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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

87%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

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.

An exceptionally well-constructed skill body: a fully sequenced workflow with real validation gates and feedback loops, copy-paste-ready deterministic CLI tooling, and clean one-level-deep progressive disclosure against a verified bundle. The only weaknesses are minor — a slightly long tail (citing/version-fetching instructions) and input formats for the evaluation matrix deferred to a reference file.

DimensionReasoningScore

Conciseness

The body is dense and prescriptive — operating rules, a numbered workflow, and bias controls with almost every clause carrying non-obvious, evidence-calibrated guidance (e.g., "Consensus is not truth and vote counts are not effect sizes"). Minor trimmable material exists (the citing section's version-fetching instructions and a few qualifying asides), which holds it at 4 rather than the every-token-earns-its-place leanness of a 5; it is well above the unnecessary-explanation level of a 3.

4 / 5

Actionability

The three CLI invocations (session_scaffold.py, validate_register.py, evaluate_matrix.py) are complete, copy-paste-ready commands with all flags shown, and the facilitation steps specify exact fields, categories, and statuses to record. It sits at 4 rather than 5 because the input formats for scores.csv/criteria.json are deferred to references/idea_evaluation.md, so the evaluate_matrix path is not fully executable from SKILL.md alone.

4 / 5

Workflow Clarity

The 10-step workflow is clearly sequenced (scope → diversify → generate → share → cluster → criteria → adversarial review → evidence check → gates → decide and log) with explicit validation checkpoints (evidence-check statuses, validate_register.py, ethics/feasibility gates: "A high creativity score never overrides a gate") and genuine feedback loops (reopen an independent generation round after the evidence check; gates can force "redesign, controlled handling, or stopping"). This matches the anchor-5 pattern of clear sequence, explicit validation, and error-recovery loops.

5 / 5

Progressive Disclosure

SKILL.md serves as a navigable overview and splits detail into five real, one-level-deep reference files (all verified present in references/), each clearly signaled both inline ("See references/idea_evaluation.md") and in the annotated reference index; scripts live in scripts/ and are referenced by exact path. Content is appropriately placed and navigation is easy, matching the anchor-5 structure.

5 / 5

Total

18

/

20

Passed

Description

83%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: it states concrete capabilities in third person, gives an explicit 'Use for...' trigger clause with natural terms, and cleanly scopes boundaries via hand-off guidance. The only room for improvement is slightly more concrete action verbs and a few more natural trigger synonyms.

DimensionReasoningScore

Specificity

"Facilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs" names the domain plus six specific capability components, and the second sentence adds concrete hand-off behaviors. Not a 5 because the components are process descriptors rather than the fully concrete, verb-driven actions of the anchor-5 example, and coverage of what the skill actually produces is slightly indirect.

4 / 5

Completeness

It explicitly answers both what ("Facilitates evidence-aware scientific ideation with independent generation, ... decision logs") and when ("Use for early-stage research brainstorming or prioritizing candidate directions") with concrete trigger phrases, and even adds boundary/hand-off guidance. This matches the anchor-5 example pattern exactly; a 4 would require the 'when' to be less explicit than it is.

5 / 5

Trigger Term Quality

"Use for early-stage research brainstorming or prioritizing candidate directions" plus "scientific ideation" in the what-clause give natural phrases users would say ("research brainstorming", "prioritizing candidate directions"). A few common variations are missing ("brainstorm research ideas", "research questions", "come up with hypotheses"), which keeps it below the comprehensive synonym coverage of a 5.

4 / 5

Distinctiveness Conflict Risk

"Scientific ideation" and "early-stage research brainstorming" carve a clear niche, and the explicit hand-offs ("hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills") reduce collision with adjacent skills. Minor overlap risk remains with generic ideation/creativity-brainstorming skills, so it is not the minimal-conflict niche of a 5.

4 / 5

Total

17

/

20

Passed

Validation

100%

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

Reviewed

Table of Contents

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