[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M798PRDDA7T0NGFDPAC8FXZ5":3,"skill-same:01M798PRDDA7T0NGFDPAC8FXZ5":58},{"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":23,"commitSha":24,"skillDirectory":25,"forksCount":26,"repositoryHtmlUrl":27,"repositoryHomepageUrl":6,"ownerHtmlUrl":28,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":29,"install":30,"resources":43},"01M798PRDDA7T0NGFDPAC8FXZ5","pinn_residual_problem",null,"Build deterministic PINN residual problem fixtures with boundary and PDE residual targets for NTK recovery experiments.","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\u002Ftask-oriented\u002FPaperBench\u002Fskills\u002Fpinn\u002Fskill\u002Fntk_pinn_analysis\u002Fpinn_residual_problem\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: pinn_residual_problem\ndescription: Build deterministic PINN residual problem fixtures with boundary and PDE residual targets for NTK recovery experiments.\n---\n\n# PINN Residual Problem\n\nUse this skill when a recovery task needs an explicit physics-informed neural network problem item rather than a generic regression dataset. It is appropriate for small Poisson-style probes, source-boundary checks, and module tests that need separated boundary and residual loss contracts.\n\nDo not use this skill to claim a full paper benchmark. The default fixture is a declared synthetic proxy designed to exercise the paper mechanism.\n\n## Inputs\n\n- `frequency`: positive integer or float for `u(x)=sin(frequency*pi*x)`.\n- `residual_points`: interior collocation points in `[0, 1]`.\n- Optional boundary points, defaulting to `[0, 1]`.\n\n## Outputs\n\n- A JSON-compatible problem dictionary with `boundary_points`, `boundary_targets`, `residual_points`, `residual_targets`, and `operator`.\n- Residual targets follow the Poisson second-derivative convention `u_xx(x) = -(frequency*pi)^2 sin(frequency*pi*x)`.\n\n## Workflow\n\n1. Choose a deterministic set of boundary and residual points.\n2. Compute exact solution values and second-derivative forcing values.\n3. Validate that arrays are non-empty, finite, and aligned.\n4. Pass the problem dictionary to NTK diagnostics or proxy training.\n\n## Validation\n\nRun:\n\n```bash\npython tests\u002Ftest_poisson_problem.py\n```\n\nThe tests verify endpoint boundary values, residual sign, finite values, and metadata needed by downstream recovery.\n\n## Limitations\n\nThe fixture is intentionally small and synthetic. It preserves the paper's residual-plus-boundary loss mechanism but does not reproduce full-width neural network training or all numerical experiments.\n","# PINN Residual Problem\n\nUse this skill when a recovery task needs an explicit physics-informed neural network problem item rather than a generic regression dataset. It is appropriate for small Poisson-style probes, source-boundary checks, and module tests that need separated boundary and residual loss contracts.\n\nDo not use this skill to claim a full paper benchmark. The default fixture is a declared synthetic proxy designed to exercise the paper mechanism.\n\n## Inputs\n\n- `frequency`: positive integer or float for `u(x)=sin(frequency*pi*x)`.\n- `residual_points`: interior collocation points in `[0, 1]`.\n- Optional boundary points, defaulting to `[0, 1]`.\n\n## Outputs\n\n- A JSON-compatible problem dictionary with `boundary_points`, `boundary_targets`, `residual_points`, `residual_targets`, and `operator`.\n- Residual targets follow the Poisson second-derivative convention `u_xx(x) = -(frequency*pi)^2 sin(frequency*pi*x)`.\n\n## Workflow\n\n1. Choose a deterministic set of boundary and residual points.\n2. Compute exact solution values and second-derivative forcing values.\n3. Validate that arrays are non-empty, finite, and aligned.\n4. Pass the problem dictionary to NTK diagnostics or proxy training.\n\n## Validation\n\nRun:\n\n```bash\npython tests\u002Ftest_poisson_problem.py\n```\n\nThe tests verify endpoint boundary values, residual sign, finite values, and metadata needed by downstream recovery.\n\n## Limitations\n\nThe fixture is intentionally small and synthetic. It preserves the paper's residual-plus-boundary loss mechanism but does not reproduce full-width neural network training or all numerical experiments.\n",{"name":5,"description":7},"main","ac3fe1afa80fb9a09775ecfb2b6cc3ba850a2db6","skills\u002Ftask-oriented\u002FPaperBench\u002Fskills\u002Fpinn\u002Fskill\u002Fntk_pinn_analysis\u002Fpinn_residual_problem",27,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill","https:\u002F\u002Fgithub.com\u002FVectorSpaceLab","2026-09-03 10:25:21",{"source":8,"skillName":5,"commands":31,"prompt":41,"zipAvailable":42},[32,35,38],{"tool":33,"command":34},"npx","npx skills add VectorSpaceLab\u002FAREX-Skill --skill pinn_residual_problem",{"tool":36,"command":37},"bunx","bunx skills add VectorSpaceLab\u002FAREX-Skill --skill pinn_residual_problem",{"tool":39,"command":40},"pnpm","pnpm dlx skills add VectorSpaceLab\u002FAREX-Skill --skill pinn_residual_problem","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Ftask-oriented\u002FPaperBench\u002Fskills\u002Fpinn\u002Fskill\u002Fntk_pinn_analysis\u002Fpinn_residual_problem\u002FSKILL.md",true,[44,50,54],{"path":45,"resourceType":46,"resourceTypeDesc":47,"sizeBytes":48,"sourceHtmlUrl":49},"scripts\u002Fpoisson_problem.py","SCRIPT","脚本",3053,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Ftask-oriented\u002FPaperBench\u002Fskills\u002Fpinn\u002Fskill\u002Fntk_pinn_analysis\u002Fpinn_residual_problem\u002Fscripts\u002Fpoisson_problem.py",{"path":51,"resourceType":46,"resourceTypeDesc":47,"sizeBytes":52,"sourceHtmlUrl":53},"tests\u002Ftest_high_frequency_problem.py",347,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Ftask-oriented\u002FPaperBench\u002Fskills\u002Fpinn\u002Fskill\u002Fntk_pinn_analysis\u002Fpinn_residual_problem\u002Ftests\u002Ftest_high_frequency_problem.py",{"path":55,"resourceType":46,"resourceTypeDesc":47,"sizeBytes":56,"sourceHtmlUrl":57},"tests\u002Ftest_poisson_problem.py",693,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Ftask-oriented\u002FPaperBench\u002Fskills\u002Fpinn\u002Fskill\u002Fntk_pinn_analysis\u002Fpinn_residual_problem\u002Ftests\u002Ftest_poisson_problem.py",[59,66,73,80,87,94],{"skillNo":60,"name":61,"nameZh":6,"description":62,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":63,"categories":64,"keywords":65,"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":67,"name":68,"nameZh":6,"description":69,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":70,"categories":71,"keywords":72,"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":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":79,"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":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":86,"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":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":93,"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":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":98,"categories":99,"keywords":100,"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",[],[]]