Universal code quality tool supporting 70+ linters for 40+ languages via qlty CLI.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionqlty-checkExecute the skills CLI command in your project's root directory to begin installation:
Fetches qlty-check from parcadei/continuous-claude-v3 and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate qlty-check. Access via /qlty-check in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Universal code quality tool supporting 70+ linters for 40+ languages via qlty CLI.
# Check changed files with auto-fix
uv run python -m runtime.harness scripts/qlty_check.py --fix
# Check all files
uv run python -m runtime.harness scripts/qlty_check.py --all
# Format files
uv run python -m runtime.harness scripts/qlty_check.py --fmt
# Get metrics
uv run python -m runtime.harness scripts/qlty_check.py --metrics
# Find code smells
uv run python -m runtime.harness scripts/qlty_check.py --smells
| Parameter | Description |
|---|---|
--check |
Run linters (default) |
--fix |
Auto-fix issues |
--all |
Process all files, not just changed |
--fmt |
Format files instead |
--metrics |
Calculate code metrics |
--smells |
Find code smells |
--paths |
Specific files/directories |
--level |
Min issue level: note/low/medium/high |
--cwd |
Working directory |
--init |
Initialize qlty in a repo |
--plugins |
List available plugins |
# Auto-fix what's possible, see what remains
uv run python -m runtime.harness scripts/qlty_check.py --fix
# Get metrics for changed code
uv run python -m runtime.harness scripts/qlty_check.py --metrics
# Find complexity hotspots
uv run python -m runtime.harness scripts/qlty_check.py --smells
uv run python -m runtime.harness scripts/qlty_check.py --init --cwd /path/to/repo
# Check changed files
qlty check
# Auto-fix
qlty check --fix
# JSON output
qlty check --json
# Format
qlty fmt
servers/qlty/server.py wraps CLI.qlty/qlty.toml in repo (run qlty init first)| Tool | Use Case |
|---|---|
| qlty | Unified linting, formatting, metrics for any language |
| ast-grep | Structural code patterns and refactoring |
| morph | Fast text search |
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
parcadei/continuous-claude-v3
mattpocock/skills
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
qlty-check has been reliable in day-to-day use. Documentation quality is above average for community skills.
qlty-check fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
qlty-check fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
qlty-check is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in qlty-check — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for qlty-check matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: qlty-check is focused, and the summary matches what you get after install.
Keeps context tight: qlty-check is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for qlty-check matched our evaluation — installs cleanly and behaves as described in the markdown.
qlty-check reduced setup friction for our internal harness; good balance of opinion and flexibility.
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