Data Analysis

scrna-cell-type-annotator

Auto-annotate cell clusters from single-cell RNA data using marker genes.

86100Total Score

Core Capability

83 / 100

Functional Suitability

11 / 12

Reliability

10 / 12

Performance & Context

8 / 8

Agent Usability

13 / 16

Human Usability

7 / 8

Security

9 / 12

Maintainability

9 / 12

Agent-Specific

16 / 20

Medical Task

18 / 20 Passed

100Auto-annotate cell clusters from single-cell RNA data using marker genes

4/4

92Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format

4/4

91Auto-annotate cell clusters from single-cell RNA data using marker genes

4/4

89Packaged executable path(s): scripts/main.py

4/4

64End-to-end case for Scope-focused workflow aligned to: Auto-annotate cell clusters from single-cell RNA data using marker genes

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

PASS

Research 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 evaluated outputs stayed inside the Auto-annotate cell clusters from single-cell RNA data using marker genes 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 Capability83 / 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

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

The legacy audit gave full marks to performance context for this package.

8 / 8

100%

Agent Usability

The packaged analysis path is understandable, though the archived score suggests slightly clearer routing would help.

13 / 16

81%

Human Usability

The archived deduction in human usability traces back to: Stabilize executable path and fallback behavior. Some inputs only reached PARTIAL due to execution gaps or weak boundary handling

7 / 8

88%

Security

Security remained strong, though the archived review still left some room for clearer execution guardrails.

9 / 12

75%

Maintainability

The analysis package is maintainable overall, though the archived score suggests modest cleanup headroom.

9 / 12

75%

Agent-Specific

Related legacy finding for scrna-cell-type-annotator: Stabilize executable path and fallback behavior. Some inputs only reached PARTIAL due to execution gaps or weak boundary handling

16 / 20

80%

Core Capability Total83 / 100

Medical TaskExecution Average: 87.2 / 100 — Assertions: 18/20 Passed

100

Canonical

Auto-annotate cell clusters from single-cell RNA data using marker genes

4/4 ✓

Variant A

Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format

4/4 ✓

Edge

Auto-annotate cell clusters from single-cell RNA data using marker genes

4/4 ✓

Variant B

Packaged executable path(s): scripts/main.py

4/4 ✓

Stress

End-to-end case for Scope-focused workflow aligned to: Auto-annotate cell clusters from single-cell RNA data using marker genes

2/4 ⚠

100

Canonical✅ Pass

Auto-annotate cell clusters from single-cell RNA data using marker genes

Auto-annotate cell clusters from single-cell RNA data using marker genes remained well-aligned with the documented contract in the preserved audit.

Basic 40/40|Specialized 60/60|Total 100/100

✅A1The scrna-cell-type-annotator 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

Variant A✅ Pass

Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format

Use this skill for data analysis tasks that require explicit... remained well-aligned with the documented contract in the preserved audit.

Basic 36/40|Specialized 56/60|Total 92/100

✅A1The scrna-cell-type-annotator 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

Edge✅ Pass

Auto-annotate cell clusters from single-cell RNA data using marker genes

The Auto-annotate cell clusters from single-cell RNA data using marker genes path verified the packaged helper command without exposing a deeper execution issue.

Basic 36/40|Specialized 55/60|Total 91/100

✅A1The scrna-cell-type-annotator 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

Variant B✅ Pass

Packaged executable path(s): scripts/main.py

The Packaged executable path(s): scripts/main.py scenario completed within the documented Auto-annotate cell clusters from single-cell RNA data using marker genes boundary.

Basic 36/40|Specialized 53/60|Total 89/100

✅A1The scrna-cell-type-annotator 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

Stress⚠️ Warning

End-to-end case for Scope-focused workflow aligned to: Auto-annotate cell clusters from single-cell RNA data using marker genes

The preserved weakness for End-to-end case for Scope-focused workflow aligned to: Auto-annotate cell clusters from single-cell RNA data using marker genes was concentrated in one point: The output stays within declared skill scope and target objective.

Basic 25/40|Specialized 39/60|Total 64/100

✅A1The scrna-cell-type-annotator 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 Total87.2 / 100

Key Strengths

Primary routing is Data Analysis with execution mode B
Static quality score is 83/100 and dynamic average is 87.2/100
Assertions and command execution outcomes are recorded per input for human review