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create-illustrations-and-scenes

规划、创作、编辑或审查以画面叙事、人物动作、空间、光色或视觉隐喻为主要价值的插画与场景;覆盖编辑/概念插画、叙事场景、角色与群像、环境世界观、物件主体、拼贴/摄影、3D 辅助、生成辅助和动画场景,并要求按风险证明构图、场景逻辑、专业工具、可编辑母版与限定声明。

61

Quality

71%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins-dist/design-engine/raven_design/skills/create-illustrations-and-scenes/SKILL.md
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.

The body is a well-structured, dense domain contract with a clearly sequenced seven-gate workflow, explicit validation checkpoints and feedback loops, and clean progressive disclosure to two real reference files. Its only weakness is mild repetition of the read-only/zero-write and claim-ceiling patterns across gates.

DimensionReasoningScore

Conciseness

Dense and competence-assuming with no padding of basic concepts, but the Diagnose/Audit zero-write rule and claim-ceiling pattern recur across several gates and could be consolidated, leaving minor trims.

4 / 5

Actionability

Instruction-only yet concrete: named gates, recordable fields (input/output hash, lineage), explicit claim-ceiling labels (composition candidate, internal-positive) and allowed/forbidden actions give mostly executable guidance with only minor gaps.

4 / 5

Workflow Clarity

Seven gates are explicitly sequenced, each with trigger/inputs/actions/evidence/claim_ceiling/failure_return; failure_return fields create explicit feedback loops back to specific earlier gates and claim ceilings act as validation checkpoints.

5 / 5

Progressive Disclosure

Body is an overview that points to two real one-level-deep references (patterns.md, tool-profiles.md) with clear trigger conditions for when to read each; content is appropriately split and easy to navigate.

5 / 5

Total

18

/

20

Passed

Description

58%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.

The description gives a clear, comprehensive account of what the skill does across illustration subtypes with risk-based proof requirements, but it lacks an explicit 'when to use' trigger clause and relies on fairly technical domain language. This caps completeness and trigger-term quality at the midpoint.

Suggestions

Append an explicit 'Use when...' clause naming natural user triggers (e.g., creating or editing illustrations, narrative scenes, character art, environment/worldbuilding visuals, animation scenes).

Add common-synonym trigger terms users would actually say (插画, 场景, 角色设计, 分镜, 概念图) alongside the technical vocabulary.

Replace generic verbs with a couple of more specific actions to lift specificity from 4 toward 5.

DimensionReasoningScore

Specificity

Names the domain plus several concrete actions (规划/创作/编辑/审查) and comprehensively enumerates nine subtypes and five risk-based proof requirements, but the action verbs themselves are generic rather than narrowly specified.

4 / 5

Completeness

The 'what' is clearly stated (plan/create/edit/review illustrations and scenes across subtypes with risk-proven claims), but there is no 'Use when...' clause or equivalent explicit trigger guidance, capping completeness at 3 per the rubric.

3 / 5

Trigger Term Quality

Covers relevant domain keywords (插画, 场景, 角色与群像, 环境世界观, 动画场景) but leans technical (画面叙事, 视觉隐喻, 可编辑母版, 限定声明) and omits natural user phrasings and common synonyms.

3 / 5

Distinctiveness Conflict Risk

Occupies a clearly delineated niche (illustration/scene domain layer) with distinct subtypes and proof obligations; only minor overlap risk with the parent visual-artifact-design skill.

4 / 5

Total

14

/

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
EverMind-AI/Raven
Reviewed

Table of Contents

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