[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7ECMH8S4B7TAM4126SF8DFR":3,"skill-same:01M7ECMH8S4B7TAM4126SF8DFR":103},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":29,"commitSha":30,"skillDirectory":31,"forksCount":32,"repositoryHtmlUrl":33,"repositoryHomepageUrl":6,"ownerHtmlUrl":34,"ownerBlogUrl":6,"ownerDescription":35,"updatedAt":36,"install":37,"resources":50},"01M7ECMH8S4B7TAM4126SF8DFR","pathml",null,"Use PathML for local, research-only computational pathology workflows: load and tile slides, build preprocessing and QC pipelines, manage h5path data, quantify multiplex images, construct spatial graphs, and plan bounded model inference.","sanjeevafk\u002Fagent-skills","sanjeevafk","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F230678117?v=4","01M7C540EFY46ETG1NNWKS5X3Q","Sanjeev","01M7C540F366J5C1D587BMKH9Q",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fcollections\u002Fscientific-skills\u002Fpathml\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: pathml\ndescription: \"Use PathML for local, research-only computational pathology workflows: load and tile slides, build preprocessing and QC pipelines, manage h5path data, quantify multiplex images, construct spatial graphs, and plan bounded model inference.\"\nlicense: MIT\ncompatibility: PathML 3.0.5 is the latest PyPI release and targets Python 3.10-3.12; installation needs uv plus platform libraries for OpenSlide, BLAS\u002FLAPACK, and Java\u002FBio-Formats. Bundled Python 3.10+ CLIs are local, bounded, dependency-free, and network-free.\nallowed-tools: Read Write Edit Bash Glob\nmetadata:\n  version: \"1.1\"\n  skill-author: K-Dense Inc.\n---\n\n# PathML\n\n## Scope and safety boundary\n\nUse PathML for **local computational pathology research**. It is beta research\nsoftware, not a validated medical device, diagnostic system, clinical decision\nsupport tool, or substitute for a pathologist. Do not use outputs to diagnose,\ngrade, stage, or treat a patient.\n\nPathology files may contain faces, labels, accession numbers, patient identifiers,\nDICOM tags, filenames, or linked clinical data. Before processing:\n\n1. Confirm authorization, consent\u002Fwaiver, data-use terms, and institutional policy.\n2. De-identify pixels and metadata; keep the re-identification key outside the\n   analysis workspace.\n3. Use pseudonymous `patient_id`, `slide_id`, and `specimen_id` values. Do not put\n   direct identifiers in filenames, logs, `.h5path` labels, model cards, or reports.\n4. Keep inputs, intermediates, and outputs on approved local encrypted storage.\n5. Split by patient (then slide) before tiling or fitting any preprocessing step.\n\n## Version baseline, verified 2026-07-23\n\n- **Installable stable release:** PyPI `pathml==3.0.5`, published 2026-03-24.\n- The v3.0.5 release notes state Python **3.10-3.12** and sunset 3.9.\n  PyPI does not declare `Requires-Python` and still has a stale 3.8 classifier, so\n  use the release statement and test the exact environment.\n- GitHub releases v3.0.6 (2026-04-14) and v3.0.7 (2026-07-09) exist, but PyPI has\n  no artifacts for them as of this review. v3.0.7 updates Torch\u002FTorchVision\u002F\n  torch-geometric and ONNX export code. Do not mix those source dependencies with\n  the 3.0.5 wheel.\n- ReadTheDocs `\u002Flatest` identifies itself as 3.0.5. Examples here were checked\n  against the v3.0.5 tag and PyPI wheel metadata, not unversioned snippets.\n- This skill is MIT-licensed. PathML itself is GPL-2.0 with upstream commercial\n  licensing options; review upstream terms before redistribution.\n\n## Reproducible installation\n\nUse Python 3.11 unless the project has tested another supported interpreter:\n\n```bash\nuv venv --python 3.11\nsource .venv\u002Fbin\u002Factivate\nuv pip install \"pathml==3.0.5\"\npython -c \"import importlib.metadata as m; print(m.version('pathml'))\"\n```\n\nPathML 3.0.5 declares no package extras: do **not** use `pathml[all]`. Its base\ndistribution pins a large scientific\u002FML stack, including Torch 2.8.0, ONNX 1.17.0,\nONNX Runtime 1.17.x, OpenSlide Python 1.3.1, python-bioformats 4.1.0, and\npython-javabridge 4.0.4.\n\nInstall native prerequisites before the uv command:\n\n```bash\n# Debian\u002FUbuntu\nsudo apt-get install openslide-tools gcc g++ libblas-dev liblapack-dev openjdk-17-jdk\n\n# macOS\nbrew install openslide openjdk@17\n\n# Windows OpenSlide option documented upstream\nvcpkg install openslide\n```\n\nJava\u002FBio-Formats is needed for the broad multidimensional format backend.\nOpenSlide handles common brightfield WSI formats more efficiently. CUDA is\noptional and must match the pinned PyTorch build; follow PyTorch's platform\nselector rather than guessing a CUDA wheel. See `references\u002Fimage_loading.md`.\n\n## Stable minimal workflow\n\nPathML 3.0.5 uses slide convenience classes and `SlideData.run()`. It does not\nprovide `SlideData.from_slide()`, and `Pipeline` does not have `run()`:\n\n```python\nfrom pathml.core import HESlide\nfrom pathml.preprocessing import BoxBlur, Pipeline, TissueDetectionHE\n\nslide = HESlide(\"data\u002Fpseudonymous_slide.svs\", backend=\"openslide\")\npipeline = Pipeline(\n    [\n        BoxBlur(kernel_size=5),\n        TissueDetectionHE(mask_name=\"tissue\", min_region_size=5000),\n    ]\n)\nslide.run(\n    pipeline,\n    distributed=False,\n    tile_size=512,\n    tile_stride=512,\n    level=0,\n    tile_pad=False,\n)\nslide.write(\"derived\u002Fpseudonymous_slide.h5path\")\n```\n\nStart with a bounded manual sample before a full run:\n\n```python\nfrom itertools import islice\n\nfor tile in islice(slide.generate_tiles(shape=512, stride=512, level=0), 8):\n    pipeline.apply(tile)\n    assert tile.masks[\"tissue\"].shape[:2] == tile.image.shape[:2]\n```\n\nTiles use `(i, j)` = `(row, column)` coordinates at the selected pyramid level.\nFor OpenSlide, PathML maps them to level-0 coordinates internally. Record the\nlevel and downsample; convert to `(x, y)` or micrometres explicitly downstream.\n\n## Research workflow\n\n1. **Inventory locally.** Validate the manifest, reject URLs\u002Fsymlinks, inspect only\n   allowlisted technical metadata, and remove identifiers.\n2. **Freeze splits.** Assign every patient and all their slides to one split before\n   generating overlapping tiles, graphs, normalization references, or features.\n3. **Plan bounds.** Estimate tile count, RAM, output size, and pipeline stages.\n4. **Pilot preprocessing.** Inspect tissue masks, whitespace\u002Fartifact labels,\n   stain behavior, edge padding, and empty-mask cases on representative training\n   slides. Do not tune from test slides.\n5. **Run and preserve coordinates.** Keep tile level, `(i, j)`, downsample, MPP,\n   mask names, QC decisions, and failed\u002Fskipped tiles.\n6. **Build spatial data deliberately.** Validate channel order, physical units,\n   instance labels, node-feature alignment, graph edges, and cell-to-tissue\n   assignments.\n7. **Infer in bounded batches.** Verify model provenance and checksum without\n   loading unknown pickle checkpoints. Keep predictions linked to slide\u002Ftile\n   coordinates and stitch overlaps with a documented rule.\n8. **Report provenance and limits.** Include package lock, source hashes, scanner,\n   stain, parameters, seeds, split manifest, model card, exclusions, and QC.\n\n## No-network default and explicit consent gate\n\nDo not instantiate download-capable classes or set dataset `download=True` unless\nthe user explicitly opts in after receiving the endpoint and disclosure:\n\n- `SegmentMIFRemote` downloads an ONNX file from\n  `https:\u002F\u002Fhuggingface.co\u002Fpathml\u002Ftest\u002Fresolve\u002Fmain\u002Fmesmer.onnx` at construction,\n  then runs inference locally. Stable source does **not** upload image pixels.\n  The request still discloses network metadata such as IP address and headers and\n  creates `temp.onnx`; there is no built-in checksum or offline flag.\n- Deprecated `SegmentMIF` imports local DeepCell Mesmer, but DeepCell model\n  initialization may need separately provisioned weights. It is not a PathML\n  extra and is not the preferred stable API.\n- `RemoteTestHoverNet` downloads a model from Hugging Face.\n- `PanNukeDataModule(download=True)` contacts Warwick; `DeepFocusDataModule`\n  contacts Zenodo. Both default to `download=False`.\n\nBefore any future hosted prediction call, state the exact destination, pixel\nchannels\u002Fregions, metadata, identifiers, retention, legal basis, and safeguards;\nobtain explicit consent; and never send PHI by default. Prefer reviewed,\nchecksummed local model artifacts and local inference.\n\n## Model-code security\n\n- PyTorch `model.eval()` means **evaluation mode** for modules; it is not Python's\n  dangerous built-in evaluator. Never use Python dynamic evaluation or execution.\n- Do not name local files `pathml.py`, `torch.py`, `onnx.py`, or after standard\n  libraries; shadow modules can silently change imports.\n- PathML's `EntityDataset` loads `.pt` objects with `weights_only=False`. Never\n  open an untrusted graph\u002Fcheckpoint. Treat pickle-based pipelines and `.pt` files\n  as executable code.\n- ONNX is safer than pickle but not inherently trusted. Verify source, SHA-256,\n  expected input\u002Foutput schema, file size, and runtime limits; use isolation for\n  third-party models.\n\n## Bundled local CLIs\n\nAll helpers reject URLs and symlinks, cap inputs\u002Fwork, use strict JSON, avoid\nnetwork access, and require no PathML import for `--help`:\n\n```bash\npython scripts\u002Fslide_manifest.py validate --manifest manifest.csv --root .\npython scripts\u002Fslide_manifest.py inspect --slide data\u002Fexample.svs --root .\npython scripts\u002Fplan_pipeline.py --width 100000 --height 80000 --tile-size 512 --stride 512\npython scripts\u002Fimage_qc.py synthetic --width 256 --height 256\npython scripts\u002Fvalidate_spatial_schema.py graph --input graph.json --root .\npython scripts\u002Fvalidate_spatial_schema.py multiplex --input cells.csv --root .\npython scripts\u002Fplan_inference.py --tile-count 4000 --batch-size 16 --height 256 --width 256\n```\n\nThe inference planner reads numbers or a bounded JSON model card only; it never\nimports a model framework or opens a checkpoint.\n\n## Detailed references\n\n- `references\u002Fimage_loading.md` — slide classes, backends, formats, levels,\n  coordinates, technical metadata, and privacy.\n- `references\u002Fpreprocessing.md` — stable transforms, masks\u002FQC, stain processing,\n  pipeline execution, and leakage prevention.\n- `references\u002Fdata_management.md` — `.h5path`, manifests, datasets, provenance,\n  splits, and safe downloads.\n- `references\u002Fmultiparametric.md` — multidimensional layout, CODEX\u002FVectra,\n  quantification, AnnData, DeepCell\u002FMesmer, and network disclosure.\n- `references\u002Fgraphs.md` — instance maps, feature alignment, KNN\u002FRAG\u002FHACT graphs,\n  spatial units, schemas, and validation.\n- `references\u002Fmachine_learning.md` — HoVer-Net\u002FHACTNet, local ONNX inference,\n  batching, checkpoint trust, evaluation, and model provenance.\n\n## Primary sources\n\nAll checked 2026-07-23:\n\n- PyPI metadata: https:\u002F\u002Fpypi.org\u002Fproject\u002Fpathml\u002F3.0.5\u002F\n- Stable source tag: https:\u002F\u002Fgithub.com\u002FDana-Farber-AIOS\u002Fpathml\u002Ftree\u002Fv3.0.5\n- Releases: https:\u002F\u002Fgithub.com\u002FDana-Farber-AIOS\u002Fpathml\u002Freleases\n- Stable documentation: https:\u002F\u002Fpathml.readthedocs.io\u002Fen\u002Fstable\u002F\n- Rosenthal et al. (2022), PathML toolkit:\n  https:\u002F\u002Fdoi.org\u002F10.1158\u002F1541-7786.MCR-21-0665\n- Omar et al. (2025), multiplex workflows:\n  https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.labinv.2025.104220\n","# PathML\n\n## Scope and safety boundary\n\nUse PathML for **local computational pathology research**. It is beta research\nsoftware, not a validated medical device, diagnostic system, clinical decision\nsupport tool, or substitute for a pathologist. Do not use outputs to diagnose,\ngrade, stage, or treat a patient.\n\nPathology files may contain faces, labels, accession numbers, patient identifiers,\nDICOM tags, filenames, or linked clinical data. Before processing:\n\n1. Confirm authorization, consent\u002Fwaiver, data-use terms, and institutional policy.\n2. De-identify pixels and metadata; keep the re-identification key outside the\n   analysis workspace.\n3. Use pseudonymous `patient_id`, `slide_id`, and `specimen_id` values. Do not put\n   direct identifiers in filenames, logs, `.h5path` labels, model cards, or reports.\n4. Keep inputs, intermediates, and outputs on approved local encrypted storage.\n5. Split by patient (then slide) before tiling or fitting any preprocessing step.\n\n## Version baseline, verified 2026-07-23\n\n- **Installable stable release:** PyPI `pathml==3.0.5`, published 2026-03-24.\n- The v3.0.5 release notes state Python **3.10-3.12** and sunset 3.9.\n  PyPI does not declare `Requires-Python` and still has a stale 3.8 classifier, so\n  use the release statement and test the exact environment.\n- GitHub releases v3.0.6 (2026-04-14) and v3.0.7 (2026-07-09) exist, but PyPI has\n  no artifacts for them as of this review. v3.0.7 updates Torch\u002FTorchVision\u002F\n  torch-geometric and ONNX export code. Do not mix those source dependencies with\n  the 3.0.5 wheel.\n- ReadTheDocs `\u002Flatest` identifies itself as 3.0.5. Examples here were checked\n  against the v3.0.5 tag and PyPI wheel metadata, not unversioned snippets.\n- This skill is MIT-licensed. PathML itself is GPL-2.0 with upstream commercial\n  licensing options; review upstream terms before redistribution.\n\n## Reproducible installation\n\nUse Python 3.11 unless the project has tested another supported interpreter:\n\n```bash\nuv venv --python 3.11\nsource .venv\u002Fbin\u002Factivate\nuv pip install \"pathml==3.0.5\"\npython -c \"import importlib.metadata as m; print(m.version('pathml'))\"\n```\n\nPathML 3.0.5 declares no package extras: do **not** use `pathml[all]`. Its base\ndistribution pins a large scientific\u002FML stack, including Torch 2.8.0, ONNX 1.17.0,\nONNX Runtime 1.17.x, OpenSlide Python 1.3.1, python-bioformats 4.1.0, and\npython-javabridge 4.0.4.\n\nInstall native prerequisites before the uv command:\n\n```bash\n# Debian\u002FUbuntu\nsudo apt-get install openslide-tools gcc g++ libblas-dev liblapack-dev openjdk-17-jdk\n\n# macOS\nbrew install openslide openjdk@17\n\n# Windows OpenSlide option documented upstream\nvcpkg install openslide\n```\n\nJava\u002FBio-Formats is needed for the broad multidimensional format backend.\nOpenSlide handles common brightfield WSI formats more efficiently. CUDA is\noptional and must match the pinned PyTorch build; follow PyTorch's platform\nselector rather than guessing a CUDA wheel. See `references\u002Fimage_loading.md`.\n\n## Stable minimal workflow\n\nPathML 3.0.5 uses slide convenience classes and `SlideData.run()`. It does not\nprovide `SlideData.from_slide()`, and `Pipeline` does not have `run()`:\n\n```python\nfrom pathml.core import HESlide\nfrom pathml.preprocessing import BoxBlur, Pipeline, TissueDetectionHE\n\nslide = HESlide(\"data\u002Fpseudonymous_slide.svs\", backend=\"openslide\")\npipeline = Pipeline(\n    [\n        BoxBlur(kernel_size=5),\n        TissueDetectionHE(mask_name=\"tissue\", min_region_size=5000),\n    ]\n)\nslide.run(\n    pipeline,\n    distributed=False,\n    tile_size=512,\n    tile_stride=512,\n    level=0,\n    tile_pad=False,\n)\nslide.write(\"derived\u002Fpseudonymous_slide.h5path\")\n```\n\nStart with a bounded manual sample before a full run:\n\n```python\nfrom itertools import islice\n\nfor tile in islice(slide.generate_tiles(shape=512, stride=512, level=0), 8):\n    pipeline.apply(tile)\n    assert tile.masks[\"tissue\"].shape[:2] == tile.image.shape[:2]\n```\n\nTiles use `(i, j)` = `(row, column)` coordinates at the selected pyramid level.\nFor OpenSlide, PathML maps them to level-0 coordinates internally. Record the\nlevel and downsample; convert to `(x, y)` or micrometres explicitly downstream.\n\n## Research workflow\n\n1. **Inventory locally.** Validate the manifest, reject URLs\u002Fsymlinks, inspect only\n   allowlisted technical metadata, and remove identifiers.\n2. **Freeze splits.** Assign every patient and all their slides to one split before\n   generating overlapping tiles, graphs, normalization references, or features.\n3. **Plan bounds.** Estimate tile count, RAM, output size, and pipeline stages.\n4. **Pilot preprocessing.** Inspect tissue masks, whitespace\u002Fartifact labels,\n   stain behavior, edge padding, and empty-mask cases on representative training\n   slides. Do not tune from test slides.\n5. **Run and preserve coordinates.** Keep tile level, `(i, j)`, downsample, MPP,\n   mask names, QC decisions, and failed\u002Fskipped tiles.\n6. **Build spatial data deliberately.** Validate channel order, physical units,\n   instance labels, node-feature alignment, graph edges, and cell-to-tissue\n   assignments.\n7. **Infer in bounded batches.** Verify model provenance and checksum without\n   loading unknown pickle checkpoints. Keep predictions linked to slide\u002Ftile\n   coordinates and stitch overlaps with a documented rule.\n8. **Report provenance and limits.** Include package lock, source hashes, scanner,\n   stain, parameters, seeds, split manifest, model card, exclusions, and QC.\n\n## No-network default and explicit consent gate\n\nDo not instantiate download-capable classes or set dataset `download=True` unless\nthe user explicitly opts in after receiving the endpoint and disclosure:\n\n- `SegmentMIFRemote` downloads an ONNX file from\n  `https:\u002F\u002Fhuggingface.co\u002Fpathml\u002Ftest\u002Fresolve\u002Fmain\u002Fmesmer.onnx` at construction,\n  then runs inference locally. Stable source does **not** upload image pixels.\n  The request still discloses network metadata such as IP address and headers and\n  creates `temp.onnx`; there is no built-in checksum or offline flag.\n- Deprecated `SegmentMIF` imports local DeepCell Mesmer, but DeepCell model\n  initialization may need separately provisioned weights. It is not a PathML\n  extra and is not the preferred stable API.\n- `RemoteTestHoverNet` downloads a model from Hugging Face.\n- `PanNukeDataModule(download=True)` contacts Warwick; `DeepFocusDataModule`\n  contacts Zenodo. Both default to `download=False`.\n\nBefore any future hosted prediction call, state the exact destination, pixel\nchannels\u002Fregions, metadata, identifiers, retention, legal basis, and safeguards;\nobtain explicit consent; and never send PHI by default. Prefer reviewed,\nchecksummed local model artifacts and local inference.\n\n## Model-code security\n\n- PyTorch `model.eval()` means **evaluation mode** for modules; it is not Python's\n  dangerous built-in evaluator. Never use Python dynamic evaluation or execution.\n- Do not name local files `pathml.py`, `torch.py`, `onnx.py`, or after standard\n  libraries; shadow modules can silently change imports.\n- PathML's `EntityDataset` loads `.pt` objects with `weights_only=False`. Never\n  open an untrusted graph\u002Fcheckpoint. Treat pickle-based pipelines and `.pt` files\n  as executable code.\n- ONNX is safer than pickle but not inherently trusted. Verify source, SHA-256,\n  expected input\u002Foutput schema, file size, and runtime limits; use isolation for\n  third-party models.\n\n## Bundled local CLIs\n\nAll helpers reject URLs and symlinks, cap inputs\u002Fwork, use strict JSON, avoid\nnetwork access, and require no PathML import for `--help`:\n\n```bash\npython scripts\u002Fslide_manifest.py validate --manifest manifest.csv --root .\npython scripts\u002Fslide_manifest.py inspect --slide data\u002Fexample.svs --root .\npython scripts\u002Fplan_pipeline.py --width 100000 --height 80000 --tile-size 512 --stride 512\npython scripts\u002Fimage_qc.py synthetic --width 256 --height 256\npython scripts\u002Fvalidate_spatial_schema.py graph --input graph.json --root .\npython scripts\u002Fvalidate_spatial_schema.py multiplex --input cells.csv --root .\npython scripts\u002Fplan_inference.py --tile-count 4000 --batch-size 16 --height 256 --width 256\n```\n\nThe inference planner reads numbers or a bounded JSON model card only; it never\nimports a model framework or opens a checkpoint.\n\n## Detailed references\n\n- `references\u002Fimage_loading.md` — slide classes, backends, formats, levels,\n  coordinates, technical metadata, and privacy.\n- `references\u002Fpreprocessing.md` — stable transforms, masks\u002FQC, stain processing,\n  pipeline execution, and leakage prevention.\n- `references\u002Fdata_management.md` — `.h5path`, manifests, datasets, provenance,\n  splits, and safe downloads.\n- `references\u002Fmultiparametric.md` — multidimensional layout, CODEX\u002FVectra,\n  quantification, AnnData, DeepCell\u002FMesmer, and network disclosure.\n- `references\u002Fgraphs.md` — instance maps, feature alignment, KNN\u002FRAG\u002FHACT graphs,\n  spatial units, schemas, and validation.\n- `references\u002Fmachine_learning.md` — HoVer-Net\u002FHACTNet, local ONNX inference,\n  batching, checkpoint trust, evaluation, and model provenance.\n\n## Primary sources\n\nAll checked 2026-07-23:\n\n- PyPI metadata: https:\u002F\u002Fpypi.org\u002Fproject\u002Fpathml\u002F3.0.5\u002F\n- Stable source tag: https:\u002F\u002Fgithub.com\u002FDana-Farber-AIOS\u002Fpathml\u002Ftree\u002Fv3.0.5\n- Releases: https:\u002F\u002Fgithub.com\u002FDana-Farber-AIOS\u002Fpathml\u002Freleases\n- Stable documentation: https:\u002F\u002Fpathml.readthedocs.io\u002Fen\u002Fstable\u002F\n- Rosenthal et al. (2022), PathML toolkit:\n  https:\u002F\u002Fdoi.org\u002F10.1158\u002F1541-7786.MCR-21-0665\n- Omar et al. (2025), multiplex workflows:\n  https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.labinv.2025.104220\n",{"name":5,"license":15,"metadata":24,"description":7,"allowed-tools":27,"compatibility":28},{"version":25,"skill-author":26},"1.1","K-Dense Inc.","Read Write Edit Bash Glob","PathML 3.0.5 is the latest PyPI release and targets Python 3.10-3.12; installation needs uv plus platform libraries for OpenSlide, BLAS\u002FLAPACK, and Java\u002FBio-Formats. Bundled Python 3.10+ CLIs are local, bounded, dependency-free, and network-free.","main","4e084e84eb42fc53e144c89c0f7f48b39c8a4b0f","collections\u002Fscientific-skills\u002Fpathml",1,"https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills","https:\u002F\u002Fgithub.com\u002Fsanjeevafk","First make it work. Then make it right. Then make it fast.","2026-11-14 13:36:16",{"source":8,"skillName":5,"commands":38,"prompt":48,"zipAvailable":49},[39,42,45],{"tool":40,"command":41},"npx","npx skills add sanjeevafk\u002Fagent-skills --skill pathml",{"tool":43,"command":44},"bunx","bunx skills add sanjeevafk\u002Fagent-skills --skill pathml",{"tool":46,"command":47},"pnpm","pnpm dlx skills add sanjeevafk\u002Fagent-skills --skill pathml","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fcollections\u002Fscientific-skills\u002Fpathml\u002FSKILL.md",true,[51,57,61,65,69,73,77,83,87,91,95,99],{"path":52,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":55,"sourceHtmlUrl":56},"references\u002Fdata_management.md","REFERENCE","参考文档",11488,"https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fcollections\u002Fscientific-skills\u002Fpathml\u002Freferences\u002Fdata_management.md",{"path":58,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":59,"sourceHtmlUrl":60},"references\u002Fgraphs.md",10536,"https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fcollections\u002Fscientific-skills\u002Fpathml\u002Freferences\u002Fgraphs.md",{"path":62,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":63,"sourceHtmlUrl":64},"references\u002Fimage_loading.md",9838,"https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fcollections\u002Fscientific-skills\u002Fpathml\u002Freferences\u002Fimage_loading.md",{"path":66,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":67,"sourceHtmlUrl":68},"references\u002Fmachine_learning.md",12639,"https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fcollections\u002Fscientific-skills\u002Fpathml\u002Freferences\u002Fmachine_learning.md",{"path":70,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":71,"sourceHtmlUrl":72},"references\u002Fmultiparametric.md",12130,"https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fcollections\u002Fscientific-skills\u002Fpathml\u002Freferences\u002Fmultiparametric.md",{"path":74,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":75,"sourceHtmlUrl":76},"references\u002Fpreprocessing.md",11904,"https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fcollections\u002Fscientific-skills\u002Fpathml\u002Freferences\u002Fpreprocessing.md",{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"scripts\u002F_common.py","SCRIPT","脚本",12105,"https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fcollections\u002Fscientific-skills\u002Fpathml\u002Fscripts\u002F_common.py",{"path":84,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":85,"sourceHtmlUrl":86},"scripts\u002Fimage_qc.py",11127,"https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fcollections\u002Fscientific-skills\u002Fpathml\u002Fscripts\u002Fimage_qc.py",{"path":88,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":89,"sourceHtmlUrl":90},"scripts\u002Fplan_inference.py",9691,"https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fcollections\u002Fscientific-skills\u002Fpathml\u002Fscripts\u002Fplan_inference.py",{"path":92,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":93,"sourceHtmlUrl":94},"scripts\u002Fplan_pipeline.py",9054,"https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fcollections\u002Fscientific-skills\u002Fpathml\u002Fscripts\u002Fplan_pipeline.py",{"path":96,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":97,"sourceHtmlUrl":98},"scripts\u002Fslide_manifest.py",14065,"https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fcollections\u002Fscientific-skills\u002Fpathml\u002Fscripts\u002Fslide_manifest.py",{"path":100,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":101,"sourceHtmlUrl":102},"scripts\u002Fvalidate_spatial_schema.py",16044,"https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fcollections\u002Fscientific-skills\u002Fpathml\u002Fscripts\u002Fvalidate_spatial_schema.py",[104,111,119,128,136,143],{"skillNo":105,"name":106,"nameZh":6,"description":107,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":108,"categories":109,"keywords":110,"publishedAt":19},"01M7EFHX67M3KKXT1BXVPA5ADT","zarr-python","Chunked N-D arrays for cloud storage (Zarr-Python 3). Compressed arrays, parallel I\u002FO, S3\u002FGCS via fsspec, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fzarr-python\u002FSKILL.md",[],[],{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":115,"sourceHtmlUrl":116,"categories":117,"keywords":118,"publishedAt":19},"01M7EFFQGM51TZBGRENXY50ZYM","whiteboard-diagrams","Create accurate, simple, hand-drawn whiteboard diagrams from a user's process, system, architecture, learning workflow, comparison, or causal explanation. Use when the user asks for a scribbly whiteboard visual, educational workflow graphic, hand-drawn explainer, or diagram like the bundled reference; ask one focused clarification question when the subject or relationships are missing, then generate and visually verify the final image.","https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fwhiteboard-diagrams\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fwhiteboard-diagrams\u002FSKILL.md",[],[],{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":123,"licenseFileUrl":124,"sourceHtmlUrl":125,"categories":126,"keywords":127,"publishedAt":19},"01M7EFF1CSQNTDMHFK4ZGY6NYK","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fwebapp-testing\u002FSKILL.md",[],[],{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":123,"licenseFileUrl":132,"sourceHtmlUrl":133,"categories":134,"keywords":135,"publishedAt":19},"01M7EFE8106X6C28FT8PA74427","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fweb-artifacts-builder\u002FSKILL.md",[],[],{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":142,"publishedAt":19},"01M7EFCSNZM9G7ZHS6F127Q71X","verification-loop","A comprehensive verification system for Claude Code sessions.","https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fverification-loop\u002FSKILL.md",[],[],{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":147,"sourceHtmlUrl":148,"categories":149,"keywords":150,"publishedAt":19},"01M7EFCE7EZMKPRYDDNKBQ9EEH","vercel-deploy","Deploy applications and websites to Vercel. Use when the user requests deployment actions like \"deploy my app\", \"deploy and give me the link\", \"push this live\", or \"create a preview deployment\".","https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fvercel-deploy\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fvercel-deploy\u002FSKILL.md",[],[]]