Data Analysis

meta-forest-model-plot

89100Total 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

20 / 20 Passed

98"Generate forest plots for meta-analysis of survival data. Input is a CSV file containing study names, HR and 95% confidence intervals, output forest plot PNG and data table CSV. Supports both R and Python scripts."

4/4

94Step 2: Execute Script (R or Python)

4/4

92Step 1: Validate Input Data

4/4

92Packaged executable path(s): scripts/forest_survival.py plus 1 additional script(s)

4/4

92Step 2: Execute Script (R or Python)

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

PASS

Research 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 "Generate forest plots for meta-analysis of survival data. Input is a CSV file containing... 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 review left a small gap in how directly "Generate forest plots for meta-analysis of survival data. Input is a CSV file containing... resolves into a finished analysis deliverable.

11 / 12

92%

Reliability

Reliability remained good, but the archived review still saw room for steadier behavior under edge conditions.

10 / 12

83%

Performance & Context

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

8 / 8

100%

Agent Usability

The archived review left some headroom in how quickly an agent can lock onto the intended analysis path.

13 / 16

81%

Human Usability

The archived deduction in human usability traces back to: Minor polish before wide rollout. No major defects found

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

Agent-specific quality remained high, with only modest headroom in structured prompting or edge handling.

16 / 20

80%

Core Capability Total83 / 100

Medical TaskExecution Average: 93.6 / 100 — Assertions: 20/20 Passed

Canonical

"Generate forest plots for meta-analysis of survival data. Input is a CSV file containing study names, HR and 95% confidence intervals, output forest plot PNG and data table CSV. Supports both R and Python scripts."

4/4 ✓

Variant A

Step 2: Execute Script (R or Python)

4/4 ✓

Edge

Step 1: Validate Input Data

4/4 ✓

Variant B

Packaged executable path(s): scripts/forest_survival.py plus 1 additional script(s)

4/4 ✓

Stress

Step 2: Execute Script (R or Python)

4/4 ✓

Canonical✅ Pass

The archived run for "Generate forest plots for meta-analysis of survival data. Input is... confirmed the helper entrypoint and left the workflow in a stable state.

Basic 38/40|Specialized 60/60|Total 98/100

✅A1The meta-forest-model-plot 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: 4 / 4

Variant A✅ Pass

Step 2: Execute Script (R or Python)

The Step 2: Execute Script (R or Python) path verified the packaged helper command without exposing a deeper execution issue.

Basic 36/40|Specialized 58/60|Total 94/100

✅A1The meta-forest-model-plot 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: 4 / 4

Edge✅ Pass

Step 1: Validate Input Data

The Step 1: Validate Input Data path verified the packaged helper command without exposing a deeper execution issue.

Basic 35/40|Specialized 57/60|Total 92/100

✅A1The meta-forest-model-plot 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: 4 / 4

Variant B✅ Pass

Packaged executable path(s): scripts/forest_survival.py plus 1 additional script(s)

The archived run for Packaged executable path(s): scripts/forest_survival.py plus 1... confirmed the helper entrypoint and left the workflow in a stable state.

Basic 34/40|Specialized 58/60|Total 92/100

✅A1The meta-forest-model-plot 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: 4 / 4

Stress✅ Pass

Step 2: Execute Script (R or Python)

The archived run for Step 2: Execute Script (R or Python) confirmed the helper entrypoint and left the workflow in a stable state.

Basic 31/40|Specialized 60/60|Total 92/100

✅A1The meta-forest-model-plot 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: 4 / 4

Medical Task Total93.6 / 100

Key Strengths

Primary routing is Data Analysis with execution mode B
Static quality score is 83/100 and dynamic average is 82.6/100
Assertions and command execution outcomes are recorded per input for human review
Execution verification summary: Script verification 1/2; adjustment=3. forest_survival.py: rc=1; validate_skill.py: OK