create-anatomy
Generate a visual anatomy annotation in Figma showing numbered markers on a component instance with an attribute table. Use when the user mentions "anatomy", "anatomy annotation", "component anatomy", "create anatomy", or wants to annotate a component's structural elements.
84
81%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong skill description that clearly communicates what the skill does (generate visual anatomy annotations with numbered markers and attribute tables in Figma) and when to use it (with explicit trigger terms). The description is concise, uses third person voice, and occupies a distinct niche that minimizes conflict risk with other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Generate a visual anatomy annotation', 'showing numbered markers on a component instance', 'with an attribute table'. These are concrete, specific capabilities. | 3 / 3 |
Completeness | Clearly answers both what ('Generate a visual anatomy annotation in Figma showing numbered markers on a component instance with an attribute table') and when ('Use when the user mentions "anatomy", "anatomy annotation"...') with explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would say: 'anatomy', 'anatomy annotation', 'component anatomy', 'create anatomy', 'annotate a component's structural elements'. Good coverage of variations a user might naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive — the combination of Figma, anatomy annotation, numbered markers, and component instances creates a very clear niche that is unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
62%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill excels at actionability with fully executable Figma Plugin API code and has excellent workflow clarity with explicit validation checkpoints and a progress checklist. However, it is severely hampered by its extreme length — the massive inline JavaScript blocks (repeated marker placement algorithms, table filling logic, font loading) should be extracted into referenced script files, and the skill would benefit enormously from abstracting shared patterns rather than duplicating them across Steps 8 and 8b.
Suggestions
Extract the large JavaScript code blocks into separate referenced files (e.g., scripts/extract-anatomy.js, scripts/build-artwork.js) and reference them from SKILL.md with brief descriptions of inputs/outputs, reducing the main file by 60-70%.
Abstract the duplicated marker placement algorithm, table filling logic, and font loading utility into shared helper descriptions referenced once, rather than repeating nearly identical code in Steps 8 and 8b.
Move the MCP adapter table and figma-mcp page context workaround into a shared reference file since these appear to be cross-skill concerns, not specific to anatomy annotation.
Trim the detailed field documentation for extraction output (the long bullet list after Step 3's script) into a compact schema or reference file — Claude can infer field semantics from the extraction code itself.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | This skill is extremely verbose at ~800+ lines with massive inline JavaScript code blocks that could be referenced as external files. It includes extensive implementation details that Claude could infer from shorter specifications, and repeats similar patterns (marker placement, table filling, font loading) across multiple steps without abstraction. | 1 / 3 |
Actionability | The skill provides fully executable JavaScript code blocks with specific Figma Plugin API calls, concrete variable names, complete algorithms for marker placement and collision avoidance, and precise template manipulation. Every step has copy-paste-ready code with clear placeholder substitution patterns. | 3 / 3 |
Workflow Clarity | The workflow has a clear numbered checklist with explicit validation steps (Step 10 visual validation with up to 3 fix iterations), conditional branching (MCP provider selection, destination handling), error recovery guidance, and explicit data dependencies between steps (save frameId, compositionSectionId, etc.). | 3 / 3 |
Progressive Disclosure | The skill references external files appropriately (anatomy/agent-anatomy-instruction.md, uspecs.config.json) but includes enormous inline code blocks that should be in separate referenced files. The MCP adapter table is a good overview pattern, but the 500+ lines of JavaScript embedded inline make the main file unwieldy. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (1669 lines); consider splitting into references/ and linking | Warning |
Total | 10 / 11 Passed | |
- Repository
- redongreen/uSpec
- Commit
b1213ef
- Reviewed
Table of Contents
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