[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M796CKCWFBC9A5VCTFAZPYZZ":3,"skill-same:01M796CKCWFBC9A5VCTFAZPYZZ":63},{"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},"01M796CKCWFBC9A5VCTFAZPYZZ","graph-data",null,"Construct, batch, split, mask, validate, and troubleshoot TorchDrug graph, molecule, protein, and dataset data objects.","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\u002Ftorchdrug\u002Fsub-skills\u002Fgraph-data\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: graph-data\ndescription: \"Construct, batch, split, mask, validate, and troubleshoot\n  TorchDrug graph, molecule, protein, and dataset data objects.\"\ndisable-model-invocation: true\nmetadata:\n  disco-role: operating\nlicense: Apache 2.0\n---\n\n# TorchDrug Graph Data\n\nUse this sub-skill when the task is about TorchDrug data objects rather than model layers, training loops, or domain-specific task recipes.\n\n## Read First\n\n- For constructors, attribute contexts, batching, masking, split helpers, and dataset loading patterns, read [references\u002Fdata-objects.md](references\u002Fdata-objects.md).\n- For common failures around RDKit input, attribute lengths, device movement, dataset downloads, split lengths, and lazy loading, read [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md).\n- For a no-network sanity check that creates tiny in-memory graphs and molecules, run `python scripts\u002Fsmoke_graph_data.py` from this sub-skill directory. Add `--skip-rdkit` when RDKit or molecule parsing is unavailable.\n\n## Use This For\n\n- Creating `data.Graph`, `data.Molecule`, `data.Protein`, and their packed variants from in-memory edge lists, SMILES strings, or protein sequences.\n- Adding node, edge, graph, atom, bond, molecule, or residue attributes with the right context managers.\n- Batching samples with `Graph.pack`, `PackedMolecule.from_smiles`, `PackedProtein.from_sequence`, `data.graph_collate`, or `data.DataLoader`.\n- Applying `subgraph`, `node_mask`, `edge_mask`, `graph_mask`, `subbatch`, `compact`, `unpack`, and dataset split helpers.\n- Loading TorchDrug datasets or building small local `MoleculeDataset` \u002F `ProteinDataset` objects with transform and lazy-loading options.\n\n## Route Elsewhere\n\n- Use `layers-and-extensions` for graph neural network layers, graph construction modules, and extension compilation.\n- Use `training-engine` for `core.Engine`, optimizers, training\u002Fevaluation loops, checkpoint save\u002Fload, and `Configurable` workflows.\n- Use `molecular-workflows` for molecule property prediction, generation, reaction, or chemistry task recipes built on these data objects.\n- Use `protein-workflows` for protein prediction, sequence\u002Fstructure recipes, and protein-specific task pipelines.\n- Use `knowledge-graphs` for knowledge graph datasets, entity\u002Frelation indexing, and KG tasks.\n","# TorchDrug Graph Data\n\nUse this sub-skill when the task is about TorchDrug data objects rather than model layers, training loops, or domain-specific task recipes.\n\n## Read First\n\n- For constructors, attribute contexts, batching, masking, split helpers, and dataset loading patterns, read [references\u002Fdata-objects.md](references\u002Fdata-objects.md).\n- For common failures around RDKit input, attribute lengths, device movement, dataset downloads, split lengths, and lazy loading, read [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md).\n- For a no-network sanity check that creates tiny in-memory graphs and molecules, run `python scripts\u002Fsmoke_graph_data.py` from this sub-skill directory. Add `--skip-rdkit` when RDKit or molecule parsing is unavailable.\n\n## Use This For\n\n- Creating `data.Graph`, `data.Molecule`, `data.Protein`, and their packed variants from in-memory edge lists, SMILES strings, or protein sequences.\n- Adding node, edge, graph, atom, bond, molecule, or residue attributes with the right context managers.\n- Batching samples with `Graph.pack`, `PackedMolecule.from_smiles`, `PackedProtein.from_sequence`, `data.graph_collate`, or `data.DataLoader`.\n- Applying `subgraph`, `node_mask`, `edge_mask`, `graph_mask`, `subbatch`, `compact`, `unpack`, and dataset split helpers.\n- Loading TorchDrug datasets or building small local `MoleculeDataset` \u002F `ProteinDataset` objects with transform and lazy-loading options.\n\n## Route Elsewhere\n\n- Use `layers-and-extensions` for graph neural network layers, graph construction modules, and extension compilation.\n- Use `training-engine` for `core.Engine`, optimizers, training\u002Fevaluation loops, checkpoint save\u002Fload, and `Configurable` workflows.\n- Use `molecular-workflows` for molecule property prediction, generation, reaction, or chemistry task recipes built on these data objects.\n- Use `protein-workflows` for protein prediction, sequence\u002Fstructure recipes, and protein-specific task pipelines.\n- Use `knowledge-graphs` for knowledge graph datasets, entity\u002Frelation indexing, and KG tasks.\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\u002Ftorchdrug\u002Fsub-skills\u002Fgraph-data",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 graph-data",{"tool":40,"command":41},"bunx","bunx skills add VectorSpaceLab\u002FAREX-Skill --skill graph-data",{"tool":43,"command":44},"pnpm","pnpm dlx skills add VectorSpaceLab\u002FAREX-Skill --skill graph-data","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Ftorchdrug\u002Fsub-skills\u002Fgraph-data\u002FSKILL.md",[47,53,57],{"path":48,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":51,"sourceHtmlUrl":52},"references\u002Fdata-objects.md","REFERENCE","参考文档",12603,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Ftorchdrug\u002Fsub-skills\u002Fgraph-data\u002Freferences\u002Fdata-objects.md",{"path":54,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":55,"sourceHtmlUrl":56},"references\u002Ftroubleshooting.md",7901,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Ftorchdrug\u002Fsub-skills\u002Fgraph-data\u002Freferences\u002Ftroubleshooting.md",{"path":58,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":61,"sourceHtmlUrl":62},"scripts\u002Fsmoke_graph_data.py","SCRIPT","脚本",3257,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Ftorchdrug\u002Fsub-skills\u002Fgraph-data\u002Fscripts\u002Fsmoke_graph_data.py",[64,71,78,85,92,99],{"skillNo":65,"name":66,"nameZh":6,"description":67,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":68,"categories":69,"keywords":70,"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":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":75,"categories":76,"keywords":77,"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":79,"name":80,"nameZh":6,"description":81,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":84,"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":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":91,"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":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"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":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"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",[],[]]