Dimensionality reduction techniques reduce the number of features while preserving important information, improving model efficiency and enabling visualization of high-dimensional data.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondimensionality-reductionExecute the skills CLI command in your project's root directory to begin installation:
Fetches dimensionality-reduction from aj-geddes/useful-ai-prompts 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 dimensionality-reduction. Access via /dimensionality-reduction 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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total installs
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this week
162
GitHub stars
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upvotes
Run in your terminal
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installs
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this week
162
stars
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
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
jezweb/claude-skills
Registry listing for dimensionality-reduction matched our evaluation — installs cleanly and behaves as described in the markdown.
We added dimensionality-reduction from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
dimensionality-reduction reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for dimensionality-reduction matched our evaluation — installs cleanly and behaves as described in the markdown.
dimensionality-reduction is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
dimensionality-reduction reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: dimensionality-reduction is the kind of skill you can hand to a new teammate without a long onboarding doc.
dimensionality-reduction has been reliable in day-to-day use. Documentation quality is above average for community skills.
dimensionality-reduction fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in dimensionality-reduction — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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