Data Analysis
scikit-survival
A comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival; use it when you need to model censored time-to-event outcomes, fit Cox/RSF/GB models or Survival SVMs, evaluate with C-index/Brier score, or handle competing risks.
86100Total Score
Core Capability
84 / 100
Functional Suitability
11 / 12
Reliability
9 / 12
Performance & Context
7 / 8
Agent Usability
14 / 16
Human Usability
8 / 8
Security
10 / 12
Maintainability
9 / 12
Agent-Specific
16 / 20
Medical Task
20 / 20 Passed
91A comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival; use it when you need to model censored time-to-event outcomes, fit Cox/RSF/GB models or Survival SVMs, evaluate with C-index/Brier score, or handle competing risks
4/4
87Survival target construction via sksurv.util.Surv (arrays or DataFrame)
4/4
85Survival target construction via sksurv.util.Surv (arrays or DataFrame)
4/4
85Model families
4/4
85End-to-end case for Survival target construction via sksurv.util.Surv (arrays or DataFrame)
4/4
Veto GatesRequired pass for any deployment consideration
Skill Veto✓ All 4 gates passed
✓
Operational Stability
System remains stable across varied inputs and edge cases
PASS✓
Structural Consistency
Output structure conforms to expected skill contract format
PASS✓
Result Determinism
Equivalent inputs produce semantically equivalent outputs
PASS✓
System Security
No prompt injection, data leakage, or unsafe tool use detected
PASSResearch Veto✅ PASS — Applicable
| Dimension | Result | Detail |
|---|---|---|
| Scientific Integrity | PASS | The archived review kept this workflow anchored to supplied data fields and observable execution behavior, not fabricated results. |
| Practice Boundaries | PASS | Practice boundaries held because the package remained focused on A comprehensive toolkit for survival analysis and time-to-event modeling in Python using... rather than overclaiming what the records supported. |
| Methodological Ground | PASS | Methodological grounding was preserved through the documented inputs, transformations, and expected artifacts. |
| Code Usability | PASS | The archived review preserved a usable code path with named scripts, expected inputs, and a recognizable output contract. |
Core Capability84 / 100 — 8 Categories
Functional Suitability
The archived deduction in functional suitability traces back to: Improve stress-case output rigor. Stress and boundary scenarios show weaker consistency
11 / 12
92%
Reliability
Related legacy finding for scikit-survival: Improve stress-case output rigor. Stress and boundary scenarios show weaker consistency
9 / 12
75%
Performance & Context
The archived review left minor headroom in how this analysis workflow scales across heavier contexts.
7 / 8
88%
Agent Usability
The packaged analysis path is understandable, though the archived score suggests slightly clearer routing would help.
14 / 16
88%
Human Usability
No point loss was recorded for human usability in the legacy audit.
8 / 8
100%
Security
The packaged workflow stayed safe overall, with only a small remaining deduction around boundary signaling.
10 / 12
83%
Maintainability
The archived review treated the package as maintainable, while still preserving some room for cleanup.
9 / 12
75%
Agent-Specific
Agent specific was softened by the legacy issue 'Improve stress-case output rigor'. Stress and boundary scenarios show weaker consistency
16 / 20
80%
Core Capability Total84 / 100
Medical TaskExecution Average: 86.6 / 100 — Assertions: 20/20 Passed
91
Canonical
A comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival; use it when you need to model censored time-to-event outcomes, fit Cox/RSF/GB models or Survival SVMs, evaluate with C-index/Brier score, or handle competing risks
4/4 ✓
87
Variant A
Survival target construction via sksurv.util.Surv (arrays or DataFrame)
4/4 ✓
85
Edge
Survival target construction via sksurv.util.Surv (arrays or DataFrame)
4/4 ✓
85
Variant B
Model families
4/4 ✓
85
Stress
End-to-end case for Survival target construction via sksurv.util.Surv (arrays or DataFrame)
4/4 ✓
91
Canonical✅ Pass
A comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival; use it when you need to model censored time-to-event outcomes, fit Cox/RSF/GB models or Survival SVMs, evaluate with C-index/Brier score, or handle competing risks
This canonical case stayed within the packaged analysis boundary and kept a reviewable task contract.
Basic 36/40|Specialized 55/60|Total 91/100
✅A1The scikit-survival output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
87
Variant A✅ Pass
Survival target construction via sksurv.util.Surv (arrays or DataFrame)
Survival target construction via sksurv.util.Surv (arrays or DataFrame) remained tied to the documented analysis contract even when the preserved evidence centered on instructions instead of a full rerun.
Basic 34/40|Specialized 53/60|Total 87/100
✅A1The scikit-survival output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
85
Edge✅ Pass
Survival target construction via sksurv.util.Surv (arrays or DataFrame)
Survival target construction via sksurv.util.Surv (arrays or DataFrame) remained tied to the documented analysis contract even when the preserved evidence centered on instructions instead of a full rerun.
Basic 33/40|Specialized 52/60|Total 85/100
✅A1The scikit-survival output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
85
Variant B✅ Pass
Model families
The archived run treated Model families as a bounded analysis workflow rather than a purely narrative instruction path.
Basic 32/40|Specialized 53/60|Total 85/100
✅A1The scikit-survival output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
85
Stress✅ Pass
End-to-end case for Survival target construction via sksurv.util.Surv (arrays or DataFrame)
This stress case stayed within the packaged analysis boundary and kept a reviewable task contract.
Basic 29/40|Specialized 56/60|Total 85/100
✅A1The scikit-survival output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
Medical Task Total86.6 / 100
Key Strengths
- Primary routing is Data Analysis with execution mode A
- Static quality score is 84/100 and dynamic average is 78.6/100
- Assertions and command execution outcomes are recorded per input for human review
- Execution verification summary: No script verification was applicable