[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M795NF259PQJWT20EC04H3P7":3,"skill-same:01M795NF259PQJWT20EC04H3P7":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},"01M795NF259PQJWT20EC04H3P7","cli",null,"Routes `stitch` command-line usage, flag selection, feature-mask validation, affine mode, verbose output, and headless or Docker command building for stitching panoramas.","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\u002Fstitching\u002Fsub-skills\u002Fcli\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: cli\ndescription: \"Routes `stitch` command-line usage, flag selection, feature-mask\n  validation, affine mode, verbose output, and headless or Docker command\n  building for stitching panoramas.\"\ndisable-model-invocation: true\nmetadata:\n  disco-role: operating\nlicense: Apache 2.0\n---\n\n# CLI\n\nUse this sub-skill when a user wants to build or debug a `stitch` command-line\ninvocation rather than write Python code.\n\n## Typical triggers\n\n- \"stitch these images from the shell\"\n- \"what flags should I pass to `stitch`?\"\n- \"I need affine mode or verbose output\"\n- \"how do I use feature masks from the CLI?\"\n- \"why does `stitch` fail in a headless session?\"\n\n## What this sub-skill owns\n\n- The public `stitch` entry point.\n- Positional image arguments, glob patterns, and output paths.\n- `--affine`, `--verbose`, `--verbose_dir`, `--output`, `--output_params`.\n- Feature and matcher tuning flags such as `--detector`, `--nfeatures`,\n  `--matcher_type`, `--range_width`, `--confidence_threshold`,\n  `--matches_graph_dot_file`, `--warper_type`, `--finder`, and crop controls.\n- The optional `--feature_masks` and `--try_use_gpu` flags.\n- CLI troubleshooting for missing files, mask count problems, preview windows,\n  and output selection.\n\n## What this sub-skill does not own\n\n- Python `Stitcher` object configuration; use [python-api](..\u002Fpython-api\u002FSKILL.md).\n- Verbose file interpretation and match-graph analysis; use\n  [diagnostics](..\u002Fdiagnostics\u002FSKILL.md).\n- Install\u002Fimport troubleshooting; use the root [troubleshooting](..\u002F..\u002Freferences\u002Ftroubleshooting.md).\n\n## Start with these bundled files\n\n- [CLI reference](references\u002Fcli-reference.md) for verified flags and choices.\n- [Workflow recipes](references\u002Fworkflows.md) for copyable command examples.\n- [Troubleshooting](references\u002Ftroubleshooting.md) for common CLI failures.\n- [Validate CLI args](scripts\u002Fvalidate_cli_args.py) when you want a safe\n  checker that validates input globs and feature-mask counts without running a\n  full stitch.\n\n## Fast routing hints\n\n- Need a single command to stitch a glob of files? Read the workflow recipes.\n- Need to check whether a feature-mask list is valid before a full stitch?\n  Run the bundled validator.\n- Need to interpret dropped images or sparse matches? Route to diagnostics.\n\n## Common success criteria\n\nA good answer from this sub-skill should include:\n\n- The exact `stitch` command or command template.\n- The meaning of the relevant flags.\n- The expected output file or verbose directory.\n- A recovery path if masks, globs, or preview windows fail.\n","# CLI\n\nUse this sub-skill when a user wants to build or debug a `stitch` command-line\ninvocation rather than write Python code.\n\n## Typical triggers\n\n- \"stitch these images from the shell\"\n- \"what flags should I pass to `stitch`?\"\n- \"I need affine mode or verbose output\"\n- \"how do I use feature masks from the CLI?\"\n- \"why does `stitch` fail in a headless session?\"\n\n## What this sub-skill owns\n\n- The public `stitch` entry point.\n- Positional image arguments, glob patterns, and output paths.\n- `--affine`, `--verbose`, `--verbose_dir`, `--output`, `--output_params`.\n- Feature and matcher tuning flags such as `--detector`, `--nfeatures`,\n  `--matcher_type`, `--range_width`, `--confidence_threshold`,\n  `--matches_graph_dot_file`, `--warper_type`, `--finder`, and crop controls.\n- The optional `--feature_masks` and `--try_use_gpu` flags.\n- CLI troubleshooting for missing files, mask count problems, preview windows,\n  and output selection.\n\n## What this sub-skill does not own\n\n- Python `Stitcher` object configuration; use [python-api](..\u002Fpython-api\u002FSKILL.md).\n- Verbose file interpretation and match-graph analysis; use\n  [diagnostics](..\u002Fdiagnostics\u002FSKILL.md).\n- Install\u002Fimport troubleshooting; use the root [troubleshooting](..\u002F..\u002Freferences\u002Ftroubleshooting.md).\n\n## Start with these bundled files\n\n- [CLI reference](references\u002Fcli-reference.md) for verified flags and choices.\n- [Workflow recipes](references\u002Fworkflows.md) for copyable command examples.\n- [Troubleshooting](references\u002Ftroubleshooting.md) for common CLI failures.\n- [Validate CLI args](scripts\u002Fvalidate_cli_args.py) when you want a safe\n  checker that validates input globs and feature-mask counts without running a\n  full stitch.\n\n## Fast routing hints\n\n- Need a single command to stitch a glob of files? Read the workflow recipes.\n- Need to check whether a feature-mask list is valid before a full stitch?\n  Run the bundled validator.\n- Need to interpret dropped images or sparse matches? Route to diagnostics.\n\n## Common success criteria\n\nA good answer from this sub-skill should include:\n\n- The exact `stitch` command or command template.\n- The meaning of the relevant flags.\n- The expected output file or verbose directory.\n- A recovery path if masks, globs, or preview windows fail.\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\u002Fstitching\u002Fsub-skills\u002Fcli",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 cli",{"tool":40,"command":41},"bunx","bunx skills add VectorSpaceLab\u002FAREX-Skill --skill cli",{"tool":43,"command":44},"pnpm","pnpm dlx skills add VectorSpaceLab\u002FAREX-Skill --skill cli","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fstitching\u002Fsub-skills\u002Fcli\u002FSKILL.md",[47,53,57,61],{"path":48,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":51,"sourceHtmlUrl":52},"references\u002Fcli-reference.md","REFERENCE","参考文档",4359,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fstitching\u002Fsub-skills\u002Fcli\u002Freferences\u002Fcli-reference.md",{"path":54,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":55,"sourceHtmlUrl":56},"references\u002Ftroubleshooting.md",1960,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fstitching\u002Fsub-skills\u002Fcli\u002Freferences\u002Ftroubleshooting.md",{"path":58,"resourceType":49,"resourceTypeDesc":50,"sizeBytes":59,"sourceHtmlUrl":60},"references\u002Fworkflows.md",3051,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fstitching\u002Fsub-skills\u002Fcli\u002Freferences\u002Fworkflows.md",{"path":62,"resourceType":63,"resourceTypeDesc":64,"sizeBytes":65,"sourceHtmlUrl":66},"scripts\u002Fvalidate_cli_args.py","SCRIPT","脚本",3466,"https:\u002F\u002Fgithub.com\u002FVectorSpaceLab\u002FAREX-Skill\u002Fblob\u002Fmain\u002Fskills\u002Frepositories\u002Frepo-skills\u002Fstitching\u002Fsub-skills\u002Fcli\u002Fscripts\u002Fvalidate_cli_args.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",[],[]]