[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M795ATR2WQT437TCT8EVCFS3":3,"skill-same:01M795ATR2WQT437TCT8EVCFS3":70},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":18,"linkOnly":19,"markdownContent":20,"markdownBody":21,"frontMatter":22,"refName":26,"commitSha":27,"skillDirectory":28,"forksCount":29,"repositoryHtmlUrl":30,"repositoryHomepageUrl":6,"ownerHtmlUrl":31,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":32,"install":33,"resources":45},"01M795ATR2WQT437TCT8EVCFS3","pipeline-components",null,"Assemble and debug spaCy pipeline components, factories, registry wiring, and pipe analysis.","VectorSpaceLab\u002FAREX-Skill","VectorSpaceLab","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F180637102?v=4","01M7538C5WPSJGWF7KHR4EFZ9F","01M7538C6R2GFP53NPXTCZNBRT",351,"MIT","https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fspacy\u002Fsub-skills\u002Fpipeline-components\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: pipeline-components\ndescription: \"Assemble and debug spaCy pipeline components, factories, registry\n  wiring, and pipe analysis.\"\ndisable-model-invocation: true\nmetadata:\n  disco-role: operating\nlicense: MIT\n---\n\n# pipeline-components\n\nValidated against spaCy 3.8.15 on the installed CPU environment. Optional CUDA, Apple, transformer, and language-extra behavior was not verified here.\n\nUse this sub-skill when the task is about `Language.component`, `Language.factory`, `nlp.add_pipe`, component order, registry lookup, source-vs-factory config wiring, or `nlp.analyze_pipes`.\n\n## Read first\n\n- [references\u002Fcomponent-factories-and-registry.md](references\u002Fcomponent-factories-and-registry.md) for verified `Language.factory` \u002F `Language.component` signatures and component metadata.\n- [references\u002Fpipeline-assembly-and-analysis.md](references\u002Fpipeline-assembly-and-analysis.md) for pipe ordering, enable\u002Fdisable\u002Fselect behavior, and `analyze_pipes` usage.\n- [references\u002Fbuilt-in-components.md](references\u002Fbuilt-in-components.md) for the built-in component catalog and what each one roughly does.\n- [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md) when `add_pipe` raises, config validation fails, or a source component gets mistaken for a factory.\n- [scripts\u002Finspect_factories.py](scripts\u002Finspect_factories.py) for a safe registry and analysis smoke check.\n\n## What this sub-skill covers\n\n- Registering stateless components with `Language.component`.\n- Registering stateful factories with `Language.factory` and default configs.\n- Adding components by name, reordering them, and loading components from source pipelines.\n- Reading and interpreting built-in factory metadata.\n- Using `nlp.analyze_pipes()` to understand assigns\u002Frequires\u002Fretokenizes relationships.\n\n## What to route elsewhere\n\n- Tokenization, `Doc`\u002F`Token`\u002F`Span`, matchers\u002Frulers, serialization, and displaCy: `documents-and-visualization`.\n- `init config`, `debug config`, `train`, `evaluate`, `convert`, `package`, `validate`: `training-and-cli`.\n- Install\u002Fimport or backend concerns: `install-and-inspect`.\n- `spacy project` orchestration: `project-workflows`.\n\n## Fast use pattern\n\n1. Check the exact component signature in the reference.\n2. Decide whether the user needs a stateless component or a stateful factory.\n3. Use the bundled registry inspection script to confirm the named component exists.\n4. Read `pipeline-assembly-and-analysis.md` when order, disable\u002Fenable, or source-vs-factory confusion appears.\n\n## Evidence basis\n\nThis sub-skill is grounded in the installed `Language` signatures, built-in factory source, pipeline tests, and public docs for component registration and pipeline processing.\n","# pipeline-components\n\nValidated against spaCy 3.8.15 on the installed CPU environment. Optional CUDA, Apple, transformer, and language-extra behavior was not verified here.\n\nUse this sub-skill when the task is about `Language.component`, `Language.factory`, `nlp.add_pipe`, component order, registry lookup, source-vs-factory config wiring, or `nlp.analyze_pipes`.\n\n## Read first\n\n- [references\u002Fcomponent-factories-and-registry.md](references\u002Fcomponent-factories-and-registry.md) for verified `Language.factory` \u002F `Language.component` signatures and component metadata.\n- [references\u002Fpipeline-assembly-and-analysis.md](references\u002Fpipeline-assembly-and-analysis.md) for pipe ordering, enable\u002Fdisable\u002Fselect behavior, and `analyze_pipes` usage.\n- [references\u002Fbuilt-in-components.md](references\u002Fbuilt-in-components.md) for the built-in component catalog and what each one roughly does.\n- [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md) when `add_pipe` raises, config validation fails, or a source component gets mistaken for a factory.\n- [scripts\u002Finspect_factories.py](scripts\u002Finspect_factories.py) for a safe registry and analysis smoke check.\n\n## What this sub-skill covers\n\n- Registering stateless components with `Language.component`.\n- Registering stateful factories with `Language.factory` and default configs.\n- Adding components by name, reordering them, and loading components from source pipelines.\n- Reading and interpreting built-in factory metadata.\n- Using `nlp.analyze_pipes()` to understand assigns\u002Frequires\u002Fretokenizes relationships.\n\n## What to route elsewhere\n\n- Tokenization, `Doc`\u002F`Token`\u002F`Span`, matchers\u002Frulers, serialization, and displaCy: `documents-and-visualization`.\n- `init config`, `debug config`, `train`, `evaluate`, `convert`, `package`, `validate`: `training-and-cli`.\n- Install\u002Fimport or backend concerns: `install-and-inspect`.\n- `spacy project` orchestration: `project-workflows`.\n\n## Fast use pattern\n\n1. Check the exact component signature in the reference.\n2. Decide whether the user needs a stateless component or a stateful factory.\n3. Use the bundled registry inspection script to confirm the named component exists.\n4. Read `pipeline-assembly-and-analysis.md` when order, disable\u002Fenable, or source-vs-factory confusion appears.\n\n## Evidence basis\n\nThis sub-skill is grounded in the installed `Language` signatures, built-in factory source, pipeline tests, and public docs for component registration and pipeline processing.\n",{"name":5,"license":14,"metadata":23,"description":7,"disable-model-invocation":25},{"disco-role":24},"operating",true,"main","ac3fe1afa80fb9a09775ecfb2b6cc3ba850a2db6","skills\u002Frepositories\u002Frepo-skills\u002Fspacy\u002Fsub-skills\u002Fpipeline-components",27,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill","https:\u002F\u002Fgithub.com\u002FVectorSpaceLab","2026-09-03 10:25:21",{"source":8,"skillName":5,"commands":34,"prompt":44,"zipAvailable":25},[35,38,41],{"tool":36,"command":37},"npx","npx skills add VectorSpaceLab\u002FAREX-Skill --skill pipeline-components",{"tool":39,"command":40},"bunx","bunx skills add VectorSpaceLab\u002FAREX-Skill --skill pipeline-components",{"tool":42,"command":43},"pnpm","pnpm dlx skills add VectorSpaceLab\u002FAREX-Skill --skill pipeline-components","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fspacy\u002Fsub-skills\u002Fpipeline-components\u002FSKILL.md",[46,52,56,60,64],{"path":47,"resourceType":48,"resourceTypeDesc":49,"sizeBytes":50,"sourceHtmlUrl":51},"references\u002Fbuilt-in-components.md","REFERENCE","参考文档",4487,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fspacy\u002Fsub-skills\u002Fpipeline-components\u002Freferences\u002Fbuilt-in-components.md",{"path":53,"resourceType":48,"resourceTypeDesc":49,"sizeBytes":54,"sourceHtmlUrl":55},"references\u002Fcomponent-factories-and-registry.md",4869,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fspacy\u002Fsub-skills\u002Fpipeline-components\u002Freferences\u002Fcomponent-factories-and-registry.md",{"path":57,"resourceType":48,"resourceTypeDesc":49,"sizeBytes":58,"sourceHtmlUrl":59},"references\u002Fpipeline-assembly-and-analysis.md",6707,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fspacy\u002Fsub-skills\u002Fpipeline-components\u002Freferences\u002Fpipeline-assembly-and-analysis.md",{"path":61,"resourceType":48,"resourceTypeDesc":49,"sizeBytes":62,"sourceHtmlUrl":63},"references\u002Ftroubleshooting.md",4201,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fspacy\u002Fsub-skills\u002Fpipeline-components\u002Freferences\u002Ftroubleshooting.md",{"path":65,"resourceType":66,"resourceTypeDesc":67,"sizeBytes":68,"sourceHtmlUrl":69},"scripts\u002Finspect_factories.py","SCRIPT","脚本",3523,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fspacy\u002Fsub-skills\u002Fpipeline-components\u002Fscripts\u002Finspect_factories.py",[71,79,86,93,100,107],{"skillNo":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":75,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":78,"publishedAt":18},"01M799NYSH1R3RVAHSM2D88Y84","passnet-triton-opt","Make a single PassNet Triton kernel fast AND numerically faithful: performance model (when a replacement can win at all), block\u002Fgrid\u002Fwarp tuning, autotune policy, launch overhead, and per-op numeric recipes to pass the dtype baseline tolerances. Use when a pass matches and is correct but speedup below expected, or when correctness fails by small numeric margins.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Ftask-oriented\u002FPassNet\u002Fskills\u002Fpassnet-triton-opt\u002FSKILL.md",[],[],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":75,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":85,"publishedAt":18},"01M799NS5APEC0N1FZ7XZVW6BN","passnet-solve","END-TO-END playbook for solving one PassNet sample: analyze the computation graphs, decide the optimization strategy (what to fuse, what to replace, what to leave alone), drive the iteration loop, and maximize the sample score. This is the ENTRY skill — invoke it first for any PassNet optimization task; it tells you when to use passnet-pattern-fusion, passnet-triton-opt and passnet-feedback.","https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Ftask-oriented\u002FPassNet\u002Fskills\u002Fpassnet-solve\u002FSKILL.md",[],[],{"skillNo":87,"name":88,"nameZh":6,"description":89,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":75,"licenseFileUrl":6,"sourceHtmlUrl":90,"categories":91,"keywords":92,"publishedAt":18},"01M799NNY9BKSM21V5R7QVTBJX","passnet-skill","PassNet GPU kernel optimization via compiler passes. Design and implement Triton-based optimization passes, create pass files under .\u002Fpass_dir\u002F, self-evaluate with pass_evaluator, and iterate to maximize GPU speedup.","https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Ftask-oriented\u002FPassNet\u002Fskills\u002Fpassnet-skill\u002FSKILL.md",[],[],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":75,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":99,"publishedAt":18},"01M799NHH6NP7Z1RK77KGXAN6S","passnet-pattern-fusion","Author PassNet pass files: write patterns that actually MATCH the FX graph, pick fusion regions (which ops to absorb into one Triton kernel), and structure multi-pass submissions with the mandatory shared-dispatch architecture. Use when creating or fixing pattern()\u002Freplacement_args()\u002Freplacement_func() files, when a pass \"failed to match\", or when deciding how to fuse multiple kernels\u002Fops.","https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Ftask-oriented\u002FPassNet\u002Fskills\u002Fpassnet-pattern-fusion\u002FSKILL.md",[],[],{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":75,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":106,"publishedAt":18},"01M799ND2J4WNC2PGAZ0A06NDF","passnet-orchestrate","Round\u002Fbatch planning and decision support for PassNet graph optimization. Use for multi-sample triage, worker allocation, eval-budget policy, and post-eval keep\u002Frevert decisions. For solving one concrete sample end to end, use passnet-solve as the entry skill; consult this skill only when you need broader planning or a specific decision gate.","https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Ftask-oriented\u002FPassNet\u002Fskills\u002Fpassnet-orchestrate\u002FSKILL.md",[],[],{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":75,"licenseFileUrl":6,"sourceHtmlUrl":111,"categories":112,"keywords":113,"publishedAt":18},"01M799N8SBTMY02ZDG2AZF5ZWE","passnet-feedback","Fast, deterministic feedback for PassNet work: pre-flight pattern\u002Fmatch verification WITHOUT burning a GPU evaluation, per-node bottleneck analysis of a sample's graphs, and eval-log parsing into per-variant status + estimated score + failure classification. Use BEFORE every GPU evaluation (check_pattern), at the START of a sample (analyze_graph), and AFTER every evaluation (parse_eval_log).","https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Ftask-oriented\u002FPassNet\u002Fskills\u002Fpassnet-feedback\u002FSKILL.md",[],[]]