[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MB3KH5VCJDB0XH0RSNG5NX":3,"skill-same:01M6MB3KH5VCJDB0XH0RSNG5NX":128},{"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":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":57,"ownerHtmlUrl":58,"ownerBlogUrl":59,"ownerDescription":60,"updatedAt":61,"install":62,"resources":75},"01M6MB3KH5VCJDB0XH0RSNG5NX","scikit-survival",null,"Builds, evaluates, and audits right-censored survival analysis workflows with scikit-survival (sksurv): Cox PH, Coxnet, IPC ridge, survival trees, forests, boosting, and SVMs, plus nonparametric cumulative incidence for competing risks. Covers leakage-safe scikit-learn pipelines, nested CV, and censoring-aware metrics. Use when fitting survival models on time-to-event data, building structured outcome arrays, computing IPCW concordance, dynamic AUC, or Brier scores, estimating cause-specific cumulative incidence, or tuning models without leakage. Not for Fine-Gray regression, which scikit-survival does not provide.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1571,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fscikit-survival\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"scikit survival","survival","uv","when","which","data-science","free","scikit-learn","local","time","arrays","default","workflows","biostatistics","outcome","read","regression","stack","Bash","analysis","learn","2026-11-10 10:12:30",false,"---\nname: scikit-survival\ndescription: 'Builds, evaluates, and audits right-censored survival analysis workflows with scikit-survival (sksurv): Cox PH, Coxnet, IPC ridge, survival trees, forests, boosting, and SVMs, plus nonparametric cumulative incidence for competing risks. Covers leakage-safe scikit-learn pipelines, nested CV, and censoring-aware metrics. Use when fitting survival models on time-to-event data, building structured outcome arrays, computing IPCW concordance, dynamic AUC, or Brier scores, estimating cause-specific cumulative incidence, or tuning models without leakage. Not for Fine-Gray regression, which scikit-survival does not provide.'\nlicense: MIT\ncompatibility: Requires Python 3.11+, uv, and the pinned scikit-survival 0.28.0 stack for executable examples. Bundled CLIs are local and network-free by default.\nallowed-tools: Read Write Edit Bash\nmetadata:\n  version: '1.2'\n  category: data-science-and-ml\n  maintainer: Kalaris Labs\n  tags: survival analysis, Cox regression, time-to-event, biostatistics, machine learning\n---\n\n# scikit-survival\n\n## Scope\n\nUse this skill for scikit-survival 0.28.0 workflows involving:\n\n- right-censored structured outcomes;\n- Cox PH, Coxnet, IPC ridge, survival trees, forests, boosting, and SVMs;\n- discrimination, prediction error, calibration-oriented checks, and time-dependent prediction;\n- nonparametric cumulative incidence with competing risks;\n- scikit-learn pipelines, nested model selection, and reproducible reports.\n\nscikit-survival primarily models right-censored outcomes. Its built-in competing-risk\nsupport is nonparametric cumulative incidence; it does not provide Fine-Gray regression.\nDo not present model output as clinical advice, causal evidence, or proof of clinical\nutility.\n\n## Current release and installation\n\nVerified 2026-07-23:\n\n- Latest stable: **scikit-survival 0.28.0**, released 2026-07-05.\n- Python: **3.11 or later**; PyPI wheels cover CPython 3.11-3.14 on Linux\n  x86-64, macOS x86-64\u002FARM64, and Windows x86-64.\n- Runtime bounds: NumPy >=2.0.0, pandas >=2.2.0, SciPy >=1.13.0,\n  scikit-learn >=1.9.0,\u003C1.10, OSQP >=1.0.2, narwhals >=2.0.1.\n- 0.28 adds pandas\u002FPolars estimator support through narwhals and removes\n  `criterion` from `GradientBoostingSurvivalAnalysis`.\n\nCreate an isolated environment and install the tested snapshot:\n\n```bash\nuv venv --python 3.11\nsource .venv\u002Fbin\u002Factivate\nuv pip install \\\n  \"scikit-survival==0.28.0\" \\\n  \"scikit-learn==1.9.0\" \\\n  \"numpy==2.4.6\" \\\n  \"pandas==3.0.5\" \\\n  \"scipy==1.17.1\" \\\n  \"ecos==2.0.14\" \\\n  \"osqp==1.1.3\" \\\n  \"joblib==1.5.3\" \\\n  \"numexpr==2.14.2\" \\\n  \"narwhals==2.24.0\"\n```\n\nBinary wheels are preferred. A source build requires a C\u002FC++ compiler; OSQP may\nalso require CMake. This skill is MIT-licensed; the upstream scikit-survival package\nis GPL-3.0-or-later, so review upstream licensing before redistribution.\n\n## Non-negotiable workflow\n\n1. **Define the estimand and event coding.** Decide whether the target is\n   all-event survival, cause-specific hazard, or cause-specific cumulative incidence.\n2. **Validate outcomes.** Standard estimators need a two-field structured array:\n   boolean event first, observed time second. Competing-risk CIF instead needs a\n   separate integer event vector: 0=censored, 1..K=causes.\n3. **Split before learned preprocessing.** Never fit imputers, encoders, scalers,\n   feature selectors, or alpha choices on all rows before splitting.\n4. **Fit preprocessing inside a pipeline.** Unknown categories and missingness must\n   be handled using training-fold state only.\n5. **Tune without reusing evaluation data.** Use nested CV when reporting\n   cross-validated tuned performance, or reserve a truly untouched final holdout.\n6. **Fit censoring distributions on training data.** IPCW concordance, dynamic AUC,\n   and Brier metrics receive `survival_train`, never a pooled train+test outcome.\n7. **Restrict evaluation times.** Use a strictly increasing grid inside test\n   follow-up and below the end of training support where the estimated censoring\n   survival remains positive.\n8. **Match predictions to metrics.** Concordance\u002Fdynamic AUC consume higher-is-riskier\n   scores. Brier metrics consume survival probabilities with shape\n   `(n_test, n_times)`, not risk scores or unevaluated step functions.\n9. **Handle competing causes explicitly.** Standard survival probabilities and CIFs\n   answer different questions. Never estimate event-specific probability with\n   `1 - Kaplan-Meier` while censoring competing events.\n10. **Report limits.** Separate discrimination, calibration, prediction error,\n    and cumulative incidence. None alone establishes decision or clinical utility.\n\n## Outcome construction\n\n```python\nfrom sksurv.util import Surv\n\ny = Surv.from_arrays(event=event_bool, time=observed_time)\n# Equivalent for pandas or Polars:\ny = Surv.from_dataframe(\"event\", \"time\", frame)\n```\n\nThe first field is boolean (`True`=event, `False`=right-censored); the second is\nfloating-point time. Field names may vary, but field order and meaning may not.\nUse `references\u002Fdata-handling.md` before loading custom or competing-risk data.\n\n## Leakage-safe pipeline\n\n```python\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\nfrom sksurv.linear_model import CoxPHSurvivalAnalysis\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, test_size=0.25, stratify=y[\"event\"], random_state=20260723\n)\n\npreprocess = ColumnTransformer(\n    [\n        (\"num\", make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler()), numeric),\n        (\n            \"cat\",\n            make_pipeline(\n                SimpleImputer(strategy=\"most_frequent\"),\n                OneHotEncoder(handle_unknown=\"ignore\", drop=\"first\", sparse_output=False),\n            ),\n            categorical,\n        ),\n    ],\n    sparse_threshold=0.0,\n)\nmodel = make_pipeline(preprocess, CoxPHSurvivalAnalysis(alpha=0.1, ties=\"efron\"))\nmodel.fit(X_train, y_train)\nrisk = model.predict(X_test)\n```\n\nThe split precedes every learned transformation. For repeated or grouped records,\nuse a group-aware split; for temporal deployment, use a time-respecting split.\n\n## Model choice\n\n- `CoxPHSurvivalAnalysis`: interpretable log-hazard coefficients under proportional\n  hazards; `alpha` is ridge shrinkage and `ties` is `\"breslow\"` or `\"efron\"`.\n- `CoxnetSurvivalAnalysis`: LASSO\u002Felastic-net path for high-dimensional data.\n  `l1_ratio` is in `(0, 1]`; use `fit_baseline_model=True` before requesting\n  survival or cumulative-hazard functions.\n- `IPCRidge`: IPC-weighted ridge AFT model; prediction is on a time\u002Flog-time scale,\n  not a Cox risk score.\n- `RandomSurvivalForest` \u002F `ExtraSurvivalTrees`: nonlinear survival and cumulative\n  hazard predictions; use permutation importance, not impurity importance.\n- `GradientBoostingSurvivalAnalysis`: tree boosting with `\"coxph\"`, `\"squared\"`,\n  or `\"ipcwls\"` loss. `criterion` was removed in 0.28.\n- `ComponentwiseGradientBoostingSurvivalAnalysis`: sparse linear componentwise\n  boosting.\n- `FastSurvivalSVM` \u002F `FastKernelSurvivalSVM`: ranking or regression objectives.\n  Only `rank_ratio=1` directly returns higher-is-riskier scores; SVMs do not yield\n  survival probabilities for Brier metrics.\n\nRead the model-specific reference before interpreting coefficients or predictions:\n`references\u002Fcox-models.md`, `references\u002Fensemble-models.md`, or\n`references\u002Fsvm-models.md`.\n\n## Prediction and metric contracts\n\n```python\nimport numpy as np\nfrom sksurv.metrics import (\n    brier_score,\n    concordance_index_ipcw,\n    cumulative_dynamic_auc,\n    integrated_brier_score,\n)\n\nrisk = model.predict(X_test)  # (n_test,), higher means higher event risk\nuno_c = concordance_index_ipcw(y_train, y_test, risk, tau=times[-1])[0]\nauc_t, mean_auc = cumulative_dynamic_auc(y_train, y_test, risk, times)\n\nsurv_fns = model.predict_survival_function(X_test)\nsurv_prob = np.vstack([fn(times) for fn in surv_fns])  # (n_test, n_times)\n_, brier_t = brier_score(y_train, y_test, surv_prob, times)\nibs = integrated_brier_score(y_train, y_test, surv_prob, times)\n```\n\n- Harrell C and Uno C measure rank discrimination, not calibration.\n- Cumulative\u002Fdynamic AUC measures discrimination at selected horizons and accepts\n  1D or time-dependent 2D risk scores; it rejects survival probabilities.\n- Brier score is censoring-weighted probability error and reflects both\n  discrimination and calibration. It is not a standalone calibration curve.\n- Calibration requires horizon-specific predicted-versus-observed checks on\n  independent data. scikit-survival 0.28 has no dedicated calibration-curve API.\n\nSee `references\u002Fevaluation-metrics.md` for assumptions, primary literature, safe\ntime-grid construction, and scorer wrappers.\n\n## Pipelines, metadata routing, and tuning\n\nOrdinary `Pipeline.fit(X, y)` needs no metadata-routing setup. Metric wrappers such\nas `as_concordance_index_ipcw_scorer` are estimator wrappers, not `scoring=`\ncallables:\n\n```python\nfrom sklearn.model_selection import GridSearchCV\nfrom sksurv.metrics import as_concordance_index_ipcw_scorer\n\nwrapped = as_concordance_index_ipcw_scorer(model, tau=tau)\nsearch = GridSearchCV(\n    wrapped,\n    {\"estimator__coxphsurvivalanalysis__alpha\": [0.01, 0.1, 1.0]},\n    cv=inner_splits,\n)\n```\n\nThe wrapper learns the censoring distribution from each fit fold. Prefix wrapped\nparameters with `estimator__`. Enable scikit-learn metadata routing only when\npassing extra metadata through a meta-estimator. For example, Coxnet's\n`set_predict_request(alpha=True)` matters only when routing the `alpha` prediction\nargument with `sklearn.set_config(enable_metadata_routing=True)`.\n\nUse an outer CV loop for an unbiased CV performance estimate after inner tuning.\nDo not select parameters and report performance from the same folds as if external.\n\n## Competing risks\n\n```python\nfrom sksurv.nonparametric import cumulative_incidence_competing_risks\n\n# status: integer array, 0=censored, 1..K=mutually exclusive causes\ntime, cif = cumulative_incidence_competing_risks(status, observed_time)\ntotal_cif = cif[0]\ncause_1_cif = cif[1]\n```\n\n`cif` has shape `(K + 1, n_times)`; row 0 is total risk and rows 1..K are\ncause-specific cumulative incidence. Cause-specific Cox models treat other causes\nas censored to estimate cause-specific hazards, but one such model's\n`1 - survival` is not the cause-specific CIF. See `references\u002Fcompeting-risks.md`.\n\n## Bundled local CLIs\n\nAll helpers use deterministic synthetic data when no input is given. They make no\nnetwork calls, reject URLs and symlinks, bound files\u002Frows\u002Ffeatures, avoid unsafe\npickle loading, and lazily import scientific packages.\n\n```bash\npython skills\u002Fscikit-survival\u002Fscripts\u002Fvalidate_survival_csv.py --help\npython skills\u002Fscikit-survival\u002Fscripts\u002Ftrain_survival_model.py --help\npython skills\u002Fscikit-survival\u002Fscripts\u002Fevaluate_survival_metrics.py --help\npython skills\u002Fscikit-survival\u002Fscripts\u002Fcompeting_risk_cif.py --help\npython skills\u002Fscikit-survival\u002Fscripts\u002Fmodel_report.py --help\n```\n\nTypical local flow:\n\n```bash\npython skills\u002Fscikit-survival\u002Fscripts\u002Fvalidate_survival_csv.py \\\n  --input data.csv --event-column event --time-column time \\\n  --feature-columns age,group,measurement --structured-output outcome.npy\n\npython skills\u002Fscikit-survival\u002Fscripts\u002Ftrain_survival_model.py \\\n  --input data.csv --event-column event --time-column time \\\n  --numeric-columns age,measurement --categorical-columns group \\\n  --model coxph --tune --prediction-output predictions.npz \\\n  --output training-summary.json\n\npython skills\u002Fscikit-survival\u002Fscripts\u002Fevaluate_survival_metrics.py \\\n  --input predictions.npz --output metrics-summary.json\n\npython skills\u002Fscikit-survival\u002Fscripts\u002Fmodel_report.py \\\n  --training-summary training-summary.json \\\n  --metrics-summary metrics-summary.json --output model-report.md\n```\n\nUse only de-identified, authorized local data. The bundled tests contain synthetic\nrecords only and no patient data or PHI.\n\n## Security triage\n\n`SECURITY.md` previously claimed this skill bundled package-shadowing files named\n`sklearn.py` and `sksurv.py`. The 2026-07-23 inventory confirmed those files did\nnot exist; the claim was a phantom analyzer finding. This refresh adds only\ndescriptively named helpers and no shadow modules, environment reads, or network\ncalls.\n\nNever name a project script after an imported package (including `sklearn.py`,\n`sksurv.py`, `numpy.py`, or `pandas.py`), because Python may import the local file\ninstead of the installed library. Inspect the working directory before executing\nexamples copied from untrusted sources.\n\n## Reference files\n\n- `references\u002Fdata-handling.md` — structured arrays, datasets, schema validation,\n  pandas\u002FPolars preprocessing, and leakage-safe splitting.\n- `references\u002Fcox-models.md` — Cox PH, Coxnet, IPCRidge, assumptions, and tuning.\n- `references\u002Fensemble-models.md` — forests, trees, boosting, predictions, and\n  permutation importance.\n- `references\u002Fsvm-models.md` — SVM objectives, prediction direction, scaling,\n  kernels, and limitations.\n- `references\u002Fevaluation-metrics.md` — metric inputs, censoring assumptions,\n  time grids, calibration, nested CV, and primary literature.\n- `references\u002Fcompeting-risks.md` — integer event coding, CIF API, built-in\n  datasets, cause-specific hazards, and unsupported Fine-Gray regression.\n\n## Dated sources\n\nOfficial API and compatibility sources, checked 2026-07-23:\n\n- [PyPI 0.28.0](https:\u002F\u002Fpypi.org\u002Fproject\u002Fscikit-survival\u002F) — released 2026-07-05.\n- [GitHub v0.28.0 release](https:\u002F\u002Fgithub.com\u002Fsebp\u002Fscikit-survival\u002Freleases\u002Ftag\u002Fv0.28.0)\n  — published 2026-07-05.\n- [0.28 release notes](https:\u002F\u002Fscikit-survival.readthedocs.io\u002Fen\u002Fstable\u002Frelease_notes\u002Fv0.28.html).\n- [Installation guide](https:\u002F\u002Fscikit-survival.readthedocs.io\u002Fen\u002Fstable\u002Finstall.html).\n- [Stable user guide](https:\u002F\u002Fscikit-survival.readthedocs.io\u002Fen\u002Fstable\u002Fuser_guide\u002Findex.html).\n- [Stable API reference](https:\u002F\u002Fscikit-survival.readthedocs.io\u002Fen\u002Fstable\u002Fapi\u002Findex.html).\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm the Python environment and library versions (`python -c \"import pkg; print(pkg.__version__)\"`) and inspect the data's shape, types and missing values.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Run on a sample or a single fold first and check runtime and memory.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Use held-out data, fixed random seeds and appropriate metrics; check for leakage; report uncertainty (CIs, std over seeds).\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| Out-of-memory or very slow execution | Subsample, use chunked or lazy computation, or reduce model size, and tell the user what changed. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Never report a metric you did not compute in this session; show the code path that produced every number.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n","# scikit-survival\n\n## Scope\n\nUse this skill for scikit-survival 0.28.0 workflows involving:\n\n- right-censored structured outcomes;\n- Cox PH, Coxnet, IPC ridge, survival trees, forests, boosting, and SVMs;\n- discrimination, prediction error, calibration-oriented checks, and time-dependent prediction;\n- nonparametric cumulative incidence with competing risks;\n- scikit-learn pipelines, nested model selection, and reproducible reports.\n\nscikit-survival primarily models right-censored outcomes. Its built-in competing-risk\nsupport is nonparametric cumulative incidence; it does not provide Fine-Gray regression.\nDo not present model output as clinical advice, causal evidence, or proof of clinical\nutility.\n\n## Current release and installation\n\nVerified 2026-07-23:\n\n- Latest stable: **scikit-survival 0.28.0**, released 2026-07-05.\n- Python: **3.11 or later**; PyPI wheels cover CPython 3.11-3.14 on Linux\n  x86-64, macOS x86-64\u002FARM64, and Windows x86-64.\n- Runtime bounds: NumPy >=2.0.0, pandas >=2.2.0, SciPy >=1.13.0,\n  scikit-learn >=1.9.0,\u003C1.10, OSQP >=1.0.2, narwhals >=2.0.1.\n- 0.28 adds pandas\u002FPolars estimator support through narwhals and removes\n  `criterion` from `GradientBoostingSurvivalAnalysis`.\n\nCreate an isolated environment and install the tested snapshot:\n\n```bash\nuv venv --python 3.11\nsource .venv\u002Fbin\u002Factivate\nuv pip install \\\n  \"scikit-survival==0.28.0\" \\\n  \"scikit-learn==1.9.0\" \\\n  \"numpy==2.4.6\" \\\n  \"pandas==3.0.5\" \\\n  \"scipy==1.17.1\" \\\n  \"ecos==2.0.14\" \\\n  \"osqp==1.1.3\" \\\n  \"joblib==1.5.3\" \\\n  \"numexpr==2.14.2\" \\\n  \"narwhals==2.24.0\"\n```\n\nBinary wheels are preferred. A source build requires a C\u002FC++ compiler; OSQP may\nalso require CMake. This skill is MIT-licensed; the upstream scikit-survival package\nis GPL-3.0-or-later, so review upstream licensing before redistribution.\n\n## Non-negotiable workflow\n\n1. **Define the estimand and event coding.** Decide whether the target is\n   all-event survival, cause-specific hazard, or cause-specific cumulative incidence.\n2. **Validate outcomes.** Standard estimators need a two-field structured array:\n   boolean event first, observed time second. Competing-risk CIF instead needs a\n   separate integer event vector: 0=censored, 1..K=causes.\n3. **Split before learned preprocessing.** Never fit imputers, encoders, scalers,\n   feature selectors, or alpha choices on all rows before splitting.\n4. **Fit preprocessing inside a pipeline.** Unknown categories and missingness must\n   be handled using training-fold state only.\n5. **Tune without reusing evaluation data.** Use nested CV when reporting\n   cross-validated tuned performance, or reserve a truly untouched final holdout.\n6. **Fit censoring distributions on training data.** IPCW concordance, dynamic AUC,\n   and Brier metrics receive `survival_train`, never a pooled train+test outcome.\n7. **Restrict evaluation times.** Use a strictly increasing grid inside test\n   follow-up and below the end of training support where the estimated censoring\n   survival remains positive.\n8. **Match predictions to metrics.** Concordance\u002Fdynamic AUC consume higher-is-riskier\n   scores. Brier metrics consume survival probabilities with shape\n   `(n_test, n_times)`, not risk scores or unevaluated step functions.\n9. **Handle competing causes explicitly.** Standard survival probabilities and CIFs\n   answer different questions. Never estimate event-specific probability with\n   `1 - Kaplan-Meier` while censoring competing events.\n10. **Report limits.** Separate discrimination, calibration, prediction error,\n    and cumulative incidence. None alone establishes decision or clinical utility.\n\n## Outcome construction\n\n```python\nfrom sksurv.util import Surv\n\ny = Surv.from_arrays(event=event_bool, time=observed_time)\n# Equivalent for pandas or Polars:\ny = Surv.from_dataframe(\"event\", \"time\", frame)\n```\n\nThe first field is boolean (`True`=event, `False`=right-censored); the second is\nfloating-point time. Field names may vary, but field order and meaning may not.\nUse `references\u002Fdata-handling.md` before loading custom or competing-risk data.\n\n## Leakage-safe pipeline\n\n```python\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\nfrom sksurv.linear_model import CoxPHSurvivalAnalysis\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, test_size=0.25, stratify=y[\"event\"], random_state=20260723\n)\n\npreprocess = ColumnTransformer(\n    [\n        (\"num\", make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler()), numeric),\n        (\n            \"cat\",\n            make_pipeline(\n                SimpleImputer(strategy=\"most_frequent\"),\n                OneHotEncoder(handle_unknown=\"ignore\", drop=\"first\", sparse_output=False),\n            ),\n            categorical,\n        ),\n    ],\n    sparse_threshold=0.0,\n)\nmodel = make_pipeline(preprocess, CoxPHSurvivalAnalysis(alpha=0.1, ties=\"efron\"))\nmodel.fit(X_train, y_train)\nrisk = model.predict(X_test)\n```\n\nThe split precedes every learned transformation. For repeated or grouped records,\nuse a group-aware split; for temporal deployment, use a time-respecting split.\n\n## Model choice\n\n- `CoxPHSurvivalAnalysis`: interpretable log-hazard coefficients under proportional\n  hazards; `alpha` is ridge shrinkage and `ties` is `\"breslow\"` or `\"efron\"`.\n- `CoxnetSurvivalAnalysis`: LASSO\u002Felastic-net path for high-dimensional data.\n  `l1_ratio` is in `(0, 1]`; use `fit_baseline_model=True` before requesting\n  survival or cumulative-hazard functions.\n- `IPCRidge`: IPC-weighted ridge AFT model; prediction is on a time\u002Flog-time scale,\n  not a Cox risk score.\n- `RandomSurvivalForest` \u002F `ExtraSurvivalTrees`: nonlinear survival and cumulative\n  hazard predictions; use permutation importance, not impurity importance.\n- `GradientBoostingSurvivalAnalysis`: tree boosting with `\"coxph\"`, `\"squared\"`,\n  or `\"ipcwls\"` loss. `criterion` was removed in 0.28.\n- `ComponentwiseGradientBoostingSurvivalAnalysis`: sparse linear componentwise\n  boosting.\n- `FastSurvivalSVM` \u002F `FastKernelSurvivalSVM`: ranking or regression objectives.\n  Only `rank_ratio=1` directly returns higher-is-riskier scores; SVMs do not yield\n  survival probabilities for Brier metrics.\n\nRead the model-specific reference before interpreting coefficients or predictions:\n`references\u002Fcox-models.md`, `references\u002Fensemble-models.md`, or\n`references\u002Fsvm-models.md`.\n\n## Prediction and metric contracts\n\n```python\nimport numpy as np\nfrom sksurv.metrics import (\n    brier_score,\n    concordance_index_ipcw,\n    cumulative_dynamic_auc,\n    integrated_brier_score,\n)\n\nrisk = model.predict(X_test)  # (n_test,), higher means higher event risk\nuno_c = concordance_index_ipcw(y_train, y_test, risk, tau=times[-1])[0]\nauc_t, mean_auc = cumulative_dynamic_auc(y_train, y_test, risk, times)\n\nsurv_fns = model.predict_survival_function(X_test)\nsurv_prob = np.vstack([fn(times) for fn in surv_fns])  # (n_test, n_times)\n_, brier_t = brier_score(y_train, y_test, surv_prob, times)\nibs = integrated_brier_score(y_train, y_test, surv_prob, times)\n```\n\n- Harrell C and Uno C measure rank discrimination, not calibration.\n- Cumulative\u002Fdynamic AUC measures discrimination at selected horizons and accepts\n  1D or time-dependent 2D risk scores; it rejects survival probabilities.\n- Brier score is censoring-weighted probability error and reflects both\n  discrimination and calibration. It is not a standalone calibration curve.\n- Calibration requires horizon-specific predicted-versus-observed checks on\n  independent data. scikit-survival 0.28 has no dedicated calibration-curve API.\n\nSee `references\u002Fevaluation-metrics.md` for assumptions, primary literature, safe\ntime-grid construction, and scorer wrappers.\n\n## Pipelines, metadata routing, and tuning\n\nOrdinary `Pipeline.fit(X, y)` needs no metadata-routing setup. Metric wrappers such\nas `as_concordance_index_ipcw_scorer` are estimator wrappers, not `scoring=`\ncallables:\n\n```python\nfrom sklearn.model_selection import GridSearchCV\nfrom sksurv.metrics import as_concordance_index_ipcw_scorer\n\nwrapped = as_concordance_index_ipcw_scorer(model, tau=tau)\nsearch = GridSearchCV(\n    wrapped,\n    {\"estimator__coxphsurvivalanalysis__alpha\": [0.01, 0.1, 1.0]},\n    cv=inner_splits,\n)\n```\n\nThe wrapper learns the censoring distribution from each fit fold. Prefix wrapped\nparameters with `estimator__`. Enable scikit-learn metadata routing only when\npassing extra metadata through a meta-estimator. For example, Coxnet's\n`set_predict_request(alpha=True)` matters only when routing the `alpha` prediction\nargument with `sklearn.set_config(enable_metadata_routing=True)`.\n\nUse an outer CV loop for an unbiased CV performance estimate after inner tuning.\nDo not select parameters and report performance from the same folds as if external.\n\n## Competing risks\n\n```python\nfrom sksurv.nonparametric import cumulative_incidence_competing_risks\n\n# status: integer array, 0=censored, 1..K=mutually exclusive causes\ntime, cif = cumulative_incidence_competing_risks(status, observed_time)\ntotal_cif = cif[0]\ncause_1_cif = cif[1]\n```\n\n`cif` has shape `(K + 1, n_times)`; row 0 is total risk and rows 1..K are\ncause-specific cumulative incidence. Cause-specific Cox models treat other causes\nas censored to estimate cause-specific hazards, but one such model's\n`1 - survival` is not the cause-specific CIF. See `references\u002Fcompeting-risks.md`.\n\n## Bundled local CLIs\n\nAll helpers use deterministic synthetic data when no input is given. They make no\nnetwork calls, reject URLs and symlinks, bound files\u002Frows\u002Ffeatures, avoid unsafe\npickle loading, and lazily import scientific packages.\n\n```bash\npython skills\u002Fscikit-survival\u002Fscripts\u002Fvalidate_survival_csv.py --help\npython skills\u002Fscikit-survival\u002Fscripts\u002Ftrain_survival_model.py --help\npython skills\u002Fscikit-survival\u002Fscripts\u002Fevaluate_survival_metrics.py --help\npython skills\u002Fscikit-survival\u002Fscripts\u002Fcompeting_risk_cif.py --help\npython skills\u002Fscikit-survival\u002Fscripts\u002Fmodel_report.py --help\n```\n\nTypical local flow:\n\n```bash\npython skills\u002Fscikit-survival\u002Fscripts\u002Fvalidate_survival_csv.py \\\n  --input data.csv --event-column event --time-column time \\\n  --feature-columns age,group,measurement --structured-output outcome.npy\n\npython skills\u002Fscikit-survival\u002Fscripts\u002Ftrain_survival_model.py \\\n  --input data.csv --event-column event --time-column time \\\n  --numeric-columns age,measurement --categorical-columns group \\\n  --model coxph --tune --prediction-output predictions.npz \\\n  --output training-summary.json\n\npython skills\u002Fscikit-survival\u002Fscripts\u002Fevaluate_survival_metrics.py \\\n  --input predictions.npz --output metrics-summary.json\n\npython skills\u002Fscikit-survival\u002Fscripts\u002Fmodel_report.py \\\n  --training-summary training-summary.json \\\n  --metrics-summary metrics-summary.json --output model-report.md\n```\n\nUse only de-identified, authorized local data. The bundled tests contain synthetic\nrecords only and no patient data or PHI.\n\n## Security triage\n\n`SECURITY.md` previously claimed this skill bundled package-shadowing files named\n`sklearn.py` and `sksurv.py`. The 2026-07-23 inventory confirmed those files did\nnot exist; the claim was a phantom analyzer finding. This refresh adds only\ndescriptively named helpers and no shadow modules, environment reads, or network\ncalls.\n\nNever name a project script after an imported package (including `sklearn.py`,\n`sksurv.py`, `numpy.py`, or `pandas.py`), because Python may import the local file\ninstead of the installed library. Inspect the working directory before executing\nexamples copied from untrusted sources.\n\n## Reference files\n\n- `references\u002Fdata-handling.md` — structured arrays, datasets, schema validation,\n  pandas\u002FPolars preprocessing, and leakage-safe splitting.\n- `references\u002Fcox-models.md` — Cox PH, Coxnet, IPCRidge, assumptions, and tuning.\n- `references\u002Fensemble-models.md` — forests, trees, boosting, predictions, and\n  permutation importance.\n- `references\u002Fsvm-models.md` — SVM objectives, prediction direction, scaling,\n  kernels, and limitations.\n- `references\u002Fevaluation-metrics.md` — metric inputs, censoring assumptions,\n  time grids, calibration, nested CV, and primary literature.\n- `references\u002Fcompeting-risks.md` — integer event coding, CIF API, built-in\n  datasets, cause-specific hazards, and unsupported Fine-Gray regression.\n\n## Dated sources\n\nOfficial API and compatibility sources, checked 2026-07-23:\n\n- [PyPI 0.28.0](https:\u002F\u002Fpypi.org\u002Fproject\u002Fscikit-survival\u002F) — released 2026-07-05.\n- [GitHub v0.28.0 release](https:\u002F\u002Fgithub.com\u002Fsebp\u002Fscikit-survival\u002Freleases\u002Ftag\u002Fv0.28.0)\n  — published 2026-07-05.\n- [0.28 release notes](https:\u002F\u002Fscikit-survival.readthedocs.io\u002Fen\u002Fstable\u002Frelease_notes\u002Fv0.28.html).\n- [Installation guide](https:\u002F\u002Fscikit-survival.readthedocs.io\u002Fen\u002Fstable\u002Finstall.html).\n- [Stable user guide](https:\u002F\u002Fscikit-survival.readthedocs.io\u002Fen\u002Fstable\u002Fuser_guide\u002Findex.html).\n- [Stable API reference](https:\u002F\u002Fscikit-survival.readthedocs.io\u002Fen\u002Fstable\u002Fapi\u002Findex.html).\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm the Python environment and library versions (`python -c \"import pkg; print(pkg.__version__)\"`) and inspect the data's shape, types and missing values.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Run on a sample or a single fold first and check runtime and memory.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Use held-out data, fixed random seeds and appropriate metrics; check for leakage; report uncertainty (CIs, std over seeds).\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| Out-of-memory or very slow execution | Subsample, use chunked or lazy computation, or reduce model size, and tell the user what changed. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Never report a metric you did not compute in this session; show the code path that produced every number.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n",{"name":5,"license":15,"metadata":45,"description":7,"allowed-tools":50,"compatibility":51},{"tags":46,"version":47,"category":48,"maintainer":49},"survival analysis, Cox regression, time-to-event, biostatistics, machine learning","1.2","data-science-and-ml","Kalaris Labs","Read Write Edit Bash","Requires Python 3.11+, uv, and the pinned scikit-survival 0.28.0 stack for executable examples. Bundled CLIs are local and network-free by default.","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fscikit-survival",490,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-14 02:12:02",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill scikit-survival",{"tool":68,"command":69},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill scikit-survival",{"tool":71,"command":72},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins 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to-e2e","Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":133,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":144,"publishedAt":137},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":133,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":151,"publishedAt":137},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":133,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":158,"publishedAt":40},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[159,160,161,162,163,164,165,166,167,168,169,22,170,171,172,173,174,175,176,177],"search","deja search","never","error","ready","memory","re","decide","fix","design","debugging","you","done","reviewer","io","change","cli","merge","past",{"skillNo":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":133,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":184,"publishedAt":40},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[185,186,187,173,188,189,190,191,192,193,194,195,196,197,164,198,199,200,201,202],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":204,"name":205,"nameZh":6,"description":206,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":133,"licenseFileUrl":6,"sourceHtmlUrl":207,"categories":208,"keywords":220,"publishedAt":40},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[209,214,217],{"categoryNo":210,"categoryName":211,"taxonomyType":212,"taxonomyTypeDesc":213},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":215,"categoryName":216,"taxonomyType":212,"taxonomyTypeDesc":213},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":218,"categoryName":219,"taxonomyType":212,"taxonomyTypeDesc":213},"01JBSEEDCAT000000000000010","后端与接口",[205,221,222,223,224,225,226,199,227,165,228,229,230,231,22,232,233,234,235,236],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]