Data Analysis

xgboost-analysis

Train XGBoost gradient boosting classifiers with cross-validated hyperparameter tuning and SHAP-based feature importance. Inputs: feature matrix, class labels. Outputs: trained model, SHAP summary and dependence plots, feature importance ranking, CV metrics.

88100Total Score
Core Capability
87 / 100
Functional Suitability
10 / 12
Reliability
11 / 12
Performance & Context
7 / 8
Agent Usability
14 / 16
Human Usability
7 / 8
Security
11 / 12
Maintainability
10 / 12
Agent-Specific
17 / 20
Medical Task
20 / 20 Passed
89Auto binary classification on dt_sample1
4/4
92Character-label classification on dt_sample3
4/4
84Missing target column rejection
4/4
88Frequency metric TXT export on dt_sample2
4/4
89Regression run on dt_sample1 using RIBC2 target
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
PASS
Research Veto✅ PASS — Applicable
DimensionResultDetail
Scientific IntegrityPASSNo fabricated numerical claims or invented study results were observed in any tested output.
Practice BoundariesPASSThe skill stayed within technical modeling scope and did not produce diagnostic, prescriptive, or clinical advice.
Methodological GroundPASSClassification and regression paths aligned with target types, and invalid input was rejected before analysis.
Code UsabilityPASSAll intended execution paths were runnable in this environment, and repeated seeded runs produced identical core outputs.

Core Capability87 / 100 — 8 Categories

Functional Suitability
Regression is supported in code, but the published SKILL.md does not include a regression example or regression validation command.
10 / 12
83%
Reliability
Validation and recovery hints are strong; retry guidance after successful runs is slightly implicit rather than explicit.
11 / 12
92%
Performance & Context
References are layered well; one minor point of friction is documenting an unused data/ subdirectory without explaining its role.
7 / 8
88%
Agent Usability
Instructions are clear and consistent, but a short agent-facing output contract would improve response shaping after success or failure.
14 / 16
88%
Human Usability
Trigger language is natural; forgiveness is good but still depends on exact column-name matching.
7 / 8
88%
Security
Input validation is strong; data-handling guidance could be more explicit for sensitive real-world datasets.
11 / 12
92%
Maintainability
The R implementation is modular, but the documented validation path does not cover every advertised mode, especially regression.
10 / 12
83%
Agent-Specific
Triggering and layering are strong; composability and stop-condition guidance can be tightened slightly.
17 / 20
85%
Core Capability Total87 / 100

Medical TaskExecution Average: 88.4 / 100 — Assertions: 20/20 Passed

89
Canonical
Auto binary classification on dt_sample1
4/4 ✓
92
Variant A
Character-label classification on dt_sample3
4/4 ✓
84
Edge
Missing target column rejection
4/4 ✓
88
Variant B
Frequency metric TXT export on dt_sample2
4/4 ✓
89
Stress
Regression run on dt_sample1 using RIBC2 target
4/4 ✓
89
Canonical✅ Pass
Auto binary classification on dt_sample1

Generated performance table, feature-importance table, figure, and session info exactly as documented.

Basic 36/40|Specialized 53/60|Total 89/100
✅A1Output contains the documented performance table
✅A2Output contains the documented feature-importance table
✅A3Output contains the documented PNG figure
✅A4Auto-excluded sample ID behavior is surfaced to the user
Pass rate: 4 / 4
92
Variant A✅ Pass
Character-label classification on dt_sample3

Handled wide TXT input with a positive-class override and produced all expected artifacts.

Basic 37/40|Specialized 55/60|Total 92/100
✅A1Character-label classification works with explicit positive class
✅A2Wide TXT input is parsed successfully
✅A3A ranked feature-importance table is produced
✅A4A feature-importance figure is produced
Pass rate: 4 / 4
84
Edge✅ Pass
Missing target column rejection

Graceful validation stop with a precise error and recovery hint; no partial outputs were emitted.

Basic 34/40|Specialized 50/60|Total 84/100
✅A1Missing target column is detected before training starts
✅A2The error names the offending column clearly
✅A3The failure path includes actionable recovery guidance
✅A4No partial output directory is created for the failed run
Pass rate: 4 / 4
88
Variant B✅ Pass
Frequency metric TXT export on dt_sample2

Produced TXT tables and a frequency-ranked figure with the requested custom prefix.

Basic 36/40|Specialized 52/60|Total 88/100
✅A1TXT table export is honored
✅A2The selected importance metric is honored
✅A3Custom output prefix is honored
✅A4Session metadata is written
Pass rate: 4 / 4
89
Stress✅ Pass
Regression run on dt_sample1 using RIBC2 target

Completed the regression path and emitted RMSE, MAE, R-squared, feature importance, and figure outputs.

Basic 36/40|Specialized 53/60|Total 89/100
✅A1Regression metrics are emitted
✅A2Regression output avoids fake class labels
✅A3Regression still produces a ranked feature-importance table
✅A4Regression still produces a figure artifact
Pass rate: 4 / 4
Medical Task Total88.4 / 100

Key Strengths

  • Seeded runs are deterministic: a repeated canonical run produced identical performance and feature-importance files.
  • Validation failures are explicit and actionable, with recovery hints that point users back to the right parameter or reference file.
  • The skill executed successfully across binary classification, character-label classification, TXT export, and regression paths.
  • The implementation is modular and the generated artifacts align closely with the documented command surface.