reflection▌
davidkiss/smart-ai-skills · updated Apr 8, 2026
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Analyzes conversation patterns and tool usage to propose targeted skill improvements or user preferences.
- ›Reviews conversation history and tool failures to identify gaps in skill definitions or recurring user preferences
- ›Proposes one change at a time (either a skill update shown as a diff or a preference addition to CLAUDE.md ) for focused review
- ›Requires explicit user confirmation before applying any changes to skills or preference files
- ›Prioritizes failure analysis and user corr
Reflection Skill
Overview
This skill is used to learn from interaction with the user and failures in tool calls. It analyzes what worked, what didn't (tool failures), and identifies recurring patterns or explicit user preferences that should be formalized.
Objectives
- Improve Skills: Identify gaps or inefficiencies in existing skill definitions and propose concise updates.
- Store Preferences: Capture user preferences, project-specific rules, or recurring instructions in a
AGENT.mdorCLAUDE.md(when used in Claude Code) file.
Process
- Analyze: Review the conversation history, tool calls, and any failures or corrections from the user.
- Identify: Determine if a specific behavior should be codified in a skill or if a user preference has emerged.
- Propose: Formulate a single, concise change.
- If updating a skill, show a diff of the proposed change.
- If adding a preference, show the proposed addition to
CLAUDE.md.
- Confirm: Present the proposal to the user and ask for explicit confirmation without making any changes first.
- Apply Changes: Once user confirmed the changes, only then apply them
Guidelines
- One at a time: Only propose one change per invocation to maintain focus and allow for careful review.
- Conciseness: Keep changes as brief as possible. Often a few words are enough to clarify a requirement or fix a common mistake.
- Accuracy: Ensure the proposal directly addresses a real issue or preference observed in the session.
- Specificity: Think how you could make the learnings more generic to apply to other use cases, but don't make the changes too generic so that it would not address the original learnings
- Failure Analysis: Pay special attention to tool failures or when the user has to correct your approach. These are primary candidates for reflection.
- Conflict Resolution: If a proposed change conflicts with details of an existing skill or user preference, propose a resolution that best serves the user's current intent.
How to use reflection on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add reflection
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches reflection from GitHub repository davidkiss/smart-ai-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate reflection. Access the skill through slash commands (e.g., /reflection) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
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
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★30 reviews- ★★★★★Shikha Mishra· Dec 28, 2024
reflection has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Alexander Gill· Dec 28, 2024
Useful defaults in reflection — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Mateo Abbas· Dec 20, 2024
We added reflection from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Rahul Santra· Nov 19, 2024
reflection reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Mateo Ramirez· Nov 11, 2024
reflection fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Pratham Ware· Oct 10, 2024
We added reflection from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Valentina Kapoor· Oct 2, 2024
reflection has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Anaya Torres· Sep 17, 2024
reflection is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Anaya Anderson· Aug 8, 2024
Solid pick for teams standardizing on skills: reflection is focused, and the summary matches what you get after install.
- ★★★★★Alexander Iyer· Jul 27, 2024
I recommend reflection for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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