[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M78VX9F098MASV2A2MS1CEES":3,"skill-same:01M78VX9F098MASV2A2MS1CEES":67},{"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":27,"commitSha":28,"skillDirectory":29,"forksCount":30,"repositoryHtmlUrl":31,"repositoryHomepageUrl":6,"ownerHtmlUrl":32,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":33,"install":34,"resources":46},"01M78VX9F098MASV2A2MS1CEES","text-models",null,"DeepPavlov workflows for text classification, tagging, extraction, spelling, syntax, relation, multitask, regression, and embeddings.","VectorSpaceLab\u002FAREX-Skill","VectorSpaceLab","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F180637102?v=4","01M7538C5WPSJGWF7KHR4EFZ9F","01M7538C6R2GFP53NPXTCZNBRT",351,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fdeeppavlov\u002Fsub-skills\u002Ftext-models\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: text-models\ndescription: \"DeepPavlov workflows for text classification, tagging, extraction,\n  spelling, syntax, relation, multitask, regression, and embeddings.\"\nmetadata:\n  disco-role: operating\ndisable-model-invocation: true\nlicense: Apache 2.0\n---\n\n# Text Models\n\nUse this sub-skill for DeepPavlov model families that operate on texts, tokens, spans, relations, labels, scores, or embeddings.\n\n## Route Here\n\n- Text classification, sentence-pair classification, multiple-choice classification, few-shot classification, benchmark classification, and scalar text scoring.\n- NER, sentence segmentation, morpho-syntax parsing, and other token-level tagging workflows.\n- Entity detection, entity linking, and relation extraction workflows.\n- Spelling correction workflows.\n- Multitask workflows that share one backbone across several text heads.\n- BERT-style and sentence-level embedding workflows, including the standalone embedding configs.\n\n## Reroute Elsewhere\n\n- Core config syntax, nested configs, registry behavior, custom components, and train\u002Fevaluate plumbing -> [pipelines](..\u002Fpipelines\u002FSKILL.md).\n- Document retrieval, ranking, FAQ, SQuAD, ODQA, and KBQA workflows -> [retrieval-qa](..\u002Fretrieval-qa\u002FSKILL.md).\n- REST and socket deployment, ports, payloads, probes, and response framing -> [serving](..\u002Fserving\u002FSKILL.md).\n- Cross-cutting install\u002Fimport\u002Fbackend\u002Fcache issues -> the [root troubleshooting reference](..\u002F..\u002Freferences\u002Ftroubleshooting.md) once it is present in the generated skill tree.\n\n## Fast Workflow\n\n1. Run `scripts\u002Flist_config_categories.py` to confirm the live config inventory in the installed package.\n2. Match the task shape to `references\u002Fmodel-catalog.md`.\n3. Use `references\u002Fdata-formats.md` for `chainer.in` \u002F `chainer.out` shapes and safe batching rules.\n4. If the task needs config surgery, reader changes, iterator changes, or custom components, switch to [pipelines](..\u002Fpipelines\u002FSKILL.md).\n5. If the task is about exposing an already-selected model over HTTP or sockets, switch to [serving](..\u002Fserving\u002FSKILL.md).\n\n## References\n\n- [Model catalog](references\u002Fmodel-catalog.md)\n- [Data formats](references\u002Fdata-formats.md)\n- [Troubleshooting](references\u002Ftroubleshooting.md)\n- [Config inventory script](scripts\u002Flist_config_categories.py)\n","# Text Models\n\nUse this sub-skill for DeepPavlov model families that operate on texts, tokens, spans, relations, labels, scores, or embeddings.\n\n## Route Here\n\n- Text classification, sentence-pair classification, multiple-choice classification, few-shot classification, benchmark classification, and scalar text scoring.\n- NER, sentence segmentation, morpho-syntax parsing, and other token-level tagging workflows.\n- Entity detection, entity linking, and relation extraction workflows.\n- Spelling correction workflows.\n- Multitask workflows that share one backbone across several text heads.\n- BERT-style and sentence-level embedding workflows, including the standalone embedding configs.\n\n## Reroute Elsewhere\n\n- Core config syntax, nested configs, registry behavior, custom components, and train\u002Fevaluate plumbing -> [pipelines](..\u002Fpipelines\u002FSKILL.md).\n- Document retrieval, ranking, FAQ, SQuAD, ODQA, and KBQA workflows -> [retrieval-qa](..\u002Fretrieval-qa\u002FSKILL.md).\n- REST and socket deployment, ports, payloads, probes, and response framing -> [serving](..\u002Fserving\u002FSKILL.md).\n- Cross-cutting install\u002Fimport\u002Fbackend\u002Fcache issues -> the [root troubleshooting reference](..\u002F..\u002Freferences\u002Ftroubleshooting.md) once it is present in the generated skill tree.\n\n## Fast Workflow\n\n1. Run `scripts\u002Flist_config_categories.py` to confirm the live config inventory in the installed package.\n2. Match the task shape to `references\u002Fmodel-catalog.md`.\n3. Use `references\u002Fdata-formats.md` for `chainer.in` \u002F `chainer.out` shapes and safe batching rules.\n4. If the task needs config surgery, reader changes, iterator changes, or custom components, switch to [pipelines](..\u002Fpipelines\u002FSKILL.md).\n5. If the task is about exposing an already-selected model over HTTP or sockets, switch to [serving](..\u002Fserving\u002FSKILL.md).\n\n## References\n\n- [Model catalog](references\u002Fmodel-catalog.md)\n- [Data formats](references\u002Fdata-formats.md)\n- [Troubleshooting](references\u002Ftroubleshooting.md)\n- [Config inventory script](scripts\u002Flist_config_categories.py)\n",{"name":5,"license":23,"metadata":24,"description":7,"disable-model-invocation":26},"Apache 2.0",{"disco-role":25},"operating",true,"main","ac3fe1afa80fb9a09775ecfb2b6cc3ba850a2db6","skills\u002Frepositories\u002Frepo-skills\u002Fdeeppavlov\u002Fsub-skills\u002Ftext-models",27,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill","https:\u002F\u002Fgithub.com\u002FVectorSpaceLab","2026-09-03 10:25:21",{"source":8,"skillName":5,"commands":35,"prompt":45,"zipAvailable":26},[36,39,42],{"tool":37,"command":38},"npx","npx skills add VectorSpaceLab\u002FAREX-Skill --skill text-models",{"tool":40,"command":41},"bunx","bunx skills add VectorSpaceLab\u002FAREX-Skill --skill text-models",{"tool":43,"command":44},"pnpm","pnpm dlx skills add VectorSpaceLab\u002FAREX-Skill --skill text-models","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fdeeppavlov\u002Fsub-skills\u002Ftext-models\u002FSKILL.md",[47,53,57,61],{"path":48,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":51,"sourceHtmlUrl":52},"references\u002Fdata-formats.md","REFERENCE","参考文档",5193,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fdeeppavlov\u002Fsub-skills\u002Ftext-models\u002Freferences\u002Fdata-formats.md",{"path":54,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":55,"sourceHtmlUrl":56},"references\u002Fmodel-catalog.md",6584,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fdeeppavlov\u002Fsub-skills\u002Ftext-models\u002Freferences\u002Fmodel-catalog.md",{"path":58,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":59,"sourceHtmlUrl":60},"references\u002Ftroubleshooting.md",4100,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fdeeppavlov\u002Fsub-skills\u002Ftext-models\u002Freferences\u002Ftroubleshooting.md",{"path":62,"resourceType":63,"resourceTypeDesc":64,"sizeBytes":65,"sourceHtmlUrl":66},"scripts\u002Flist_config_categories.py","SCRIPT","脚本",2946,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fdeeppavlov\u002Fsub-skills\u002Ftext-models\u002Fscripts\u002Flist_config_categories.py",[68,75,82,89,96,103],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":74,"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.","https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Ftask-oriented\u002FPassNet\u002Fskills\u002Fpassnet-triton-opt\u002FSKILL.md",[],[],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":81,"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":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":88,"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":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":95,"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":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"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":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"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",[],[]]