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scientific-critical-thinking

Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.

69

Quality

85%

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SKILL.md
Quality
Evals
Security

Quality

Content

71%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

Well-structured analytical skill with strong progressive disclosure (real, clearly-linked reference files) and an actionable critique template. The main weakness is conciseness — the General Approach principles and the closing Remember section restate knowledge Claude already has and could be trimmed.

Suggestions

Cut or compress the 'Remember' section and the five 'General Approach' principles (e.g., 'Recognize that all research has limitations', 'Don't apply stricter standards to findings you dislike') — Claude already applies these; keep only skill-specific procedural guidance.

Replace the per-file paragraph re-descriptions in 'Reference Materials' with a compact one-line-per-file table; the file list in 'Core Capabilities' already signals what each reference covers.

Add one short worked example of critiquing a single study end-to-end through the 5-part structure to lift actionability from strong templates to fully concrete guidance.

DimensionReasoningScore

Conciseness

Mostly useful procedural content, but padded sections restate what Claude already knows — the 'General Approach' principles ('Recognize that all research has limitations', 'Distinguish between fatal flaws and minor limitations') and the closing 'Remember' section largely repeat earlier material, and the Reference Materials block re-describes each file at length.

3 / 5

Actionability

Provides concrete, usable templates — a 5-part critique structure (Summary/Strengths/Concerns/Specific Recommendations/Overall Assessment), example uncertainty phrasings ('This could be X or Y; additional information needed is Z'), and executable commands (the generate_schematic.py bash snippet, `grep -r "pattern" references/`). Minor gap: no fully worked end-to-end critique example.

4 / 5

Workflow Clarity

The critique workflow is clearly sequenced with severity tiers (Critical/Important/Minor concerns) and the 'When Uncertain' section gives conditional-assessment branching. No explicit validate→fix→retry loop, but the task is analytical rather than destructive/batch, so the validation cap does not apply.

4 / 5

Progressive Disclosure

SKILL.md is a genuine overview pointing to seven one-level-deep reference files, all present in references/ and linked with clear markdown paths; navigation guidance ('Load references into context when detailed frameworks are needed', grep command) is explicit and there is no nested-reference indirection.

5 / 5

Total

16

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: third-person voice, concrete actions, explicit 'Use for...' triggers, and clear boundary guidance against the peer-review skill. It matches the rubric's good examples closely with no vague fluff.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Evaluate scientific claims and evidence quality', 'assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias)', 'teaching critical analysis' — giving comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

Explicitly answers both what ('Evaluate scientific claims and evidence quality') and when ('Use for assessing experimental design validity...', 'Best for understanding evidence quality, identifying flaws') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Comprehensive natural-term coverage for its audience: 'scientific claims', 'evidence quality', 'experimental design', 'biases', 'confounders', 'GRADE', 'Cochrane Risk of Bias', 'critical analysis' — including the specific framework names researchers actually say.

5 / 5

Distinctiveness Conflict Risk

Clear niche (scientific evidence appraisal via GRADE/Cochrane ROB) with an explicit boundary pointer — 'For formal peer review writing use peer-review' — minimizing conflict with adjacent skills.

5 / 5

Total

20

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

referenced_paths_exist

Referenced path issues: 1 missing

Warning

Total

15

/

16

Passed

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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