memory-reflect▌
basicmachines-co/basic-memory-skills · updated Apr 8, 2026
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Review recent activity and consolidate valuable insights into long-term memory.
Memory Reflect
Review recent activity and consolidate valuable insights into long-term memory.
Inspired by sleep-time compute — the idea that memory formation happens best between active sessions, not during them.
When to Run
- Cron/heartbeat: Schedule as a periodic background task (recommended: 1-2x daily)
- On demand: User asks to reflect, consolidate, or review recent memory
- Post-compaction: After context window compaction events
Process
1. Gather Recent Material
Find what changed recently, then read the relevant files:
# Find recently modified notes — use json format for the complete list
# (text format truncates to ~5 items in the summary)
recent_activity(timeframe="2d", output_format="json")
# Read specific daily notes
read_note(identifier="memory/2026-02-27")
read_note(identifier="memory/2026-02-26")
# Check active tasks
search_notes(note_types=["task"], status="active")
2. Evaluate What Matters
For each piece of information, ask:
- Is this a decision that affects future work? → Keep
- Is this a lesson learned or mistake to avoid? → Keep
- Is this a preference or working style insight? → Keep
- Is this a relationship detail (who does what, contact info)? → Keep
- Is this transient (weather checked, heartbeat ran, routine task)? → Skip
- Is this already captured in MEMORY.md or another long-term file? → Skip
3. Update Long-Term Memory
Write consolidated insights to MEMORY.md following its existing structure:
- Add new sections or update existing ones
- Use concise, factual language
- Include dates for temporal context
- Remove or update outdated entries that the new information supersedes
4. Log the Reflection
Append a brief entry to today's daily note:
## Reflection (HH:MM)
- Reviewed: [list of files reviewed]
- Added to MEMORY.md: [brief summary of what was consolidated]
- Removed/updated: [anything cleaned up]
Guidelines
- Be selective. The goal is distillation, not duplication. MEMORY.md should be curated wisdom, not a copy of daily notes.
- Preserve voice. If the agent has a personality/soul file, reflections should match that voice.
- Don't delete daily notes. They're the raw record. Reflection extracts from them; it doesn't replace them.
- Merge, don't append. If MEMORY.md already has a section about a topic, update it in place rather than adding a duplicate entry.
- Flag uncertainty. If something seems important but you're not sure, add it with a note like "(needs confirmation)" rather than skipping it entirely.
- Restructure over time. If MEMORY.md is a chronological dump, restructure it into topical sections during reflection. Curated knowledge > raw logs.
- Check for filesystem issues. Look for recursive nesting (memory/memory/memory/...), orphaned files, or bloat while gathering material.
How to use memory-reflect 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 memory-reflect
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches memory-reflect from GitHub repository basicmachines-co/basic-memory-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 memory-reflect. Access the skill through slash commands (e.g., /memory-reflect) 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
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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
4.8★★★★★45 reviews- ★★★★★Ganesh Mohane· Dec 12, 2024
memory-reflect fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Noah Ndlovu· Dec 12, 2024
Registry listing for memory-reflect matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Ama Chawla· Dec 4, 2024
memory-reflect fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Michael Bhatia· Nov 23, 2024
Registry listing for memory-reflect matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Anika Malhotra· Nov 23, 2024
Useful defaults in memory-reflect — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Sakshi Patil· Nov 3, 2024
Registry listing for memory-reflect matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Anaya Khanna· Nov 3, 2024
memory-reflect fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Chaitanya Patil· Oct 22, 2024
memory-reflect reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Sophia Shah· Oct 22, 2024
We added memory-reflect from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Kiara Gill· Oct 18, 2024
memory-reflect fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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