code-quality▌
tursodatabase/turso · updated Apr 10, 2026
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Production-grade code standards emphasizing correctness, crash-safety, and Rust idioms over convenience.
- ›Prioritizes data integrity: crash on invalid state rather than corrupt data; assert invariants aggressively; handle all errors explicitly
- ›Rust-specific patterns: make illegal states unrepresentable, use exhaustive pattern matching and enums over strings, minimize allocations
- ›Comments document why , not what ; avoid temporal markers, AI conversation references, and code-repeating e
Code Quality Guide
Core Principle
Production database. Correctness paramount. Crash > corrupt.
Correctness Rules
- No workarounds or quick hacks. Handle all errors, check invariants
- Assert often. Never silently fail or swallow edge cases
- Crash on invalid state if it risks data integrity. Don't continue in undefined state
- Consider edge cases. On long enough timeline, all possible bugs will happen
Rust Patterns
- Make illegal states unrepresentable
- Exhaustive pattern matching
- Prefer enums over strings/sentinels
- Minimize heap allocations
- Write CPU-friendly code (microsecond = long time)
If-Statements
Wrong:
if condition {
// happy path
} else {
// "shouldn't happen" - silently ignored
}
Right:
// If only one branch should ever be hit:
assert!(condition, "invariant violated: ...");
// OR
return Err(LimboError::InternalError("unexpected state".into()));
// OR
unreachable!("impossible state: ...");
Use if-statements only when both branches are expected paths.
Comments
Do:
- Document WHY, not what
- Document functions, structs, enums, variants
- Focus on why something is necessary
Don't:
- Comments that repeat code
- References to AI conversations ("This test should trigger the bug")
- Temporal markers ("added", "existing code", "Phase 1")
Avoid Over-Engineering
- Only changes directly requested or clearly necessary
- Don't add features beyond what's asked
- Don't add docstrings/comments to unchanged code
- Don't add error handling for impossible scenarios
- Don't create abstractions for one-time operations
- Three similar lines > premature abstraction
Index Mutations
When code involves index inserts, deletes, or conflict resolution, double-check the ordering against SQLite. Wrong ordering causes index inconsistencies. and easy to miss.
Ensure understanding of IO model
Cleanup
- Delete unused code completely
- No backwards-compat hacks (renamed
_vars, re-exports,// removedcomments)
How to use code-quality on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add code-quality
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches code-quality from GitHub repository tursodatabase/turso and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate code-quality. Access the skill through slash commands (e.g., /code-quality) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★60 reviews- ★★★★★Harper Bhatia· Dec 28, 2024
code-quality reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Chen Malhotra· Dec 28, 2024
code-quality fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ganesh Mohane· Dec 24, 2024
We added code-quality from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Nikhil Okafor· Dec 16, 2024
We added code-quality from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Luis Bansal· Dec 8, 2024
Solid pick for teams standardizing on skills: code-quality is focused, and the summary matches what you get after install.
- ★★★★★Luis Anderson· Dec 4, 2024
code-quality has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Li Anderson· Dec 4, 2024
Useful defaults in code-quality — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Henry Perez· Nov 27, 2024
I recommend code-quality for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Aanya Torres· Nov 23, 2024
We added code-quality from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Hiroshi Park· Nov 19, 2024
Registry listing for code-quality matched our evaluation — installs cleanly and behaves as described in the markdown.
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