Data Analysis
scanpy
89100Total Score
Core Capability
86 / 100
Functional Suitability
11 / 12
Reliability
10 / 12
Performance & Context
7 / 8
Agent Usability
14 / 16
Human Usability
7 / 8
Security
10 / 12
Maintainability
10 / 12
Agent-Specific
17 / 20
Medical Task
20 / 20 Passed
96Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata
4/4
92Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata
4/4
90Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata
4/4
90Packaged executable path(s): scripts/qc_analysis.py
4/4
90End-to-end case for Scope-focused workflow aligned to: Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata
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 | No scientific-integrity problem was surfaced because the package did not claim more than the available records, article text, or script evidence supported. |
| Practice Boundaries | PASS | The evaluated outputs stayed inside the Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization,... and did not drift into unsupported interpretation beyond the available inputs. |
| Methodological Ground | PASS | The archived evaluation treated the workflow as method-linked rather than ad hoc. |
| Code Usability | PASS | The archived review preserved a usable code path with named scripts, expected inputs, and a recognizable output contract. |
Core Capability86 / 100 — 8 Categories
Functional Suitability
Related legacy finding for scanpy: Improve stress-case output rigor. Stress and boundary scenarios show weaker consistency
11 / 12
92%
Reliability
Related legacy finding for scanpy: Improve stress-case output rigor. Stress and boundary scenarios show weaker consistency
10 / 12
83%
Performance & Context
Performance-context scoring suggests the package could handle larger or denser runs a little more gracefully.
7 / 8
88%
Agent Usability
The packaged analysis path is understandable, though the archived score suggests slightly clearer routing would help.
14 / 16
88%
Human Usability
The archived score suggests the output contract could be a little easier for users to inspect or reuse.
7 / 8
88%
Security
Security remained strong, though the archived review still left some room for clearer execution guardrails.
10 / 12
83%
Maintainability
Maintainability stayed solid, with only limited room to simplify scripts, dependencies, or packaging structure.
10 / 12
83%
Agent-Specific
Related legacy finding for scanpy: Improve stress-case output rigor. Stress and boundary scenarios show weaker consistency
17 / 20
85%
Core Capability Total86 / 100
Medical TaskExecution Average: 91.6 / 100 — Assertions: 20/20 Passed
96
Canonical
Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata
4/4 ✓
92
Variant A
Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata
4/4 ✓
90
Edge
Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata
4/4 ✓
90
Variant B
Packaged executable path(s): scripts/qc_analysis.py
4/4 ✓
90
Stress
End-to-end case for Scope-focused workflow aligned to: Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata
4/4 ✓
96
Canonical✅ Pass
Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata
Standard single-cell RNA-seq analysis pipeline. For quality control... remained tied to the documented analysis contract even when the preserved evidence centered on instructions instead of a full rerun.
Basic 36/40|Specialized 60/60|Total 96/100
✅A1The scanpy 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
92
Variant A✅ Pass
Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata
This variant a case stayed within the packaged analysis boundary and kept a reviewable task contract.
Basic 34/40|Specialized 58/60|Total 92/100
✅A1The scanpy 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
90
Edge✅ Pass
Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata
Standard single-cell RNA-seq analysis pipeline. For quality control... remained tied to the documented analysis contract even when the preserved evidence centered on instructions instead of a full rerun.
Basic 33/40|Specialized 57/60|Total 90/100
✅A1The scanpy 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
90
Variant B✅ Pass
Packaged executable path(s): scripts/qc_analysis.py
Packaged executable path(s): scripts/qc_analysis.py remained tied to the documented analysis contract even when the preserved evidence centered on instructions instead of a full rerun.
Basic 32/40|Specialized 58/60|Total 90/100
✅A1The scanpy 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
90
Stress✅ Pass
End-to-end case for Scope-focused workflow aligned to: Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata
The archived run treated Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization,... as a bounded analysis workflow rather than a purely narrative instruction path.
Basic 29/40|Specialized 60/60|Total 90/100
✅A1The scanpy 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 Total91.6 / 100
Key Strengths
- Primary routing is Data Analysis with execution mode B
- Static quality score is 86/100 and dynamic average is 78.6/100
- Assertions and command execution outcomes are recorded per input for human review
- Execution verification summary: Script verification 1/1; adjustment=5. qc_analysis.py: OK