[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M78T2P7N1T1FJRNETMRGKRPB":3,"skill-same:01M78T2P7N1T1FJRNETMRGKRPB":71},{"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},"01M78T2P7N1T1FJRNETMRGKRPB","aws-sdk-pandas",null,"Routes AWS SDK for pandas (awswrangler) workflows across S3 lakehouse data, Athena\u002FGlue catalogs, SQL connectors, runtime configuration, and AWS service integrations.","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\u002Faws-sdk-pandas\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: aws-sdk-pandas\ndescription: \"Routes AWS SDK for pandas (awswrangler) workflows across S3\n  lakehouse data, Athena\u002FGlue catalogs, SQL connectors, runtime configuration,\n  and AWS service integrations.\"\ndisable-model-invocation: true\nmetadata:\n  disco-role: operating\nlicense: Apache 2.0\n---\n\n# AWS SDK for pandas\n\nUse this skill when a request names `awswrangler`, `aws-sdk-pandas`, `Pandas on AWS`, or a `wr.*` API from the repository.\n\nThis repo is a Python API package, not a CLI tool. The first questions are usually:\n- how to install the right extras,\n- how to import and verify the runtime,\n- which AWS service family the user is working with,\n- and which AWS credentials, region, or optional backend are required.\n\nRead `references\u002Fruntime-overview.md` first for install, extras, and runtime facts.\nRead `references\u002Ftroubleshooting.md` when import errors, missing extras, region issues, or AWS service prerequisites are blocking you.\nRead `references\u002Frepo-provenance.md` before deciding whether this skill is current for the checkout.\nRun `scripts\u002Fcheck_runtime.py` after installation to confirm importability and optional dependency availability.\n\n## Route map\n\n| If the user asks about... | Read this sub-skill |\n| --- | --- |\n| Installing, importing, setting `WR_*` env vars, switching `wr.engine` \u002F `wr.memory_format`, or debugging missing extras | `sub-skills\u002Fruntime-and-config\u002FSKILL.md` |\n| `wr.s3.*` object, file-format, dataset, S3 Tables, or S3 Vectors workflows | `sub-skills\u002Fs3-lakehouse\u002FSKILL.md` |\n| `wr.catalog.*`, `wr.athena.*`, `wr.data_quality.*`, or `wr.cleanrooms.*` workflows | `sub-skills\u002Fcatalog-and-query\u002FSKILL.md` |\n| `wr.redshift.*`, `wr.data_api.*`, `wr.mysql.*`, `wr.postgresql.*`, `wr.sqlserver.*`, or `wr.oracle.*` workflows | `sub-skills\u002Fsql-connectors\u002FSKILL.md` |\n| `wr.dynamodb.*`, `wr.timestream.*`, `wr.opensearch.*`, `wr.neptune.*`, `wr.quicksight.*`, `wr.cloudwatch.*`, `wr.emr.*`, `wr.emr_serverless.*`, `wr.sts.*`, `wr.secretsmanager.*`, or `wr.chime.*` workflows | `sub-skills\u002Fservice-integrations\u002FSKILL.md` |\n\n## Shared operating pattern\n\n1. Confirm the runtime with `scripts\u002Fcheck_runtime.py` when the package was just installed or the environment changed.\n2. Pick the service-family sub-skill that matches the `wr.*` API surface.\n3. Use `wr.config` and `boto3.Session` for defaults, region, and credentials.\n4. Install only the extras needed for the chosen workflow family.\n5. Keep live AWS operations separate from local smoke checks; if a workflow needs credentials or managed AWS resources, say so early.\n\n## Common signals\n\n- `NoCredentialsError` or `NoRegionError` usually means the AWS session is not configured.\n- `Missing optional dependency ...` usually means the relevant extra was not installed.\n- Ray\u002FModin questions belong in `runtime-and-config` even when they affect `wr.s3` or `wr.athena` calls.\n- Live AWS service prerequisites and IAM needs belong in the nearest service sub-skill, not here.\n\n## Shared references and scripts\n\n- `references\u002Fruntime-overview.md` for install commands, supported versions, and optional extras.\n- `references\u002Ftroubleshooting.md` for cross-cutting import, dependency, and credential failures.\n- `scripts\u002Fcheck_runtime.py` for a safe import\u002Fconfig\u002Fextra check.\n","# AWS SDK for pandas\n\nUse this skill when a request names `awswrangler`, `aws-sdk-pandas`, `Pandas on AWS`, or a `wr.*` API from the repository.\n\nThis repo is a Python API package, not a CLI tool. The first questions are usually:\n- how to install the right extras,\n- how to import and verify the runtime,\n- which AWS service family the user is working with,\n- and which AWS credentials, region, or optional backend are required.\n\nRead `references\u002Fruntime-overview.md` first for install, extras, and runtime facts.\nRead `references\u002Ftroubleshooting.md` when import errors, missing extras, region issues, or AWS service prerequisites are blocking you.\nRead `references\u002Frepo-provenance.md` before deciding whether this skill is current for the checkout.\nRun `scripts\u002Fcheck_runtime.py` after installation to confirm importability and optional dependency availability.\n\n## Route map\n\n| If the user asks about... | Read this sub-skill |\n| --- | --- |\n| Installing, importing, setting `WR_*` env vars, switching `wr.engine` \u002F `wr.memory_format`, or debugging missing extras | `sub-skills\u002Fruntime-and-config\u002FSKILL.md` |\n| `wr.s3.*` object, file-format, dataset, S3 Tables, or S3 Vectors workflows | `sub-skills\u002Fs3-lakehouse\u002FSKILL.md` |\n| `wr.catalog.*`, `wr.athena.*`, `wr.data_quality.*`, or `wr.cleanrooms.*` workflows | `sub-skills\u002Fcatalog-and-query\u002FSKILL.md` |\n| `wr.redshift.*`, `wr.data_api.*`, `wr.mysql.*`, `wr.postgresql.*`, `wr.sqlserver.*`, or `wr.oracle.*` workflows | `sub-skills\u002Fsql-connectors\u002FSKILL.md` |\n| `wr.dynamodb.*`, `wr.timestream.*`, `wr.opensearch.*`, `wr.neptune.*`, `wr.quicksight.*`, `wr.cloudwatch.*`, `wr.emr.*`, `wr.emr_serverless.*`, `wr.sts.*`, `wr.secretsmanager.*`, or `wr.chime.*` workflows | `sub-skills\u002Fservice-integrations\u002FSKILL.md` |\n\n## Shared operating pattern\n\n1. Confirm the runtime with `scripts\u002Fcheck_runtime.py` when the package was just installed or the environment changed.\n2. Pick the service-family sub-skill that matches the `wr.*` API surface.\n3. Use `wr.config` and `boto3.Session` for defaults, region, and credentials.\n4. Install only the extras needed for the chosen workflow family.\n5. Keep live AWS operations separate from local smoke checks; if a workflow needs credentials or managed AWS resources, say so early.\n\n## Common signals\n\n- `NoCredentialsError` or `NoRegionError` usually means the AWS session is not configured.\n- `Missing optional dependency ...` usually means the relevant extra was not installed.\n- Ray\u002FModin questions belong in `runtime-and-config` even when they affect `wr.s3` or `wr.athena` calls.\n- Live AWS service prerequisites and IAM needs belong in the nearest service sub-skill, not here.\n\n## Shared references and scripts\n\n- `references\u002Fruntime-overview.md` for install commands, supported versions, and optional extras.\n- `references\u002Ftroubleshooting.md` for cross-cutting import, dependency, and credential failures.\n- `scripts\u002Fcheck_runtime.py` for a safe import\u002Fconfig\u002Fextra check.\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\u002Faws-sdk-pandas",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 aws-sdk-pandas",{"tool":40,"command":41},"bunx","bunx skills add VectorSpaceLab\u002FAREX-Skill --skill aws-sdk-pandas",{"tool":43,"command":44},"pnpm","pnpm dlx skills add VectorSpaceLab\u002FAREX-Skill --skill aws-sdk-pandas","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Faws-sdk-pandas\u002FSKILL.md",[47,53,57,61,65],{"path":48,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":51,"sourceHtmlUrl":52},"references\u002Frepo-provenance.md","REFERENCE","参考文档",1346,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Faws-sdk-pandas\u002Freferences\u002Frepo-provenance.md",{"path":54,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":55,"sourceHtmlUrl":56},"references\u002Frepo-routing-metadata.json",407,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Faws-sdk-pandas\u002Freferences\u002Frepo-routing-metadata.json",{"path":58,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":59,"sourceHtmlUrl":60},"references\u002Fruntime-overview.md",3827,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Faws-sdk-pandas\u002Freferences\u002Fruntime-overview.md",{"path":62,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":63,"sourceHtmlUrl":64},"references\u002Ftroubleshooting.md",3414,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Faws-sdk-pandas\u002Freferences\u002Ftroubleshooting.md",{"path":66,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":69,"sourceHtmlUrl":70},"scripts\u002Fcheck_runtime.py","SCRIPT","脚本",5953,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Faws-sdk-pandas\u002Fscripts\u002Fcheck_runtime.py",[72,79,86,93,100,107],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"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.","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":14,"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":14,"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":14,"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":14,"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":14,"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",[],[]]