Data Analysis
pydeseq2
Differential gene expression analysis for bulk RNA-seq count matrices using a DESeq2-like workflow in Python; use when you need Wald tests, FDR correction, and optional LFC shrinkage for condition/batch/covariate designs.
87100Total Score
Core Capability
87 / 100
Functional Suitability
11 / 12
Reliability
10 / 12
Performance & Context
8 / 8
Agent Usability
14 / 16
Human Usability
8 / 8
Security
9 / 12
Maintainability
10 / 12
Agent-Specific
17 / 20
Medical Task
20 / 20 Passed
91Differential gene expression analysis for bulk RNA-seq count matrices using a DESeq2-like workflow in Python; use when you need Wald tests, FDR correction, and optional LFC shrinkage for condition/batch/covariate designs
4/4
87Differential gene expression analysis for bulk RNA-seq count matrices using a DESeq2-like workflow in Python; use when you need Wald tests, FDR correction, and optional LFC shrinkage for condition/batch/covariate designs
4/4
86End-to-end DESeq2-like workflow: normalization (size factors), dispersion estimation/shrinkage, LFC fitting, outlier handling
4/4
86Wald tests for differential expression with Benjamini–Hochberg FDR (padj)
4/4
86End-to-end case for End-to-end DESeq2-like workflow: normalization (size factors), dispersion estimation/shrinkage, LFC fitting, outlier handling
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 | The archived review kept this package within Differential gene expression analysis for bulk RNA-seq count matrices using a DESeq2-like..., 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 archived review preserved a usable code path with named scripts, expected inputs, and a recognizable output contract. |
Core Capability87 / 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
Performance context reached full score in the archived evaluation.
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
Security remained strong, though the archived review still left some room for clearer execution guardrails.
9 / 12
75%
Maintainability
The archived review treated the package as maintainable, while still preserving some room for cleanup.
10 / 12
83%
Agent-Specific
Agent specific was softened by the legacy issue 'Improve stress-case output rigor'. Stress and boundary scenarios show weaker consistency
17 / 20
85%
Core Capability Total87 / 100
Medical TaskExecution Average: 87.2 / 100 — Assertions: 20/20 Passed
91
Canonical
Differential gene expression analysis for bulk RNA-seq count matrices using a DESeq2-like workflow in Python; use when you need Wald tests, FDR correction, and optional LFC shrinkage for condition/batch/covariate designs
4/4 ✓
87
Variant A
Differential gene expression analysis for bulk RNA-seq count matrices using a DESeq2-like workflow in Python; use when you need Wald tests, FDR correction, and optional LFC shrinkage for condition/batch/covariate designs
4/4 ✓
86
Edge
End-to-end DESeq2-like workflow: normalization (size factors), dispersion estimation/shrinkage, LFC fitting, outlier handling
4/4 ✓
86
Variant B
Wald tests for differential expression with Benjamini–Hochberg FDR (padj)
4/4 ✓
86
Stress
End-to-end case for End-to-end DESeq2-like workflow: normalization (size factors), dispersion estimation/shrinkage, LFC fitting, outlier handling
4/4 ✓
91
Canonical✅ Pass
Differential gene expression analysis for bulk RNA-seq count matrices using a DESeq2-like workflow in Python; use when you need Wald tests, FDR correction, and optional LFC shrinkage for condition/batch/covariate designs
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 pydeseq2 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
Differential gene expression analysis for bulk RNA-seq count matrices using a DESeq2-like workflow in Python; use when you need Wald tests, FDR correction, and optional LFC shrinkage for condition/batch/covariate designs
Differential gene expression analysis for bulk RNA-seq count... 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 pydeseq2 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
86
Edge✅ Pass
End-to-end DESeq2-like workflow: normalization (size factors), dispersion estimation/shrinkage, LFC fitting, outlier handling
The archived run treated End-to-end DESeq2-like workflow: normalization (size factors),... as a bounded analysis workflow rather than a purely narrative instruction path.
Basic 33/40|Specialized 53/60|Total 86/100
✅A1The pydeseq2 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
86
Variant B✅ Pass
Wald tests for differential expression with Benjamini–Hochberg FDR (padj)
The archived run treated Wald tests for differential expression with Benjamini–Hochberg FDR... as a bounded analysis workflow rather than a purely narrative instruction path.
Basic 32/40|Specialized 54/60|Total 86/100
✅A1The pydeseq2 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
86
Stress✅ Pass
End-to-end case for End-to-end DESeq2-like workflow: normalization (size factors), dispersion estimation/shrinkage, LFC fitting, outlier handling
This stress case stayed within the packaged analysis boundary and kept a reviewable task contract.
Basic 29/40|Specialized 57/60|Total 86/100
✅A1The pydeseq2 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 Total87.2 / 100
Key Strengths
- Primary routing is Data Analysis with execution mode B
- Static quality score is 87/100 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. run_deseq2_analysis.py: rc=1