survival-analysis — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited survival-analysis (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Provide expert-standard support for data scientists at any experience level. Cover description, association, prediction and analysis review across domains, with customer churn as a recurring example. Briefly define unfamiliar concepts in plain language, connect them to the immediate decision, then offer deeper technical detail when it helps. Prioritise the estimand, observation process, executable work and evidence over a tutorial or paperwork.
| Request | Steps that apply | Reference to load |
|---|---|---|
| Define a cohort, event or estimand | 1-2 | Design and assumptions |
| Choose or justify a method | 1, 5 | Methods and interpretation |
| Write or adapt code | 2, 5, 6 | Implementation |
| Diagnose a failing or suspect fit | 2, 6 | Implementation, methods |
| Design or repair validation | 3, 7 | Evaluation and reporting |
| Interpret or explain a result | 4, 8 | Methods and interpretation |
| Review someone else's analysis | 1-8 as evidence allows | All, as the findings require |
| Cohort too large or too slow | 2, 5, 7 | Scale and performance |
interpret or review. Do the requested slice; do not restart a settled analysis.
Ask only questions whose answers change the next step. Never invent event definitions, censoring rules, business thresholds or results.
material unknowns [TODO] only when recording them is useful. Explain why a material unknown blocks fitting or interpretation and what evidence resolves it. A clearly labelled synthetic example may proceed without real data.
project's libraries, versions and environment manager where suitable. Confirm API support for the installed version before writing library-specific code.
on import failure; report the missing prerequisite.
aggregate diagnostics or synthetic examples over sensitive row-level output.
Confirm the population, unit of analysis, decision, event and meaningful time horizons. Distinguish describing event-free survival, estimating adjusted associations, predicting absolute risk, and estimating a causal intervention effect. A predictive model or adjusted hazard ratio alone does not identify a causal effect. Retention targeting needs evidence of intervention benefit, not only a ranking of churn risk.
Record time zero, prediction time, eligibility, time scale and units, event coding, observation end, censoring reasons, competing events, delayed entry, repeated episodes and feature-availability timestamps. Ask whether churn is cancellation, non-renewal or a confirmed inactivity rule, and whether reactivation is possible. Do not fit while these definitions materially change the target or risk sets.
Reconcile cohort counts and exclusions. Check IDs, event-code meanings, missing outcomes, non-finite or negative durations, zero-time events, timestamp order, duplicates, time units and the last date at which events can genuinely be observed. Never turn a missing event flag into censoring or drop censored rows by default.
Use the correct schema: duration/event for ordinary right censoring; entry/exit for delayed entry; lower/upper bounds for interval censoring; event types for competing risks; ID/start/stop/event for changing covariates or repeated episodes. Check non-overlapping, positive-length intervals and events at the correct stops. Do not force unsupported observation schemes into a two-column right-censored API.
Examine censoring by reason, cohort and predictors. Independent censoring is an assumption, not something a diagnostic can certify. Distinguish assumptions for unadjusted Kaplan-Meier from those conditional on covariates in a regression model. Escalate plausible informative dropout with sensitivity analyses and limitations.
For prediction, agree training, tuning and held-out evaluation populations and calendar cut-offs before fitting. Match validation to the intended deployment. Keep dependent entities and episodes together unless an explicit existing-customer backtest justifies otherwise; preserve temporal order and label availability. Fit preprocessing, feature selection and tuning only within training folds.
At prediction time, use only information already available, including ingestion and reporting delays. For time-varying analysis, covariates must be available at each interval start. Do not use final tenure, later cancellations or future behaviour as predictors. A later training-label freeze can leak into an earlier deployment backtest even when feature timestamps are correct.
For an association study, use a justified inferential design, diagnostics and sensitivity analyses; do not impose a predictive train/test split automatically.
Report subjects, events by type, censoring, follow-up and numbers at risk. Use Kaplan-Meier with confidence intervals and a risk table for suitable single-event data; use cumulative incidence for cause-specific probabilities with competing events. Do not call one minus cause-specific Kaplan-Meier an observed event risk.
Report the median only if the curve reaches 0.5; otherwise say "not reached within follow-up". Consider restricted mean survival over an agreed supported horizon. An unadjusted log-rank comparison is neither an effect size nor a causal result.
Start with a transparent baseline compatible with the estimand and observation scheme, often Cox PH for baseline covariates and ordinary right censoring. Consider penalisation and nonlinear terms where justified. Compare a limited set of alternatives rather than conducting an unbounded algorithm search.
Route competing probabilities to cumulative-incidence methods; changing covariates to start-stop methods or a justified landmark design; non-PH effects to time interactions, stratification or suitable alternatives; time ratios to AFT; repeated events to an explicit recurrent-event or multi-state design. Consider survival forests, boosting or discrete-time models when prediction warrants them. Do not assume a Weibull AFT model resolves PH violations: its usual form is also PH.
When cohort size, expanded row count or runtime constrains the choice, size the problem before abandoning a method. Treat any sampling design as an explicit observation and estimator choice: report the inclusion rule, compatible estimator and variance procedure rather than applying it as a silent shortcut.
For Cox models, combine scaled Schoenfeld diagnostics and plots with effect size, functional form and domain knowledge. Non-significance is not proof of PH. Check convergence, separation, collinearity, sparse events, influential observations and dependence. Do not silence warnings or use penalisation to hide a broken cohort.
Explain how each material violation changes the conclusion or model. Stratifying on a variable does not estimate its hazard ratio. Time-varying covariates do not automatically create time-varying coefficients. Extrapolation needs explicit distributional assumptions, sensitivity analysis and a labelled extrapolated range.
For prediction, assess held-out discrimination, censoring-aware probability error and calibration at agreed horizons, with uncertainty and a simple baseline. Name the concordance estimator, AUC definition, time grid, censoring estimator and integration interval. A high C-index is not evidence of calibrated probabilities.
Verify follow-up and censoring support before IPCW metrics. Do not silently remove long-followed subjects, clip their outcomes or change horizons to make a metric run. Use competing-risk metrics for a competing-risk estimand. Review subgroup and calendar-period performance where sample support permits; report unsupported horizons and unstable estimates instead of inventing thresholds or precision.
Distinguish survival probability, event probability, hazard ratio, subdistribution hazard ratio and time ratio. State units, reference categories, conditioning, uncertainty and limitations. Keep association, prediction and causation separate.
For work that will be rerun, retain the cohort/query version, extraction and label cut-offs, split membership, feature specification, seeds, package versions, transformations and model settings in the project's existing artefact. Provide reproducible code plus evidence appropriate to the requested slice. For prediction use, state scoring time, eligible population, feature freshness and outcome maturation so the analysis can be reproduced and evaluated over time.
Return the decision or finding first, then only the supporting evidence relevant to the request:
For reviews, lead with severity-ranked findings tied to the supplied code or analysis, consequences and suggested corrections. Never claim that documentation review, code generation or a synthetic smoke test validates a real-world model.
Load only the resources needed for the current task. Prefer the project's existing notebook, code comments, analysis document or issue context. Use a template only when it helps resolve material decisions or the user asks for one; copy only the relevant sections and omit the rest without justification.
observation schemas, churn definitions, leakage and informative censoring.
selection, equations, diagnostics, advanced designs and interpretation traps.
design, metric contracts, calibration, uncertainty and evidence by task.
size, expanded row count or runtime constrains the work. Single-workstation choices, design-specific sampling and warehouse escalation.
checks, troubleshooting, Python usage and sources, plus a secondary R example that is not execution-tested. Read before running the bundled script.
example with input guards, Cox diagnostics, held-out metrics and calibration. Requires the documented dependencies; it never installs or accesses real data.
estimand, cohort, leakage, assumption and validation decisions.
diagnostics, validation limits and reproducibility evidence.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.