lindoelio/spec-driven-steroids/quality-grading

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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 5 without prompting. Invoke when you want consistent quality assessment on design, implementation, or specification with actionable feedback and self-healing improvements.

Install

npx vskill@latest install lindoelio/spec-driven-steroids/quality-grading

SKILL.md

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 5, re-grade, and provide final results

The skill targets score 5+ as the quality bar. Auto-fix attempts are limited to 3 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 5 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
  1. Load artifact: Read the target file, all files in the directory, or parse the snippet
  2. Detect artifact type: Determine if this is code, a specification, or a design document
  3. Apply dimension rubrics: Score each of the four dimensions independently, adapting criteria to artifact type
  4. Use calibration examples: Compare against few-shot examples for consistency
  5. If grade-and-fix mode and score < 5: Apply targeted auto-fix, re-grade, repeat up to 3 times
  6. 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:

ScoreDescriptor
5Exceptional architecture with clear module boundaries, proven scalability patterns, strong separation of concerns
4Good architecture with clear modules, adequate scalability consideration, minor separation issues
3Adequate architecture with recognizable modules, moderate separation of concerns
2Weak architecture with unclear module boundaries, limited separation of concerns
1Poor architecture with tangled dependencies, no clear modules, intermingled concerns

Auto-Fix (score < 5):

  • 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:

ScoreDescriptor
5Highly tailored solution with novel approaches directly mapped to the problem domain
4Mostly tailored solution with some unique adaptations to the problem
3Mix of generic and tailored approaches
2Mostly generic with minimal adaptation to the problem
1Generic, copy-paste solution with no adaptation to the specific problem

Auto-Fix (score < 5):

  • 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:

ScoreDescriptor
5Exceptionally clean code with comprehensive error handling and exemplary documentation
4Clean code with good error handling and documentation with minor gaps
3Adequate code quality with some error handling and documentation
2Messy code with limited error handling and minimal documentation
1Messy, error-prone code with little to no documentation

Auto-Fix (score < 5):

  • 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:

ScoreDescriptor
5Complete feature implementation with comprehensive edge case handling
4Full implementation with minor edge case gaps
3Most features implemented with some edge cases handled
2Partial implementation with several unhandled edge cases
1Missing features and unhandled edge cases causing frequent failures

Auto-Fix (score < 5):

  • 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:

  1. Trigger: Auto-fix activates for any dimension scoring below 5
  2. Fix Strategy: Apply minimal, targeted changes that address the specific gap without introducing unnecessary complexity
  3. Re-grade: After each fix, re-score to verify improvement
  4. Attempts: Maximum 2 auto-fix attempts per dimension
  5. Fallback: If auto-fix fails to achieve score 5+, record actionable suggestions instead of blocking
  6. 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:

json
{
"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": {
"score": 1-5,
"justification": "<brief explanation of the score>",
"improved": "<null | description of auto-fix applied>"
},
"functionality": {
"score": 1-5,
"justification": "<brief explanation of the score>",
"improved": "<null | description of auto-fix applied>"
}
},
"overallScore": "<average of dimensions, displayed as X.Y>",
"summary": "<overall assessment text>",
"suggestions": {
"<dimension>": "<actionable improvement if score < 5 and auto-fix failed>"
}
}

Glossary

TermDefinition
Quality Grading SkillA skill that evaluates artifacts across multiple dimensions using a 1-5 scoring scale
DimensionA distinct aspect of quality being evaluated (Design Quality, Originality, Craft, Functionality)
Artifact TypeThe category of artifact being graded: code, specification, or design
Few-Shot CalibrationProviding example artifacts with known scores to guide consistent grading
Self-HealingThe skill's ability to auto-fix quality gaps without user prompting
Grade-and-Fix ModeInvocation 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

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
54
Repository forks
3
7-day trend score
20

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 13 Jul 2026, 3 months ago.

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