Quality Checklist Generator

Overview

Generate comprehensive, context-aware quality checklists combining IEEE 1028 standards (80-90%) with LLM-generated contextual items (10-20%). Ensures systematic quality validation before completion.

Core principle:

Universal quality standards enhanced with project-specific context.

When to Use

Always:

Before completing implementation tasks

During code review

QA validation phase

Pre-commit verification

Before marking tasks complete

Exceptions:

Throwaway prototypes

Configuration-only changes

Purpose

IEEE 1028 Base

Proven quality review standards (universal)

Contextual Enhancement

LLM adds project-specific items

Systematic Validation

Ensures comprehensive quality coverage IEEE 1028 Base Categories The following categories are included in every checklist (80-90% of items): Code Quality Code follows project style guide No code duplication (DRY principle) Cyclomatic complexity < 10 per function Functions have single responsibility Variable names are clear and descriptive Magic numbers replaced with named constants Dead code removed Testing Tests written first (TDD followed) All new code has corresponding tests Tests cover edge cases and error conditions Test coverage ≥ 80% for new code Integration tests for multi-component interactions Tests are isolated and don't depend on order Security Input validation on all user inputs No SQL injection vulnerabilities No XSS vulnerabilities Sensitive data encrypted at rest and in transit Authentication and authorization checks present No hardcoded secrets or credentials OWASP Top 10 considered Performance No obvious performance bottlenecks Database queries optimized (no N+1 queries) Appropriate caching used Resource cleanup (close connections, release memory) No infinite loops or recursion risks Large data operations paginated Documentation Public APIs documented Complex logic has explanatory comments README updated if needed CHANGELOG updated Breaking changes documented Architecture diagrams updated if structure changed Error Handling All error conditions handled User-friendly error messages (4xx for user errors) Detailed logs for debugging (5xx for system errors) No swallowed exceptions Graceful degradation implemented Rollback procedures for failures Contextual Addition Logic The skill analyzes the current project context to add 10-20% contextual items: Detection Strategy Read project files to identify: Framework (React, Vue, Angular, Next.js, FastAPI, etc.) Language (TypeScript, Python, Go, Java, etc.) Patterns (REST API, GraphQL, microservices, monolith) Infrastructure (Docker, Kubernetes, serverless) Generate contextual items based on findings: TypeScript Projects: [AI-GENERATED] TypeScript types exported properly [AI-GENERATED] No any types unless justified with comment [AI-GENERATED] Strict null checks satisfied React Projects: [AI-GENERATED] Components use proper memo/useCallback [AI-GENERATED] No unnecessary re-renders (React DevTools checked) [AI-GENERATED] Hooks follow Rules of Hooks [AI-GENERATED] Accessibility attributes (aria-*) on interactive elements API Projects: [AI-GENERATED] Rate limiting implemented [AI-GENERATED] API versioning strategy followed [AI-GENERATED] OpenAPI/Swagger docs updated [AI-GENERATED] Request/response validation with schemas Database Projects: [AI-GENERATED] Migration scripts reversible [AI-GENERATED] Indexes added for query performance [AI-GENERATED] Database transactions used appropriately [AI-GENERATED] Connection pooling configured Python Projects: [AI-GENERATED] Type hints on all public functions [AI-GENERATED] Docstrings follow Google/NumPy style [AI-GENERATED] Virtual environment requirements.txt updated Mobile Projects: [AI-GENERATED] Offline mode handled gracefully [AI-GENERATED] Battery usage optimized (no constant polling) [AI-GENERATED] Data usage minimized (compression, caching) [AI-GENERATED] Platform-specific features tested DevOps/Infrastructure: [AI-GENERATED] Infrastructure as code (Terraform, CloudFormation) [AI-GENERATED] Monitoring and alerting configured [AI-GENERATED] Backup and disaster recovery tested [AI-GENERATED] Security groups/firewall rules minimal access Output Format Checklists are returned as markdown with checkboxes:

Quality Checklist Generated: {timestamp} Context: {detected frameworks/languages}

Code Quality (IEEE 1028)

[ ] Code follows project style guide

[ ] No code duplication

[ ] Cyclomatic complexity < 10

Testing (IEEE 1028)

[ ] Tests written first (TDD followed)

[ ] Test coverage ≥ 80%

[ ] Tests cover edge cases

Security (IEEE 1028)

[ ] Input validation on all user inputs

[ ] No hardcoded secrets

[ ] OWASP Top 10 considered

Performance (IEEE 1028)

[ ] No obvious performance bottlenecks

[ ] Database queries optimized

[ ] Appropriate caching used

Documentation (IEEE 1028)

[ ] Public APIs documented

[ ] README updated if needed

[ ] CHANGELOG updated

Error Handling (IEEE 1028)

[ ] All error conditions handled

[ ] User-friendly error messages

[ ] Detailed logs for debugging

Context-Specific Items (AI-Generated) {Detected: TypeScript + React + REST API} - [ ] [ AI-GENERATED ] TypeScript types exported properly - [ ] [ AI-GENERATED ] React components use proper memo - [ ] [ AI-GENERATED ] API rate limiting implemented - [ ] [ AI-GENERATED ] OpenAPI docs updated

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Total Items

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{count}

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IEEE Base

**

{ieee_count} ({percentage}%)

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Contextual

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{contextual_count} ({percentage}%) Usage Basic Invocation Skill ( { skill : 'checklist-generator' } ) ; This will: Analyze current project context Load IEEE 1028 base checklist Generate contextual items (10-20%) Return combined markdown checklist With Specific Context // Provide explicit context Skill ( { skill : 'checklist-generator' , args : 'typescript react api' , } ) ; Integration with QA Workflow // Part of QA validation Skill ( { skill : 'checklist-generator' } ) ; // Use checklist for systematic validation Skill ( { skill : 'qa-workflow' } ) ; Integration Points QA Agent The qa agent uses this skill for validation: Generate checklist at task start Validate each item systematically Report checklist completion status Verification-Before-Completion Used as pre-completion gate: Generate checklist before marking task complete Ensure all items verified Block completion if critical items fail Code-Reviewer Agent Used during code review: Generate checklist for PR Check each item against changes Comment on missing items Context Detection Algorithm 1. Read package.json or requirements.txt or go.mod → Extract dependencies 2. Glob for framework-specific files: - React: /*.jsx, /.tsx, package.json with "react" - Vue: /.vue, package.json with "vue" - Next.js: next.config.js, app/, pages/ - FastAPI: /main.py with "from fastapi" - Django: /settings.py, /models.py 3. Analyze imports/dependencies: - TypeScript: tsconfig.json - GraphQL: /.graphql, /.gql - Docker: Dockerfile, docker-compose.yml - Kubernetes: /.yaml in k8s/ or manifests/ 4. Generate contextual items based on detected stack 5. Mark all generated items with [AI-GENERATED] Example: TypeScript + React + API Project Input Context: package.json contains: "react": "^18.0.0", "typescript": "^5.0.0" Files include: src/components/.tsx , src/api/*.ts Generated Checklist:

Quality Checklist Generated: 2026-01-28 10:30:00 Context: TypeScript, React, REST API

Code Quality (IEEE 1028)

[ ] Code follows project style guide

[ ] No code duplication

[ ] Cyclomatic complexity < 10

[ ] Functions have single responsibility

[ ] Variable names clear and descriptive

[ ] Magic numbers replaced with constants

[ ] Dead code removed

Testing (IEEE 1028)

[ ] Tests written first (TDD)

[ ] All new code has tests

[ ] Tests cover edge cases

[ ] Test coverage ≥ 80%

[ ] Integration tests present

[ ] Tests isolated (no order dependency)

Security (IEEE 1028)

[ ] Input validation on all inputs

[ ] No SQL injection risks

[ ] No XSS vulnerabilities

[ ] Sensitive data encrypted

[ ] Auth/authz checks present

[ ] No hardcoded secrets

[ ] OWASP Top 10 reviewed

Performance (IEEE 1028)

[ ] No performance bottlenecks

[ ] Database queries optimized

[ ] Caching used appropriately

[ ] Resource cleanup (connections)

[ ] No infinite loop risks

[ ] Large data paginated

Documentation (IEEE 1028)

[ ] Public APIs documented

[ ] Complex logic has comments

[ ] README updated

[ ] CHANGELOG updated

[ ] Breaking changes documented

Error Handling (IEEE 1028)

[ ] All errors handled

[ ] User-friendly error messages

[ ] Detailed logs for debugging

[ ] No swallowed exceptions

[ ] Graceful degradation

[ ] Rollback procedures

TypeScript (AI-Generated)

[ ] [ AI-GENERATED ] Types exported from modules - [ ] [ AI-GENERATED ] No any types (justified if used) - [ ] [ AI-GENERATED ] Strict null checks satisfied - [ ] [ AI-GENERATED ] Interfaces prefer over types

React (AI-GENERATED)

[ ] [ AI-GENERATED ] Components use React.memo appropriately - [ ] [ AI-GENERATED ] Hooks follow Rules of Hooks - [ ] [ AI-GENERATED ] No unnecessary re-renders - [ ] [ AI-GENERATED ] Keys on list items

REST API (AI-GENERATED)

[ ] [ AI-GENERATED ] Rate limiting implemented - [ ] [ AI-GENERATED ] API versioning in URLs - [ ] [ AI-GENERATED ] Request/response validation - [ ] [ AI-GENERATED ] OpenAPI/Swagger updated

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Total Items

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38

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IEEE Base

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30 (79%)

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Contextual

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8 (21%) Best Practices DO Start with IEEE 1028 base (universal quality) Analyze project context before generating Mark all LLM items with [AI-GENERATED] Keep contextual items focused (10-20%) Return actionable checklist (not generic advice) DON'T Generate checklist without context analysis Exceed 20% contextual items (dilutes IEEE base) Forget [AI-GENERATED] prefix Include items that can't be verified Make checklist too long (>50 items) Iron Laws NEVER complete a task without checklist validation — use verification-before-completion to enforce this gate; skipping checklist validation defeats the entire purpose of the skill. ALWAYS analyze project context before generating the checklist — detect framework, language, and patterns first; a generic checklist without context misses stack-specific items and includes irrelevant ones. NEVER exceed 50 checklist items — checklists longer than 50 items are not completed in practice; be ruthlessly selective and prioritize blocking items over nice-to-haves. ALWAYS mark LLM-generated items with [AI-GENERATED] prefix — IEEE 1028 base items are verified standards; AI-generated items are contextual suggestions requiring human judgment. NEVER include non-verifiable items — every checklist item must be objectively checkable with a yes/no answer; vague items like "code is clean" cannot be definitively validated. Anti-Patterns Anti-Pattern Why It Fails Correct Approach Generating without context analysis Produces generic, irrelevant checklist items Detect framework/language/patterns first Exceeding 50 items Long checklists are abandoned mid-review Cut ruthlessly; keep to ≤50 high-value items No [AI-GENERATED] prefix on contextual items Conflates standards with suggestions Mark all AI-contextual items clearly Non-verifiable items ("code is clean") Can't be definitively checked off Only include objectively pass/fail items Skipping checklist for "small" changes Small changes introduce large bugs Apply checklist proportionally for every change