| name | quality-grading |
| description | Use this skill to grade code, specifications, or design documents across four quality dimensions using a 1-5 scoring scale. In grade-and-fix mode, the skill auto-improves artifacts scoring below 4 without prompting. Invoke when you want consistent quality assessment on design, implementation, or specification with actionable feedback and self-healing improvements. |
Quality Grading Skill
Evaluate and improve code, specifications, or design documents across four quality dimensions with calibrated scoring and auto-fix capabilities.
Overview
This skill provides a multi-dimensional quality grading system for any artifact type:
- Code: Implementation files, modules, functions
- Specifications: Requirements documents, README, acceptance criteria
- Designs: Architecture documents, system designs, technical specifications
It supports two invocation modes:
- evaluate: Score the artifact and return structured JSON results
- grade-and-fix: Score the artifact, then auto-fix dimensions scoring below 4, re-grade, and provide final results
The skill targets score 4+ as the quality bar. Auto-fix attempts are limited to 2 per dimension to keep improvements pragmatic and avoid unnecessary complexity.
Invocation
Load this skill when:
- You want quality feedback on code or implementation
- You want quality feedback on design or architecture documents
- You want quality feedback on specifications or requirements
- You want to improve artifacts scoring below 4 automatically
- You need consistent, calibrated quality assessments
Input
artifact: Path to the file, directory, or snippet to grade (required)mode: evaluate or grade-and-fix (default: evaluate)
Process
- Load artifact: Read the target file, all files in the directory, or parse the snippet
- Detect artifact type: Determine if this is code, a specification, or a design document
- Apply dimension rubrics: Score each of the four dimensions independently, adapting criteria to artifact type
- Use calibration examples: Compare against few-shot examples for consistency
- If grade-and-fix mode and score < 4: Apply targeted auto-fix, re-grade, repeat up to 2 times
- Generate JSON output: Return structured results with scores, justifications, and suggestions
Grading Dimensions
1. Design Quality
Measures: Architecture clarity, scalability, and separation of concerns
For Code: Module structure, dependency management, separation of concerns
For Designs: Architecture clarity, module boundaries, scalability patterns, diagram quality
For Specifications: Logical organization, traceability, clarity of structure
What to look for:
- Clear module boundaries and logical file organization
- Proven scalability patterns appropriate to the problem
- Strong separation of concerns (no intermingled responsibilities)
- Clean dependency flow
Scoring Rubric:
| Score | Descriptor |
|---|
| 5 | Exceptional architecture with clear module boundaries, proven scalability patterns, strong separation of concerns |
| 4 | Good architecture with clear modules, adequate scalability consideration, minor separation issues |
| 3 | Adequate architecture with recognizable modules, moderate separation of concerns |
| 2 | Weak architecture with unclear module boundaries, limited separation of concerns |
| 1 | Poor architecture with tangled dependencies, no clear modules, intermingled concerns |
Auto-Fix (score < 4):
- Add missing module boundaries or clear separation between concerns
- Consolidate tangled dependencies into logical modules
- Simplify overly complex architectural patterns
2. Originality
Measures: Avoidance of generic boilerplate and tailoring to the problem
For Code: Problem-specific solutions vs copy-paste patterns
For Designs: Domain-specific architecture vs generic templates
For Specifications: Tailored requirements vs boilerplate language
What to look for:
- Problem-specific terminology and approach
- Contextual examples that map to the domain
- Removal of irrelevant template content
- Novel solutions rather than copy-paste patterns
Scoring Rubric:
| Score | Descriptor |
|---|
| 5 | Highly tailored solution with novel approaches directly mapped to the problem domain |
| 4 | Mostly tailored solution with some unique adaptations to the problem |
| 3 | Mix of generic and tailored approaches |
| 2 | Mostly generic with minimal adaptation to the problem |
| 1 | Generic, copy-paste solution with no adaptation to the specific problem |
Auto-Fix (score < 4):
- Replace generic boilerplate with problem-specific terminology
- Add context-specific examples or patterns
- Remove irrelevant template content
3. Craft
Measures: Code cleanliness, error handling, and documentation
For Code: Clean code, error handling, comments, documentation
For Designs: Diagram clarity, consistent formatting, completeness
For Specifications: Clear writing, consistent terminology, proper structure
What to look for:
- Clean, consistent formatting and naming
- Comprehensive error handling for edge cases
- Clear documentation (doc comments, README, inline explanations)
- No obvious bugs or security issues
Scoring Rubric:
| Score | Descriptor |
|---|
| 5 | Exceptionally clean code with comprehensive error handling and exemplary documentation |
| 4 | Clean code with good error handling and documentation with minor gaps |
| 3 | Adequate code quality with some error handling and documentation |
| 2 | Messy code with limited error handling and minimal documentation |
| 1 | Messy, error-prone code with little to no documentation |
Auto-Fix (score < 4):
- Add missing error handling for edge cases
- Clean up inconsistent formatting or naming
- Add doc comments to undocumented functions
4. Functionality
Measures: Feature completeness and edge case handling
For Code: Features work, edge cases handled
For Designs: All requirements covered, feasibility, completeness
For Specifications: Complete requirements, clear acceptance criteria, no gaps
What to look for:
- All specified features are implemented
- Edge cases are identified and handled
- Requirements or acceptance criteria are covered
- No broken references or incomplete specifications
Scoring Rubric:
| Score | Descriptor |
|---|
| 5 | Complete feature implementation with comprehensive edge case handling |
| 4 | Full implementation with minor edge case gaps |
| 3 | Most features implemented with some edge cases handled |
| 2 | Partial implementation with several unhandled edge cases |
| 1 | Missing features and unhandled edge cases causing frequent failures |
Auto-Fix (score < 4):
- Add missing functionality or requirements coverage
- Address identified edge cases in the implementation
- Fix broken references or incomplete specifications
Self-Healing Behavior
When invoked in grade-and-fix mode:
- Trigger: Auto-fix activates for any dimension scoring below 4
- Fix Strategy: Apply minimal, targeted changes that address the specific gap without introducing unnecessary complexity
- Re-grade: After each fix, re-score to verify improvement
- Attempts: Maximum 2 auto-fix attempts per dimension
- Fallback: If auto-fix fails to achieve score 4+, record actionable suggestions instead of blocking
- Principle: Prioritize pragmatic simplicity over exhaustive perfection
Few-Shot Calibration
Use the examples in references/ directories to calibrate your scoring:
- design-quality/: Examples of architecture quality at each score level
- originality/: Examples of boilerplate vs tailored solutions
- craft/: Examples of code cleanliness and documentation quality
- functionality/: Examples of feature completeness
Compare the artifact you're grading against these examples for consistent calibration.
Output Format
The skill produces JSON output:
{
"artifact": "<path-to-graded-file>",
"artifactType": "code | specification | design",
"timestamp": "<ISO-8601 timestamp>",
"mode": "evaluate | grade-and-fix",
"dimensions": {
"designQuality": {
"score": 1-5,
"justification": "<brief explanation of the score>",
"improved": "<null | description of auto-fix applied>"
},
"originality": {
"score": 1-5,
"justification": "<brief explanation of the score>",
"improved": "<null | description of auto-fix applied>"
},
"craft"
Glossary
| Term | Definition |
|---|
| Quality Grading Skill | A skill that evaluates artifacts across multiple dimensions using a 1-5 scoring scale |
| Dimension | A distinct aspect of quality being evaluated (Design Quality, Originality, Craft, Functionality) |
| Artifact Type | The category of artifact being graded: code, specification, or design |
| Few-Shot Calibration | Providing example artifacts with known scores to guide consistent grading |
| Self-Healing | The skill's ability to auto-fix quality gaps without user prompting |
| Grade-and-Fix Mode | Invocation mode where grading and improvement are performed sequentially without user input |
Examples
Example: Evaluate Code Quality
User: Grade this implementation using quality-grading skill
Artifact: src/services/auth.ts
Mode: evaluate
Example: Grade Design Document
User: Assess the quality of this architecture design
Artifact: docs/architecture/system-design.md
Mode: grade-and-fix
Example: Evaluate Specification
User: Grade this requirements document
Artifact: specs/changes/feature/requirements.md
Mode: evaluate
Example: Grade and Fix
User: Grade and improve this module
Artifact: src/utils/
Mode: grade-and-fix
