Data Analysis

lipinski-rule-filter

86100Total Score

Core Capability

88 / 100

Functional Suitability

11 / 12

Reliability

10 / 12

Performance & Context

8 / 8

Agent Usability

14 / 16

Human Usability

8 / 8

Security

10 / 12

Maintainability

10 / 12

Agent-Specific

17 / 20

Medical Task

17 / 20 Passed

100Filter compound libraries based on Lipinski's Rule of Five for drug-likeness

4/4

100Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format

4/4

92Filter compound libraries based on Lipinski's Rule of Five for drug-likeness

4/4

65Packaged executable path(s): scripts/main.py

3/4

65End-to-end case for Scope-focused workflow aligned to: Filter compound libraries based on Lipinski's Rule of Five for drug-likeness

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	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	Practice boundaries held because the package remained focused on Filter compound libraries based on Lipinski's Rule of Five for drug-likeness rather than overclaiming what the records supported.
Methodological Ground	PASS	Methodological grounding was preserved through the documented inputs, transformations, and expected artifacts.
Code Usability	PASS	The archived review preserved a usable code path with named scripts, expected inputs, and a recognizable output contract.

Core Capability88 / 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

Performance context reached full score in the archived evaluation.

8 / 8

100%

Agent Usability

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

14 / 16

88%

Human Usability

No point loss was recorded for human usability in the legacy audit.

8 / 8

100%

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

Agent specific 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

17 / 20

85%

Core Capability Total88 / 100

Medical TaskExecution Average: 84.4 / 100 — Assertions: 17/20 Passed

100

Canonical

Filter compound libraries based on Lipinski's Rule of Five for drug-likeness

4/4 ✓

100

Variant A

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

4/4 ✓

Edge

Filter compound libraries based on Lipinski's Rule of Five for drug-likeness

4/4 ✓

Variant B

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

3/4 ⚠

Stress

End-to-end case for Scope-focused workflow aligned to: Filter compound libraries based on Lipinski's Rule of Five for drug-likeness

2/4 ⚠

100

Canonical✅ Pass

Filter compound libraries based on Lipinski's Rule of Five for drug-likeness

The archived evaluation treated Filter compound libraries based on Lipinski's Rule of Five for... as a clean in-scope run.

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

✅A1The lipinski-rule-filter 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

100

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 60/60|Total 100/100

✅A1The lipinski-rule-filter 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

Filter compound libraries based on Lipinski's Rule of Five for drug-likeness

The Filter compound libraries based on Lipinski's Rule of Five for... path verified the packaged helper command without exposing a deeper execution issue.

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

✅A1The lipinski-rule-filter 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⚠️ Warning

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

The main issue in this variant b run was: Script execution path is available (command exit code is 0).

Basic 26/40|Specialized 39/60|Total 65/100

✅A1The lipinski-rule-filter 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: 3 / 4

Stress⚠️ Warning

End-to-end case for Scope-focused workflow aligned to: Filter compound libraries based on Lipinski's Rule of Five for drug-likeness

The main issue in this stress run was: The output stays within declared skill scope and target objective.

Basic 25/40|Specialized 40/60|Total 65/100

✅A1The lipinski-rule-filter 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 Total84.4 / 100

Key Strengths

Primary routing is Data Analysis with execution mode B
Static quality score is 88/100 and dynamic average is 84.4/100
Assertions and command execution outcomes are recorded per input for human review