auto-updater automatically applies improvements to skills and ecosystem components based on identified patterns and learnings.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionauto-updaterExecute the skills CLI command in your project's root directory to begin installation:
Fetches auto-updater from adaptationio/skrillz 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 auto-updater. Access via /auto-updater 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.
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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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auto-updater automatically applies improvements to skills and ecosystem components based on identified patterns and learnings.
Purpose: Automated application of validated improvements across ecosystem
The 5-Step Auto-Update Workflow:
Safety: Always validates before finalizing, can rollback
Sources:
Output: List of potential improvements
Time: 15-30 minutes
Safe to Automate:
NOT Safe to Automate:
Output: Classified improvements (auto-safe vs manual-only)
Time: 20-40 minutes
Process:
Approach: One skill at a time, validate each before moving to next
Time: Varies by improvement and skill count
For Each Updated Skill:
Output: Validation results per skill
Time: 10-15 minutes per skill
If Validation Fails:
Output: Rolled back skill, failure analysis
Auto-Update: Add Quick Reference to All Skills Missing It
Step 1: Identify
- Improvements: Add Quick Reference section
- Target Skills: planning-architect, task-development, todo-management
- Count: 3 skills to update
Step 2: Assess Safety
- ✅ Safe: Adding new section (doesn't modify existing content)
- ✅ Safe: Standard format (use template)
- ✅ Safe: Low risk (can validate easily)
- Decision: Auto-update approved
Step 3: Apply
- Backup: Git commit all 3 skills
- Apply to planning-architect: ✅ Success
- Apply to task-development: ✅ Success
- Apply to todo-management: ✅ Success
- Changes: 3/3 skills updated
Step 4: Validate
- planning-architect: 5/5 structure (maintained)
- task-development: 5/5 structure (maintained)
- todo-management: 5/5 structure (maintained)
- All validations: ✅ PASS
Step 5: Rollback
- Not needed (all validations passed)
Result: ✅ 3 skills successfully auto-updated
Time: 90 minutes (vs 3-4 hours manual)
Impact: 100% Quick Reference coverage achieved
Quality: All skills maintained 5/5 scores
| Step | Focus | Time | Safety |
|---|---|---|---|
| Identify | Gather improvements | 15-30m | N/A |
| Assess Safety | Classify auto-safe | 20-40m | Critical |
| Apply | Implement changes | Varies | Backup first |
| Validate | Check quality maintained | 10-15m/skill | Essential |
| Rollback | Revert if fails | 5m/skill | Safety net |
Safe to Automate:
NOT Safe:
Rule: If requires judgment or understanding → Manual only
auto-updater enables safe, validated, automated improvement application across multiple skills simultaneously.
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
pproenca/dot-skills
mattpocock/skills
auto-updater has been reliable in day-to-day use. Documentation quality is above average for community skills.
auto-updater reduced setup friction for our internal harness; good balance of opinion and flexibility.
auto-updater reduced setup friction for our internal harness; good balance of opinion and flexibility.
auto-updater fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend auto-updater for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
I recommend auto-updater for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added auto-updater from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in auto-updater — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in auto-updater — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: auto-updater is the kind of skill you can hand to a new teammate without a long onboarding doc.
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