scientific-brainstorming

Facilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs. Use for early-stage research brainstorming or prioritizing candidate directions; hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills.

Quality

88%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

85%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 procedural skill with a clear sequenced workflow, explicit validation gates, executable CLI examples, and clean one-level-deep reference disclosure. Minor conciseness and inline-example gaps keep it just below full marks on two dimensions.

Suggestions

Trim framing sentences that restate general knowledge (e.g., 'Consensus is not truth and vote counts are not effect sizes') to lean statements of rule only.

Add a brief inline example of the scores.csv / criteria.json shape expected by evaluate_matrix.py so the command is fully copy-paste ready.

Move the dated citation/provenance instructions into references/sources.md and keep only a one-line pointer in the body.

Dimension	Reasoning	Score
Conciseness	Generally lean procedural prose with executable commands, though a few framing sentences restate concepts Claude already knows and could be trimmed.	4 / 5
Actionability	Provides concrete, executable CLI invocations with real flags and per-step instructions; minor gaps in showing input file shapes inline.	4 / 5
Workflow Clarity	A clearly sequenced 10-step workflow with explicit validation checkpoints, gates, and stop conditions ('A high creativity score never overrides a gate').	5 / 5
Progressive Disclosure	The body is a clear overview with one-level-deep, clearly signaled references (indexed at the end) and bundled scripts, all of which exist on disk.	5 / 5
	Total	18 / 20 Passed

Description

92%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, specific description that names concrete capabilities, provides an explicit use-trigger, and de-risks conflicts via hand-off boundaries. The only minor gap is trigger-term synonym coverage.

Dimension	Reasoning	Score
Specificity	Lists multiple distinct concrete capabilities (independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, decision logs), matching the comprehensive-coverage anchor.	5 / 5
Completeness	Explicitly answers both 'what' (the enumerated capabilities) and 'when' via a concrete 'Use for...' clause plus hand-off boundaries.	5 / 5
Trigger Term Quality	'Use for early-stage research brainstorming or prioritizing candidate directions' gives natural user phrasing with good coverage, but a few common synonyms are absent.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear scientific-ideation niche with explicit hand-offs to sibling skills, minimizing overlap and wrong-skill triggers.	5 / 5
	Total	19 / 20 Passed

Validation

100%

Warnings & errors only

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: K-Dense-AI/scientific-agent-skills
Path: skills/scientific-brainstorming/SKILL.md
Commit: 1e5eeff

Reviewed: ~17 hours ago

Table of Contents

Discovery Implementation Validation

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