[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M791KB7RB8MPCEW8D47QP7DM":3,"skill-same:01M791KB7RB8MPCEW8D47QP7DM":66},{"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},"01M791KB7RB8MPCEW8D47QP7DM","graph-property-prediction",null,"Routes OGB graph-property workflows for ogbg datasets, molecular graphs, and code2 conversion.","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\u002Fogb\u002Fsub-skills\u002Fgraph-property-prediction\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: graph-property-prediction\ndescription: \"Routes OGB graph-property workflows for ogbg datasets, molecular\n  graphs, and code2 conversion.\"\nmetadata:\n  disco-role: operating\ndisable-model-invocation: true\nlicense: MIT\n---\n\n# Graph Property Prediction\n\nUse this subskill for the graph-property family: `ogbg-*` datasets, molecular\nclassification\u002Fregression, `ogbg-code2`, `ogbg-ppa`, and SMILES-to-graph\nconversion.\n\n## Use this subskill when\n\n- The task names `GraphPropPredDataset`, `PygGraphPropPredDataset`,\n  `DglGraphPropPredDataset`, or `ogbg-*`.\n- The task asks for graph-level evaluation metrics such as ROC-AUC, average\n  precision, RMSE, accuracy, or code2 F1.\n- The task mentions `smiles2graph`, `py2graph`, molecular datasets, or code to\n  graph conversion.\n- The task needs the PyG or DGL graph-property wrappers and the corresponding\n  backend packages are installed.\n\n## First decisions\n\n1. Read [`references\u002Fworkflows.md`](references\u002Fworkflows.md) for the graph\n   property task flow and dataset-specific gotchas.\n2. Read [`references\u002Fapi-reference.md`](references\u002Fapi-reference.md) for the\n   public class names, input\u002Foutput shapes, and metric names.\n3. Read [`references\u002Ftroubleshooting.md`](references\u002Ftroubleshooting.md) when a\n   loader, evaluator, or conversion helper fails.\n4. If you need a tiny sanity check for code2-style AST conversion, run\n   [`scripts\u002Fpy2graph-smoke.py`](scripts\u002Fpy2graph-smoke.py).\n\n## Main workflow\n\n- Pick the dataset name from the exact `ogbg-*` catalog.\n- Decide whether you need the library-agnostic loader or the PyG\u002FDGL wrapper.\n- Load the dataset and inspect `get_idx_split()`.\n- Read the evaluator's expected input format before computing metrics.\n- For molecular datasets, convert external molecules with `smiles2graph` rather\n  than hand-assembling graph dictionaries.\n- For `ogbg-code2`, treat the evaluator input as token sequences, not numeric\n  labels.\n\n## Common routing choices\n\n- `ogbg-mol*` -> graph classification\u002Fregression with molecule graphs.\n- `ogbg-ppa` -> graph classification with accuracy.\n- `ogbg-code2` -> code-to-graph conversion and F1-style sequence evaluation.\n- `smiles2graph` -> molecule-to-graph helper backed by rdkit.\n\n## Optional backend note\n\nPyG and DGL wrappers are optional. If the current environment does not have the\nmatching backend packages, route the task to the library-agnostic loader or ask\nfor the optional dependency rather than assuming the graph-property skill is\nbroken.\n\n## What not to do here\n\n- Do not route node\u002Flink\u002FLSC dataset questions into this subskill just because\n  they are graph-shaped.\n- Do not tell the user to run the original repository's training scripts as the\n  runtime answer.\n- Do not depend on the original checkout for the final skill content.\n\n## Related references\n\n- [`..\u002F..\u002Freferences\u002Fapi-overview.md`](..\u002F..\u002Freferences\u002Fapi-overview.md)\n- [`..\u002F..\u002Freferences\u002Fdataset-catalog.md`](..\u002F..\u002Freferences\u002Fdataset-catalog.md)\n- [`..\u002F..\u002Freferences\u002Ftroubleshooting.md`](..\u002F..\u002Freferences\u002Ftroubleshooting.md)\n- [`..\u002F..\u002Fscripts\u002Fsmiles2graph-smoke.py`](..\u002F..\u002Fscripts\u002Fsmiles2graph-smoke.py)\n","# Graph Property Prediction\n\nUse this subskill for the graph-property family: `ogbg-*` datasets, molecular\nclassification\u002Fregression, `ogbg-code2`, `ogbg-ppa`, and SMILES-to-graph\nconversion.\n\n## Use this subskill when\n\n- The task names `GraphPropPredDataset`, `PygGraphPropPredDataset`,\n  `DglGraphPropPredDataset`, or `ogbg-*`.\n- The task asks for graph-level evaluation metrics such as ROC-AUC, average\n  precision, RMSE, accuracy, or code2 F1.\n- The task mentions `smiles2graph`, `py2graph`, molecular datasets, or code to\n  graph conversion.\n- The task needs the PyG or DGL graph-property wrappers and the corresponding\n  backend packages are installed.\n\n## First decisions\n\n1. Read [`references\u002Fworkflows.md`](references\u002Fworkflows.md) for the graph\n   property task flow and dataset-specific gotchas.\n2. Read [`references\u002Fapi-reference.md`](references\u002Fapi-reference.md) for the\n   public class names, input\u002Foutput shapes, and metric names.\n3. Read [`references\u002Ftroubleshooting.md`](references\u002Ftroubleshooting.md) when a\n   loader, evaluator, or conversion helper fails.\n4. If you need a tiny sanity check for code2-style AST conversion, run\n   [`scripts\u002Fpy2graph-smoke.py`](scripts\u002Fpy2graph-smoke.py).\n\n## Main workflow\n\n- Pick the dataset name from the exact `ogbg-*` catalog.\n- Decide whether you need the library-agnostic loader or the PyG\u002FDGL wrapper.\n- Load the dataset and inspect `get_idx_split()`.\n- Read the evaluator's expected input format before computing metrics.\n- For molecular datasets, convert external molecules with `smiles2graph` rather\n  than hand-assembling graph dictionaries.\n- For `ogbg-code2`, treat the evaluator input as token sequences, not numeric\n  labels.\n\n## Common routing choices\n\n- `ogbg-mol*` -> graph classification\u002Fregression with molecule graphs.\n- `ogbg-ppa` -> graph classification with accuracy.\n- `ogbg-code2` -> code-to-graph conversion and F1-style sequence evaluation.\n- `smiles2graph` -> molecule-to-graph helper backed by rdkit.\n\n## Optional backend note\n\nPyG and DGL wrappers are optional. If the current environment does not have the\nmatching backend packages, route the task to the library-agnostic loader or ask\nfor the optional dependency rather than assuming the graph-property skill is\nbroken.\n\n## What not to do here\n\n- Do not route node\u002Flink\u002FLSC dataset questions into this subskill just because\n  they are graph-shaped.\n- Do not tell the user to run the original repository's training scripts as the\n  runtime answer.\n- Do not depend on the original checkout for the final skill content.\n\n## Related references\n\n- [`..\u002F..\u002Freferences\u002Fapi-overview.md`](..\u002F..\u002Freferences\u002Fapi-overview.md)\n- [`..\u002F..\u002Freferences\u002Fdataset-catalog.md`](..\u002F..\u002Freferences\u002Fdataset-catalog.md)\n- [`..\u002F..\u002Freferences\u002Ftroubleshooting.md`](..\u002F..\u002Freferences\u002Ftroubleshooting.md)\n- [`..\u002F..\u002Fscripts\u002Fsmiles2graph-smoke.py`](..\u002F..\u002Fscripts\u002Fsmiles2graph-smoke.py)\n",{"name":5,"license":14,"metadata":23,"description":7,"disable-model-invocation":25},{"disco-role":24},"operating",true,"main","ac3fe1afa80fb9a09775ecfb2b6cc3ba850a2db6","skills\u002Frepositories\u002Frepo-skills\u002Fogb\u002Fsub-skills\u002Fgraph-property-prediction",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 graph-property-prediction",{"tool":39,"command":40},"bunx","bunx skills add VectorSpaceLab\u002FAREX-Skill --skill graph-property-prediction",{"tool":42,"command":43},"pnpm","pnpm dlx skills add VectorSpaceLab\u002FAREX-Skill --skill graph-property-prediction","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fogb\u002Fsub-skills\u002Fgraph-property-prediction\u002FSKILL.md",[46,52,56,60],{"path":47,"resourceType":48,"resourceTypeDesc":49,"sizeBytes":50,"sourceHtmlUrl":51},"references\u002Fapi-reference.md","REFERENCE","参考文档",1333,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fogb\u002Fsub-skills\u002Fgraph-property-prediction\u002Freferences\u002Fapi-reference.md",{"path":53,"resourceType":48,"resourceTypeDesc":49,"sizeBytes":54,"sourceHtmlUrl":55},"references\u002Ftroubleshooting.md",1205,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fogb\u002Fsub-skills\u002Fgraph-property-prediction\u002Freferences\u002Ftroubleshooting.md",{"path":57,"resourceType":48,"resourceTypeDesc":49,"sizeBytes":58,"sourceHtmlUrl":59},"references\u002Fworkflows.md",1744,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fogb\u002Fsub-skills\u002Fgraph-property-prediction\u002Freferences\u002Fworkflows.md",{"path":61,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":64,"sourceHtmlUrl":65},"scripts\u002Fpy2graph-smoke.py","SCRIPT","脚本",823,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fogb\u002Fsub-skills\u002Fgraph-property-prediction\u002Fscripts\u002Fpy2graph-smoke.py",[67,75,82,89,96,103],{"skillNo":68,"name":69,"nameZh":6,"description":70,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":71,"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.","Apache-2.0","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":71,"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":71,"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":71,"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":71,"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":71,"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",[],[]]