspeed","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Forch-axolotl","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6","https:\u002F\u002Fdisha6.6.thenitishkr.in\u002F","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh","www.thenitishkr","thenitishkr | AI • Cyber-security • Research\r\n","2026-11-08 03:33:25",{"source":8,"skillName":5,"commands":68,"prompt":78,"zipAvailable":79},[69,72,75],{"tool":70,"command":71},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill axolotl",{"tool":73,"command":74},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill axolotl",{"tool":76,"command":77},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill axolotl","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Forch-axolotl\u002FSKILL.md",true,[81,87,91,95],{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fapi.md","REFERENCE","参考文档",121142,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Forch-axolotl\u002Freferences\u002Fapi.md",{"path":88,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Fdataset-formats.md",46043,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Forch-axolotl\u002Freferences\u002Fdataset-formats.md",{"path":92,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Findex.md",199,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Forch-axolotl\u002Freferences\u002Findex.md",{"path":96,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Fother.md",140407,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Forch-axolotl\u002Freferences\u002Fother.md",[100,131,156,182,207,231],{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":111,"publishedAt":39},"01M6QRD07D4TGNDK6VJ4EHF8BV","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[106],{"categoryNo":107,"categoryName":108,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,21,128,129,130],"youtube full","youtube","full","transcript","search","monitoring","data","Video","Audio","list","files","do","get","age","trigger","URL","browsing","request","youtube transcripts",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":137,"publishedAt":39},"01M6QRCJ3112VYPA97YG7KGMC2","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[138,139,140,141,116,142,143,144,145,146,147,148,149,150,151,152,153,154,155,21],"twitter","x twitter growth","growth","research","content","engineering","plan","web search","competitors","analyze","engine","write","strategy","use","platform","grow","posting","competitive",{"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":165,"publishedAt":39},"01M6QRBN3EHQNJY42RERP7A149","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[162],{"categoryNo":163,"categoryName":164,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000023","Skill与智能体开发",[166,149,167,168,169,170,171,172,151,173,174,58,175,176,177,178,23,179,180,181],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","tree","progressive disclosure","author",{"skillNo":183,"name":184,"nameZh":6,"description":185,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":186,"categories":187,"keywords":188,"publishedAt":39},"01M6QRB6G8P33PNGAF9Y69EJ9T","wrap-up","Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says \"wrap up\", \"close this out\", \"what do I own now\", \"give me the summary\", \"recap the agent\", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained .\u002Fmy-agent\u002Fagent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[189,190,191,192,193,194,195,196,197,151,198,123,199,200,201,202,203,204,205,206],"wrap up","up","wrap","gemini","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","directions","phase",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":211,"categories":212,"keywords":214,"publishedAt":39},"01M6QRA5AYC1RBFZ39GTWGS3N8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[213],{"categoryNo":163,"categoryName":164,"taxonomyType":109,"taxonomyTypeDesc":110},[215,216,217,218,219,168,194,220,221,222,223,224,149,225,122,226,227,228,229,230],"workflow","workflow builder","builder","design","workflows","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":232,"name":233,"nameZh":6,"description":234,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":235,"categories":236,"keywords":237,"publishedAt":39},"01M6QR7QMCZESKQA0ERW1E657M","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[238,239,240,241,242,243,244,245,246,223,247,248,249,250,251,252,124,253,254,206],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]