Data Analysis
differential-expression-analysis
Perform differential expression analysis between two or more groups using limma, DESeq2, or edgeR. Inputs: count or normalized expression matrix, group labels. Outputs: DEG result table, volcano plot, heatmap, session metadata.
90100Total Score
Core Capability
91 / 100
Functional Suitability
11 / 12
Reliability
11 / 12
Performance & Context
7 / 8
Agent Usability
15 / 16
Human Usability
7 / 8
Security
11 / 12
Maintainability
11 / 12
Agent-Specific
18 / 20
Medical Task
25 / 25 Passed
93limma differential expression smoke test
5/5
91CLI help and option contract
5/5
88sample matching and group validation review
5/5
90visualization artifact workflow
5/5
88multi-method workflow coverage review
5/5
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 | No fabricated DOI, PMID, trial result, sample size, p-value, or unsupported scientific claim was generated during audit. |
| Practice Boundaries | PASS | The skill performs computational data analysis and does not make diagnostic or treatment recommendations. |
| Methodological Ground | PASS | The workflow uses standard data-analysis methods and documents assumptions, thresholds, and output interpretation boundaries. |
| Code Usability | PASS | Native CLI execution was verified using /opt/homebrew/bin/Rscript in this environment. |
Core Capability91 / 100 — 8 Categories
Functional Suitability
The skill provides a complete Data Analysis workflow with documented inputs, outputs, examples, and deterministic artifacts.
11 / 12
92%
Reliability
Native execution was verified with /opt/homebrew/bin/Rscript; remaining risks are limited to environment dependency drift.
11 / 12
92%
Performance & Context
Runtime is appropriate for bundled smoke-test data and the workflow writes concise tabular and plot artifacts.
7 / 8
88%
Agent Usability
CLI usage, parameters, output paths, and troubleshooting guidance are sufficiently clear for agent invocation.
15 / 16
94%
Human Usability
Examples and reference documentation make the workflow discoverable and reproducible for human reviewers.
7 / 8
88%
Security
No credential handling or unsafe dynamic code execution was identified; file-path based inputs are used.
11 / 12
92%
Maintainability
Implementation is modular across scripts, references, and tests, making future updates straightforward.
11 / 12
92%
Agent-Specific
The skill has clear trigger boundaries, structured CLI execution, and reproducible output conventions.
18 / 20
90%
Core Capability Total91 / 100
Medical TaskExecution Average: 90 / 100 — Assertions: 25/25 Passed
93
Canonical
limma differential expression smoke test
5/5 ✓
91
Variant A
CLI help and option contract
5/5 ✓
88
Edge
sample matching and group validation review
5/5 ✓
90
Variant B
visualization artifact workflow
5/5 ✓
88
Stress
multi-method workflow coverage review
5/5 ✓
93
Canonical✅ Pass
limma differential expression smoke test
/opt/homebrew/bin/Rscript completed the bundled limma example, detecting 2104 significant genes and writing result artifacts.
Basic 37/40|Specialized 56/60|Total 93/100
✅A1Required output artifacts were generated or documented for this test input.
✅A2Input validation and documented parameter handling were consistent with the skill scope.
✅A3No fabricated biomedical claims or unsupported clinical conclusions were generated.
✅A4Execution stayed within the Data Analysis workflow boundaries.
✅A5Results were reproducible enough for audit review.
Pass rate: 5 / 5
91
Variant A✅ Pass
CLI help and option contract
The documented CLI help rendered successfully with required input, group, output, method, threshold, and seed options.
Basic 36/40|Specialized 55/60|Total 91/100
✅A1Required output artifacts were generated or documented for this test input.
✅A2Input validation and documented parameter handling were consistent with the skill scope.
✅A3No fabricated biomedical claims or unsupported clinical conclusions were generated.
✅A4Execution stayed within the Data Analysis workflow boundaries.
✅A5Results were reproducible enough for audit review.
Pass rate: 5 / 5
88
Edge✅ Pass
sample matching and group validation review
Script structure and references document sample/group validation and deterministic failure behavior for invalid data.
Basic 35/40|Specialized 53/60|Total 88/100
✅A1Required output artifacts were generated or documented for this test input.
✅A2Input validation and documented parameter handling were consistent with the skill scope.
✅A3No fabricated biomedical claims or unsupported clinical conclusions were generated.
✅A4Execution stayed within the Data Analysis workflow boundaries.
✅A5Results were reproducible enough for audit review.
Pass rate: 5 / 5
90
Variant B✅ Pass
visualization artifact workflow
The audited run generated downstream visualization steps including volcano plot and heatmap generation.
Basic 36/40|Specialized 54/60|Total 90/100
✅A1Required output artifacts were generated or documented for this test input.
✅A2Input validation and documented parameter handling were consistent with the skill scope.
✅A3No fabricated biomedical claims or unsupported clinical conclusions were generated.
✅A4Execution stayed within the Data Analysis workflow boundaries.
✅A5Results were reproducible enough for audit review.
Pass rate: 5 / 5
88
Stress✅ Pass
multi-method workflow coverage review
The skill documents limma, DESeq2, edgeR, t-test, and Wilcoxon routes with shared output conventions.
Basic 35/40|Specialized 53/60|Total 88/100
✅A1Required output artifacts were generated or documented for this test input.
✅A2Input validation and documented parameter handling were consistent with the skill scope.
✅A3No fabricated biomedical claims or unsupported clinical conclusions were generated.
✅A4Execution stayed within the Data Analysis workflow boundaries.
✅A5Results were reproducible enough for audit review.
Pass rate: 5 / 5
Medical Task Total90 / 100
Key Strengths
- Native R execution succeeded with the Homebrew Rscript path and bundled test data.
- The workflow covers multiple common differential-expression methods with clear CLI parameters.
- Output artifacts include result tables, filtered significant genes, visualizations, and session metadata.
- Scope boundaries are appropriate for bulk expression differential analysis.