Evidence Insight
arboreto
85100Total Score
Core Capability
85 / 100
Functional Suitability
11 / 12
Reliability
9 / 12
Performance & Context
8 / 8
Agent Usability
14 / 16
Human Usability
8 / 8
Security
9 / 12
Maintainability
9 / 12
Agent-Specific
17 / 20
Medical Task
15 / 20 Passed
85You have a bulk RNA-seq expression matrix and want to infer transcription factor (TF) → target gene regulatory edges
3/4
85You have single-cell RNA-seq data (after normalization/aggregation as needed) and want to recover putative regulatory interactions
3/4
85GRN inference from gene expression data using GRNBoost2 (gradient boosting) or GENIE3 (random forest)
3/4
85Scalable execution via Dask, from a single machine to multi-node clusters
3/4
85End-to-end case for GRN inference from gene expression data using GRNBoost2 (gradient boosting) or GENIE3 (random forest)
3/4
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 legacy review kept arboreto tied to supported scientific content rather than invented findings. |
| Practice Boundaries | PASS | Practice boundaries held because the package remained focused on source handling, lookup, or structured evidence use. |
| Methodological Ground | PASS | The archived review found the package methodologically anchored to a named assessment rule set. |
| Code Usability | PASS | The archived review found the packaged execution path for arboreto usable in its intended context. |
Core Capability85 / 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
Related legacy finding for arboreto: Improve stress-case output rigor. Stress and boundary scenarios show weaker consistency
9 / 12
75%
Performance & Context
Performance context reached full score in the archived evaluation.
8 / 8
100%
Agent Usability
A modest deduction remained in agent usability for arboreto in the archived review.
14 / 16
88%
Human Usability
No point loss was recorded for human usability in the legacy audit.
8 / 8
100%
Security
The legacy audit deducted points for arboreto in security.
9 / 12
75%
Maintainability
The archived evaluation left some headroom for arboreto under maintainability.
9 / 12
75%
Agent-Specific
Related legacy finding for arboreto: Stabilize executable path and fallback behavior. Some inputs only reached PARTIAL due to execution gaps or weak boundary handling
17 / 20
85%
Core Capability Total85 / 100
Medical TaskExecution Average: 85 / 100 — Assertions: 15/20 Passed
85
Canonical
You have a bulk RNA-seq expression matrix and want to infer transcription factor (TF) → target gene regulatory edges
3/4 ✓
85
Variant A
You have single-cell RNA-seq data (after normalization/aggregation as needed) and want to recover putative regulatory interactions
3/4 ✓
85
Edge
GRN inference from gene expression data using GRNBoost2 (gradient boosting) or GENIE3 (random forest)
3/4 ✓
85
Variant B
Scalable execution via Dask, from a single machine to multi-node clusters
3/4 ✓
85
Stress
End-to-end case for GRN inference from gene expression data using GRNBoost2 (gradient boosting) or GENIE3 (random forest)
3/4 ✓
85
Canonical✅ Pass
You have a bulk RNA-seq expression matrix and want to infer transcription factor (TF) → target gene regulatory edges
This canonical case was mostly intact, but the archived review centered its concern on: The script execution path completed successfully for the documented case.
Basic 33/40|Specialized 52/60|Total 85/100
✅A1The arboreto output structure matches the documented deliverable
❌A2The script execution path completed successfully for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 3 / 4
85
Variant A✅ Pass
You have single-cell RNA-seq data (after normalization/aggregation as needed) and want to recover putative regulatory interactions
The preserved weakness for You have single-cell RNA-seq data (after normalization/aggregation as needed) and want to recover putative regulatory interactions was concentrated in one point: The script execution path completed successfully for the documented case.
Basic 31/40|Specialized 54/60|Total 85/100
✅A1The arboreto output structure matches the documented deliverable
❌A2The script execution path completed successfully for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 3 / 4
85
Edge✅ Pass
GRN inference from gene expression data using GRNBoost2 (gradient boosting) or GENIE3 (random forest)
The main issue in this edge run was: The script execution path completed successfully for the documented case.
Basic 30/40|Specialized 55/60|Total 85/100
✅A1The arboreto output structure matches the documented deliverable
❌A2The script execution path completed successfully for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 3 / 4
85
Variant B✅ Pass
Scalable execution via Dask, from a single machine to multi-node clusters
This variant b case was mostly intact, but the archived review centered its concern on: The script execution path completed successfully for the documented case.
Basic 29/40|Specialized 56/60|Total 85/100
✅A1The arboreto output structure matches the documented deliverable
❌A2The script execution path completed successfully for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 3 / 4
85
Stress✅ Pass
End-to-end case for GRN inference from gene expression data using GRNBoost2 (gradient boosting) or GENIE3 (random forest)
This stress case was mostly intact, but the archived review centered its concern on: The script execution path completed successfully for the documented case.
Basic 26/40|Specialized 59/60|Total 85/100
✅A1The arboreto output structure matches the documented deliverable
❌A2The script execution path completed successfully for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 3 / 4
Medical Task Total85 / 100
Key Strengths
- Primary routing is Evidence Insight with execution mode B
- Static quality score is 85/100 and dynamic average is 73.6/100
- Assertions and command execution outcomes are recorded per input for human review
- Execution verification summary: Script verification 0/1; adjustment=0. infer_network.py: rc=1