Automatically extract reusable patterns from Claude Code sessions and save them as learned skills.
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
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
Submit your Claude Code skill and start earning
epx add affaan-m/everything-claude-code --skill continuous-learningCompatible with
Supported agents
Runs as a Stop hook at session end to evaluate transcripts, detect patterns, and save skills to ~/.claude/skills/learned/
Configurable pattern detection across five categories: error resolution, user corrections, workarounds, debugging techniques, and project-specific conventions
Customizable via config.json with minimum session length, extraction thresholds, and pattern ignore lists
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncontinuous-learningExecute the skills CLI command in your project's root directory to begin installation:
epx add affaan-m/everything-claude-code --skill continuous-learningFetches continuous-learning from affaan-m/everything-claude-code 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:
.cursor/skills/continuous-learning
Restart Cursor to activate continuous-learning. Access via /continuous-learningin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Time estimate: 30-60 minutes to see productivity improvements
Prerequisites
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.
video-editing
affaan-m/everything-claude-code · Video · same repo
java-coding-standards
affaan-m/everything-claude-code · Backend · same repo
grill-me
mattpocock/skills · Productivity · same category
framer-motion
pproenca/dot-skills · Productivity · same category
pagespeed-insights
enderpuentes/ai-agent-skills · Productivity · same category
web-accessibility
supercent-io/skills-template · Productivity · same category
Keeps context tight: continuous-learning is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for continuous-learning matched our evaluation — installs cleanly and behaves as described in the markdown.
We added continuous-learning from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
continuous-learning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in continuous-learning — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
continuous-learning has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in continuous-learning — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend continuous-learning for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
continuous-learning reduced setup friction for our internal harness; good balance of opinion and flexibility.
continuous-learning fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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