Data Analysis
clinical-data-cleaner
86100Total Score
Core Capability
88 / 100
Functional Suitability
11 / 12
Reliability
10 / 12
Performance & Context
8 / 8
Agent Usability
14 / 16
Human Usability
8 / 8
Security
10 / 12
Maintainability
10 / 12
Agent-Specific
17 / 20
Medical Task
17 / 20 Passed
100Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails
4/4
100Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format
4/4
92Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails
4/4
65Packaged executable path(s): scripts/main.py
3/4
65End-to-end case for Scope-focused workflow aligned to: Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails
2/4
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 Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing... rather than overclaiming what the records supported. |
| Methodological Ground | PASS | The legacy review kept the package aligned with its named analysis library, data structure, or processing workflow. |
| Code Usability | PASS | The archived review preserved a usable code path with named scripts, expected inputs, and a recognizable output contract. |
Core Capability88 / 100 — 8 Categories
Functional Suitability
Functional suitability was softened by the legacy issue 'Improve stress-case output rigor'. Stress and boundary scenarios show weaker consistency
11 / 12
92%
Reliability
The archived deduction in reliability traces back to: Improve stress-case output rigor. Stress and boundary scenarios show weaker consistency
10 / 12
83%
Performance & Context
The legacy audit gave full marks to performance context for this package.
8 / 8
100%
Agent Usability
The archived review left some headroom in how quickly an agent can lock onto the intended analysis path.
14 / 16
88%
Human Usability
The legacy audit gave full marks to human usability for this package.
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.
10 / 12
83%
Agent-Specific
Related legacy finding for clinical-data-cleaner: Stabilize executable path and fallback behavior. Some inputs only reached PARTIAL due to execution gaps or weak boundary handling
17 / 20
85%
Core Capability Total88 / 100
Medical TaskExecution Average: 84.4 / 100 — Assertions: 17/20 Passed
100
Canonical
Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails
4/4 ✓
100
Variant A
Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format
4/4 ✓
92
Edge
Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails
4/4 ✓
65
Variant B
Packaged executable path(s): scripts/main.py
3/4 ⚠
65
Stress
End-to-end case for Scope-focused workflow aligned to: Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails
2/4 ⚠
100
Canonical✅ Pass
Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails
The Use when cleaning clinical trial data, preparing data for FDA/EMA... scenario completed within the documented Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing... boundary.
Basic 40/40|Specialized 60/60|Total 100/100
✅A1The clinical-data-cleaner output structure covers required deliverable blocks
✅A2Script execution path is available (command exit code is 0)
✅A3The output stays within declared skill scope and target objective
✅A4Required research safety/boundary guidance is present without overclaims
Pass rate: 4 / 4
100
Variant A✅ Pass
Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format
The Use this skill for data analysis tasks that require explicit... scenario completed within the documented Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing... boundary.
Basic 40/40|Specialized 60/60|Total 100/100
✅A1The clinical-data-cleaner output structure covers required deliverable blocks
✅A2Script execution path is available (command exit code is 0)
✅A3The output stays within declared skill scope and target objective
✅A4Required research safety/boundary guidance is present without overclaims
Pass rate: 4 / 4
92
Edge✅ Pass
Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails
The Use when cleaning clinical trial data, preparing data for FDA/EMA... path verified the packaged helper command without exposing a deeper execution issue.
Basic 36/40|Specialized 56/60|Total 92/100
✅A1The clinical-data-cleaner output structure covers required deliverable blocks
✅A2Script execution path is available (command exit code is 0)
✅A3The output stays within declared skill scope and target objective
✅A4Required research safety/boundary guidance is present without overclaims
Pass rate: 4 / 4
65
Variant B⚠️ Warning
Packaged executable path(s): scripts/main.py
The preserved weakness for Packaged executable path(s): scripts/main.py was concentrated in one point: Script execution path is available (command exit code is 0).
Basic 26/40|Specialized 39/60|Total 65/100
✅A1The clinical-data-cleaner output structure covers required deliverable blocks
❌A2Script execution path is available (command exit code is 0)
✅A3The output stays within declared skill scope and target objective
✅A4Required research safety/boundary guidance is present without overclaims
Pass rate: 3 / 4
65
Stress⚠️ Warning
End-to-end case for Scope-focused workflow aligned to: Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails
The preserved weakness for End-to-end case for Scope-focused workflow aligned to: Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails was concentrated in one point: The output stays within declared skill scope and target objective.
Basic 25/40|Specialized 40/60|Total 65/100
✅A1The clinical-data-cleaner output structure covers required deliverable blocks
✅A2Script execution path is available (command exit code is 0)
❌A3The output stays within declared skill scope and target objective
❌A4Required research safety/boundary guidance is present without overclaims
Pass rate: 2 / 4
Medical Task Total84.4 / 100
Key Strengths
- Primary routing is Data Analysis with execution mode B
- Static quality score is 88/100 and dynamic average is 84.4/100
- Assertions and command execution outcomes are recorded per input for human review