concept-modeler
从模糊的用户需求中提取领域概念——实体、流程和"暗物质"（用户没说的）。基于 DDD（领域驱动设计）方法论。
71
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Discovery
32%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 identifies a specific methodology (DDD) and describes the general purpose of extracting domain concepts, but lacks explicit trigger guidance for when Claude should select this skill. The metaphorical language ('暗物质'/dark matter) adds flavor but reduces clarity, and the description would benefit from concrete examples of user requests that should trigger this skill.
Suggestions
Add a 'Use when...' clause with explicit triggers like '当用户提到领域建模、DDD、业务实体、领域分析时使用' (Use when user mentions domain modeling, DDD, business entities, domain analysis)
Include more natural trigger terms users would say, such as '需求分析', '业务逻辑', 'domain model', '领域模型'
Replace or clarify the metaphorical '暗物质' term with concrete examples of what implicit requirements look like
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (DDD methodology) and some actions ('提取领域概念——实体、流程和暗物质'), but the actions are somewhat abstract rather than concrete operations. The term '暗物质' (dark matter) is metaphorical rather than specific. | 2 / 3 |
Completeness | Describes what it does (extract domain concepts from vague requirements) but completely lacks a 'Use when...' clause or any explicit trigger guidance. Per rubric guidelines, missing explicit trigger guidance should cap completeness at 2, and this has no trigger guidance at all. | 1 / 3 |
Trigger Term Quality | Includes some relevant keywords like 'DDD', '领域驱动设计', '实体', '流程', but missing common variations users might say like 'domain modeling', 'requirements analysis', 'business logic', or Chinese equivalents like '需求分析', '业务建模'. | 2 / 3 |
Distinctiveness Conflict Risk | The DDD methodology reference provides some distinctiveness, but '模糊的用户需求' (vague user requirements) is quite broad and could overlap with general requirements gathering, product management, or other analysis skills. | 2 / 3 |
Total | 7 / 12 Passed |
Implementation
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill that provides actionable guidance for domain modeling with clear workflows and good progressive disclosure. The main weakness is some verbosity in the form of stylistic flourishes (emoji, 'old master sayings') that don't add functional value. The concrete checklists, JSON output format, and tool references make this highly actionable.
Suggestions
Remove decorative elements like '老师傅箴言' labels and excessive emoji - the guidance itself is valuable but the framing adds tokens without value
Consolidate the 'mandatory thinking' section into the workflow steps rather than as a separate preamble to reduce redundancy
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Content is reasonably efficient but includes some unnecessary flourishes (emoji headers, 'old master sayings') and explanatory text that Claude doesn't need. The core methodology is clear but could be tighter. | 2 / 3 |
Actionability | Provides concrete checklists, specific questions to ask, clear output JSON schema, and executable tool commands. The extraction process has specific examples showing input/output transformations. | 3 / 3 |
Workflow Clarity | Clear three-step sequence (Noun Hunting → Verb Analysis → Dark Matter Detection) with explicit validation checkpoints. The checklist table for dark matter detection provides systematic verification, and the mandatory thinking step ensures deliberate analysis. | 3 / 3 |
Progressive Disclosure | Well-organized with clear sections. References to external tools and prompts are one level deep and clearly signaled. The Collaboration section provides context without bloating the main content. | 3 / 3 |
Total | 11 / 12 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 — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Reviewed
Table of Contents
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