# Send Survival Analysis (KM) to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "survival-analysis-km", "name": "Survival Analysis (KM)", "source": "tencent", "type": "skill", "category": "开发工具", "sourceUrl": "https://clawhub.ai/AIPOCH-AI/survival-analysis-km", "canonicalUrl": "https://clawhub.ai/AIPOCH-AI/survival-analysis-km", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/survival-analysis-km", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=survival-analysis-km", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "requirements.txt", "SKILL.md", "scripts/main.py", "references/README.md", "references/sample_data.csv" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-05-07T17:22:31.273Z", "expiresAt": "2026-05-14T17:22:31.273Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-annual-report", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-annual-report", "contentDisposition": "attachment; filename=\"afrexai-annual-report-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/survival-analysis-km" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/survival-analysis-km", "downloadUrl": "https://openagent3.xyz/downloads/survival-analysis-km", "agentUrl": "https://openagent3.xyz/skills/survival-analysis-km/agent", "manifestUrl": "https://openagent3.xyz/skills/survival-analysis-km/agent.json", "briefUrl": "https://openagent3.xyz/skills/survival-analysis-km/agent.md" } } ``` ## Documentation ### Survival Analysis (Kaplan-Meier) Kaplan-Meier survival analysis tool for clinical and biological research. Generates publication-ready survival curves with statistical tests. ### Features Kaplan-Meier Curve Generation: Publication-quality survival plots with confidence intervals Statistical Tests: Log-rank test, Wilcoxon test, Peto-Peto test Hazard Ratios: Cox proportional hazards regression with 95% CI Summary Statistics: Median survival time, restricted mean survival time (RMST) Multi-group Analysis: Supports 2+ comparison groups Risk Tables: Optional at-risk table below curves ### Python Script python scripts/main.py --input data.csv --time time_col --event event_col --group group_col --output results/ ### Arguments ArgumentDescriptionRequired--inputInput CSV file pathYes--timeColumn name for survival timeYes--eventColumn name for event indicator (1=event, 0=censored)Yes--groupColumn name for grouping variableOptional--outputOutput directory for resultsYes--conf-levelConfidence level (default: 0.95)Optional--risk-tableInclude risk table in plotOptional ### Input Format CSV with columns: Time column: Numeric, time to event or censoring Event column: Binary (1 = event occurred, 0 = censored/right-censored) Group column: Categorical variable for stratification Example: patient_id,time_months,death,treatment_group P001,24.5,1,Drug_A P002,36.2,0,Drug_A P003,18.7,1,Placebo ### Output Files km_curve.png: Kaplan-Meier survival curve km_curve.pdf: Vector version for publications survival_stats.csv: Statistical summary (median survival, confidence intervals) hazard_ratios.csv: Cox regression results with HR and 95% CI \`logrank_test.csv**: Pairwise comparison p-values \`report.txt**: Human-readable summary report ### Statistical Methods Kaplan-Meier Estimator: Non-parametric maximum likelihood estimate of survival function Product-limit estimator: Ŝ(t) = Π(tᵢ≤t) (1 - dᵢ/nᵢ) Greenwood's formula for variance estimation Log-Rank Test: Most widely used test for comparing survival curves Null hypothesis: No difference between groups Weighted by number at risk at each event time Cox Proportional Hazards: Semi-parametric regression model h(t|X) = h₀(t) × exp(β₁X₁ + β₂X₂ + ...) Proportional hazards assumption checked via Schoenfeld residuals ### Dependencies lifelines: Core survival analysis library matplotlib, seaborn: Visualization pandas, numpy: Data handling scipy: Statistical tests ### Technical Difficulty: High ⚠️ This skill involves advanced statistical modeling. Results should be reviewed by a biostatistician, especially for: Proportional hazards assumption violations Small sample sizes (< 30 per group) Heavy censoring (> 50%) Time-varying covariates ### References See references/ folder for: Kaplan EL, Meier P (1958) original paper Cox DR (1972) regression models paper Sample datasets for testing Clinical reporting guidelines (ATN, CONSORT) ### Parameters ParameterTypeDefaultDescription--inputstrRequiredInput CSV file path--timestrRequiredColumn name for survival time--eventstrRequired--groupstrRequired--outputstrRequiredOutput directory for results--conf-levelfloat0.95--risk-tablestrRequiredInclude risk table in plot--figsizestr'10--dpiint300 ### Example # Basic survival curve python scripts/main.py \\ --input clinical_data.csv \\ --time overall_survival_months \\ --event death \\ --group treatment_arm \\ --output ./results/ \\ --risk-table Output includes: Survival curves with 95% confidence bands Median survival: Drug A = 28.4 months (95% CI: 24.1-32.7), Placebo = 18.2 months (95% CI: 15.3-21.1) Log-rank test p-value: 0.0023 Hazard ratio: 0.62 (95% CI: 0.45-0.85), p = 0.003 ### Risk Assessment Risk IndicatorAssessmentLevelCode ExecutionPython/R scripts executed locallyMediumNetwork AccessNo external API callsLowFile System AccessRead input files, write output filesMediumInstruction TamperingStandard prompt guidelinesLowData ExposureOutput files saved to workspaceLow ### Security Checklist No hardcoded credentials or API keys No unauthorized file system access (../) Output does not expose sensitive information Prompt injection protections in place Input file paths validated (no ../ traversal) Output directory restricted to workspace Script execution in sandboxed environment Error messages sanitized (no stack traces exposed) Dependencies audited ### Prerequisites # Python dependencies pip install -r requirements.txt ### Success Metrics Successfully executes main functionality Output meets quality standards Handles edge cases gracefully Performance is acceptable ### Test Cases Basic Functionality: Standard input → Expected output Edge Case: Invalid input → Graceful error handling Performance: Large dataset → Acceptable processing time ### Lifecycle Status Current Stage: Draft Next Review Date: 2026-03-06 Known Issues: None Planned Improvements: Performance optimization Additional feature support ## Trust - Source: tencent - Verification: Indexed source record - Publisher: AIPOCH-AI - Version: 1.0.0 ## Source health - Status: healthy - Source download looks usable. - Yavira can redirect you to the upstream package for this source. - Health scope: source - Reason: direct_download_ok - Checked at: 2026-05-07T17:22:31.273Z - Expires at: 2026-05-14T17:22:31.273Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/survival-analysis-km) - [Send to Agent page](https://openagent3.xyz/skills/survival-analysis-km/agent) - [JSON manifest](https://openagent3.xyz/skills/survival-analysis-km/agent.json) - [Markdown brief](https://openagent3.xyz/skills/survival-analysis-km/agent.md) - [Download page](https://openagent3.xyz/downloads/survival-analysis-km)