Data Analysis
cnv-caller-plotter
Detect copy number variations from whole genome sequencing data and generate publication-quality genome-wide CNV plots. Supports CNV calling, segmentation, and visualization for cancer genomics and rare disease analysis.
88100Total 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
18 / 20 Passed
100Detect copy number variations from whole genome sequencing data and generate publication-quality genome-wide CNV plots. Supports CNV calling, segmentation, and visualization for cancer genomics and rare disease analysis
4/4
97Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format
4/4
92Detect copy number variations from whole genome sequencing data and generate publication-quality genome-wide CNV plots. Supports CNV calling, segmentation, and visualization for cancer genomics and rare disease analysis
4/4
91Packaged executable path(s): scripts/main.py
4/4
65End-to-end case for Scope-focused workflow aligned to: Detect copy number variations from whole genome sequencing data and generate publication-quality genome-wide CNV plots. Supports CNV calling, segmentation, and visualization for cancer genomics and rare disease analysis
2/4
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 evaluated outputs stayed inside the Detect copy number variations from whole genome sequencing data and generate... 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 | Code usability passed because the package still exposed a reviewable execution surface for its documented workflow. |
Core Capability86 / 100 — 8 Categories
Functional Suitability
The archived deduction in functional suitability traces back to: Improve stress-case output rigor. Stress and boundary scenarios show weaker consistency
11 / 12
92%
Reliability
Reliability was softened by the legacy issue 'Improve stress-case output rigor'. Stress and boundary scenarios show weaker consistency
10 / 12
83%
Performance & Context
The archived review left minor headroom in how this analysis workflow scales across heavier contexts.
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
Human usability was softened by the legacy issue 'Stabilize executable path and fallback behavior'. Some inputs only reached PARTIAL due to execution gaps or weak boundary handling
7 / 8
88%
Security
A modest security gap remained in the archived evaluation despite otherwise controlled workflow behavior.
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 cnv-caller-plotter: Stabilize executable path and fallback behavior. Some inputs only reached PARTIAL due to execution gaps or weak boundary handling
17 / 20
85%
Core Capability Total86 / 100
Medical TaskExecution Average: 89 / 100 — Assertions: 18/20 Passed
100
Canonical
Detect copy number variations from whole genome sequencing data and generate publication-quality genome-wide CNV plots. Supports CNV calling, segmentation, and visualization for cancer genomics and rare disease analysis
4/4 ✓
97
Variant A
Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format
4/4 ✓
92
Edge
Detect copy number variations from whole genome sequencing data and generate publication-quality genome-wide CNV plots. Supports CNV calling, segmentation, and visualization for cancer genomics and rare disease analysis
4/4 ✓
91
Variant B
Packaged executable path(s): scripts/main.py
4/4 ✓
65
Stress
End-to-end case for Scope-focused workflow aligned to: Detect copy number variations from whole genome sequencing data and generate publication-quality genome-wide CNV plots. Supports CNV calling, segmentation, and visualization for cancer genomics and rare disease analysis
2/4 ⚠
100
Canonical✅ Pass
Detect copy number variations from whole genome sequencing data and generate publication-quality genome-wide CNV plots. Supports CNV calling, segmentation, and visualization for cancer genomics and rare disease analysis
The archived evaluation treated Detect copy number variations from whole genome sequencing data and... as a clean in-scope run.
Basic 40/40|Specialized 60/60|Total 100/100
✅A1The cnv-caller-plotter output structure covers required deliverable blocks
✅A2Script execution path is available (command exit code is 0)
✅A3The output stays within declared skill scope and target objective
✅A4Required research safety/boundary guidance is present without overclaims
Pass rate: 4 / 4
97
Variant A✅ Pass
Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format
The archived evaluation treated Use this skill for data analysis tasks that require explicit... as a clean in-scope run.
Basic 40/40|Specialized 57/60|Total 97/100
✅A1The cnv-caller-plotter output structure covers required deliverable blocks
✅A2Script execution path is available (command exit code is 0)
✅A3The output stays within declared skill scope and target objective
✅A4Required research safety/boundary guidance is present without overclaims
Pass rate: 4 / 4
92
Edge✅ Pass
Detect copy number variations from whole genome sequencing data and generate publication-quality genome-wide CNV plots. Supports CNV calling, segmentation, and visualization for cancer genomics and rare disease analysis
The archived evaluation treated Detect copy number variations from whole genome sequencing data and... as a clean in-scope run.
Basic 36/40|Specialized 56/60|Total 92/100
✅A1The cnv-caller-plotter output structure covers required deliverable blocks
✅A2Script execution path is available (command exit code is 0)
✅A3The output stays within declared skill scope and target objective
✅A4Required research safety/boundary guidance is present without overclaims
Pass rate: 4 / 4
91
Variant B✅ Pass
Packaged executable path(s): scripts/main.py
The archived evaluation treated Packaged executable path(s): scripts/main.py as a clean in-scope run.
Basic 36/40|Specialized 55/60|Total 91/100
✅A1The cnv-caller-plotter output structure covers required deliverable blocks
✅A2Script execution path is available (command exit code is 0)
✅A3The output stays within declared skill scope and target objective
✅A4Required research safety/boundary guidance is present without overclaims
Pass rate: 4 / 4
65
Stress⚠️ Warning
End-to-end case for Scope-focused workflow aligned to: Detect copy number variations from whole genome sequencing data and generate publication-quality genome-wide CNV plots. Supports CNV calling, segmentation, and visualization for cancer genomics and rare disease analysis
This stress case was mostly intact, but the archived review centered its concern on: The output stays within declared skill scope and target objective.
Basic 25/40|Specialized 40/60|Total 65/100
✅A1The cnv-caller-plotter output structure covers required deliverable blocks
✅A2Script execution path is available (command exit code is 0)
❌A3The output stays within declared skill scope and target objective
❌A4Required research safety/boundary guidance is present without overclaims
Pass rate: 2 / 4
Medical Task Total89 / 100
Key Strengths
- Primary routing is Data Analysis with execution mode B
- Static quality score is 86/100 and dynamic average is 89.0/100
- Assertions and command execution outcomes are recorded per input for human review