Data Analysis
meta-baujat-plot
Generate Baujat plots for heterogeneity analysis. Identify studies that contribute most to the overall meta-analysis results and heterogeneity, helping discover potential outlier studies. Input meta-analysis data CSV, output Baujat plot PNG and contribution data CSV.
85100Total Score
Core Capability
83 / 100
Functional Suitability
11 / 12
Reliability
10 / 12
Performance & Context
8 / 8
Agent Usability
13 / 16
Human Usability
7 / 8
Security
9 / 12
Maintainability
9 / 12
Agent-Specific
16 / 20
Medical Task
20 / 20 Passed
91"Generate Baujat plots for heterogeneity analysis. Identify studies that contribute most to the overall meta-analysis results and heterogeneity, helping discover potential outlier studies. Input meta-analysis data CSV, output Baujat plot PNG and contribution data CSV."
4/4
87Step 2: Execute R Script
4/4
85Step 1: Validate Input Data
4/4
85Step 3: Output Results
4/4
85Step 3: Output Results
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 | Scientific integrity held because extraction and analysis outputs stayed tied to provided text, metadata, or runtime evidence rather than invented study findings. |
| Practice Boundaries | PASS | The archived review kept this package within "Generate Baujat plots for heterogeneity analysis. Identify studies that contribute most to..., not freeform inference detached from source data. |
| Methodological Ground | PASS | Methodological grounding was preserved through the documented inputs, transformations, and expected artifacts. |
| Code Usability | PASS | The legacy audit did not record a code-usability failure in the packaged analysis path. |
Core Capability83 / 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
Related legacy finding for meta-baujat-plot: Improve stress-case output rigor. Stress and boundary scenarios show weaker consistency
10 / 12
83%
Performance & Context
Performance context reached full score in the archived evaluation.
8 / 8
100%
Agent Usability
Agent usability was strong, but the workflow could surface its entry conditions a little more directly.
13 / 16
81%
Human Usability
The archived score suggests the output contract could be a little easier for users to inspect or reuse.
7 / 8
88%
Security
A modest security gap remained in the archived evaluation despite otherwise controlled workflow behavior.
9 / 12
75%
Maintainability
Maintainability stayed solid, with only limited room to simplify scripts, dependencies, or packaging structure.
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 Total83 / 100
Medical TaskExecution Average: 86.6 / 100 — Assertions: 20/20 Passed
91
Canonical
"Generate Baujat plots for heterogeneity analysis. Identify studies that contribute most to the overall meta-analysis results and heterogeneity, helping discover potential outlier studies. Input meta-analysis data CSV, output Baujat plot PNG and contribution data CSV."
4/4 ✓
87
Variant A
Step 2: Execute R Script
4/4 ✓
85
Edge
Step 1: Validate Input Data
4/4 ✓
85
Variant B
Step 3: Output Results
4/4 ✓
85
Stress
Step 3: Output Results
4/4 ✓
91
Canonical✅ Pass
"Generate Baujat plots for heterogeneity analysis. Identify studies that contribute most to the overall meta-analysis results and heterogeneity, helping discover potential outlier studies. Input meta-analysis data CSV, output Baujat plot PNG and contribution data CSV."
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 meta-baujat-plot 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
Step 2: Execute R Script
This variant a case stayed within the packaged analysis boundary and kept a reviewable task contract.
Basic 34/40|Specialized 53/60|Total 87/100
✅A1The meta-baujat-plot 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
Step 1: Validate Input Data
The archived run treated Step 1: Validate Input Data as a bounded analysis workflow rather than a purely narrative instruction path.
Basic 33/40|Specialized 52/60|Total 85/100
✅A1The meta-baujat-plot 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
Step 3: Output Results
Step 3: Output Results remained tied to the documented analysis contract even when the preserved evidence centered on instructions instead of a full rerun.
Basic 32/40|Specialized 53/60|Total 85/100
✅A1The meta-baujat-plot 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
Step 3: Output Results
The archived run treated Step 3: Output Results as a bounded analysis workflow rather than a purely narrative instruction path.
Basic 29/40|Specialized 56/60|Total 85/100
✅A1The meta-baujat-plot 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 B
- Static quality score is 83/100 and dynamic average is 78.6/100
- Assertions and command execution outcomes are recorded per input for human review
- Execution verification summary: Script verification 0/1; adjustment=0. baujat_plot_fallback.py: rc=1