If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionwrite-queryExecute the skills CLI command in your project's root directory to begin installation:
Fetches write-query from anthropics/knowledge-work-plugins 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 write-query. Access via /write-query 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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If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Write a SQL query from a natural language description, optimized for your specific SQL dialect and following best practices.
/write-query <description of what data you need>
Parse the user's description to identify:
If the user's SQL dialect is not already known, ask which they use:
Remember the dialect for future queries in the same session.
If a data warehouse MCP server is connected:
Follow these best practices:
Structure:
daily_signups, active_users, revenue_by_product)Performance:
SELECT * in production queries -- specify only needed columnsEXISTS over IN for subqueries with large result setsReadability:
a, b, c)Dialect-specific optimizations:
sql-queries skill for details)Provide:
If a data warehouse is connected, offer to run the query and analyze the results. If the user wants to run it themselves, the query is ready to copy-paste.
Simple aggregation:
/write-query Count of orders by status for the last 30 days
Complex analysis:
/write-query Cohort retention analysis -- group users by their signup month, then show what percentage are still active (had at least one event) at 1, 3, 6, and 12 months after signup
Performance-critical:
/write-query We have a 500M row events table partitioned by date. Find the top 100 users by event count in the last 7 days with their most recent event type.
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.
mattpocock/skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Useful defaults in write-query — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for write-query matched our evaluation — installs cleanly and behaves as described in the markdown.
write-query has been reliable in day-to-day use. Documentation quality is above average for community skills.
write-query reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend write-query for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for write-query matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in write-query — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
write-query fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend write-query for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added write-query from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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