agent-memory▌
yamadashy/repomix · updated Apr 8, 2026
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A persistent memory space for storing knowledge that survives across conversations.
Agent Memory
A persistent memory space for storing knowledge that survives across conversations.
Location: .claude/skills/agent-memory/memories/
Proactive Usage
Save memories when you discover something worth preserving:
- Research findings that took effort to uncover
- Non-obvious patterns or gotchas in the codebase
- Solutions to tricky problems
- Architectural decisions and their rationale
- In-progress work that may be resumed later
Check memories when starting related work:
- Before investigating a problem area
- When working on a feature you've touched before
- When resuming work after a conversation break
Organize memories when needed:
- Consolidate scattered memories on the same topic
- Remove outdated or superseded information
- Update status field when work completes, gets blocked, or is abandoned
Folder Structure
When possible, organize memories into category folders. No predefined structure - create categories that make sense for the content.
Guidelines:
- Use kebab-case for folder and file names
- Consolidate or reorganize as the knowledge base evolves
Example:
memories/
├── file-processing/
│ └── large-file-memory-issue.md
├── dependencies/
│ └── iconv-esm-problem.md
└── project-context/
└── december-2025-work.md
This is just an example. Structure freely based on actual content.
Frontmatter
All memories must include frontmatter with a summary field. The summary should be concise enough to determine whether to read the full content.
Summary is the decision point: Agents scan summaries via rg "^summary:" to decide which memories to read in full. Write summaries that contain enough context to make this decision - what the memory is about, the key problem or topic, and why it matters.
Required:
---
summary: "1-2 line description of what this memory contains"
created: 2025-01-15 # YYYY-MM-DD format
---
Optional:
---
summary: "Worker thread memory leak during large file processing - cause and solution"
created: 2025-01-15
updated: 2025-01-20
status: in-progress # in-progress | resolved | blocked | abandoned
tags: [performance, worker, memory-leak]
related: [src/core/file/fileProcessor.ts]
---
Search Workflow
Use summary-first approach to efficiently find relevant memories:
# 1. List categories
ls .claude/skills/agent-memory/memories/
# 2. View all summaries
rg "^summary:" .claude/skills/agent-memory/memories/ --no-ignore --hidden
# 3. Search summaries for keyword
rg "^summary:.*keyword" .claude/skills/agent-memory/memories/ --no-ignore --hidden -i
# 4. Search by tag
rg "^tags:.*keyword" .claude/skills/agent-memory/memories/ --no-ignore --hidden -i
# 5. Full-text search (when summary search isn't enough)
rg "keyword" .claude/skills/agent-memory/memories/ --no-ignore --hidden -i
# 6. Read specific memory file if relevant
Note: Memory files are gitignored, so use --no-ignore and --hidden flags with ripgrep.
Operations
Save
- Determine appropriate category for the content
- Check if existing category fits, or create new one
- Write file with required frontmatter (use
date +%Y-%m-%dfor current date)
mkdir -p .claude/skills/agent-memory/memories/category-name/
# Note: Check if file exists before writing to avoid accidental overwrites
cat > .claude/skills/agent-memory/memories/category-name/filename.md << 'EOF'
---
summary: "Brief description of this memory"
created: 2025-01-15
---
# Title
Content here...
EOF
Maintain
- Update: When information changes, update the content and add
updatedfield to frontmatter - Delete: Remove memories that are no longer relevant
trash .claude/skills/agent-memory/memories/category-name/filename.md # Remove empty category folders rmdir .claude/skills/agent-memory/memories/category-name/ 2>/dev/null || true - Consolidate: Merge related memories when they grow
- Reorganize: Move memories to better-fitting categories as the knowledge base evolves
Guidelines
- Write for resumption: Memories exist to resume work later. Capture all key points needed to continue without losing context - decisions made, reasons why, current state, and next steps.
- Write self-contained notes: Include full context so the reader needs no prior knowledge to understand and act on the content
- Keep summaries decisive: Reading the summary should tell you if you need the details
- Stay current: Update or delete outdated information
- Be practical: Save what's actually useful, not everything
Content Reference
When writing detailed memories, consider including:
- Context: Goal, background, constraints
- State: What's done, in progress, or blocked
- Details: Key files, commands, code snippets
- Next steps: What to do next, open questions
Not all memories need all sections - use what's relevant.
How to use agent-memory 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 agent-memory
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches agent-memory from GitHub repository yamadashy/repomix 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 agent-memory. Access the skill through slash commands (e.g., /agent-memory) 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
4.6★★★★★43 reviews- ★★★★★Mei Kapoor· Dec 28, 2024
We added agent-memory from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Layla Garcia· Dec 24, 2024
I recommend agent-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Yusuf Tandon· Dec 20, 2024
Keeps context tight: agent-memory is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ganesh Mohane· Dec 12, 2024
Keeps context tight: agent-memory is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Zara Khan· Dec 4, 2024
agent-memory has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Zara Torres· Nov 23, 2024
agent-memory fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Tariq Dixit· Nov 19, 2024
Solid pick for teams standardizing on skills: agent-memory is focused, and the summary matches what you get after install.
- ★★★★★Dev Kapoor· Nov 11, 2024
Registry listing for agent-memory matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Rahul Santra· Nov 3, 2024
Registry listing for agent-memory matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Pratham Ware· Oct 22, 2024
agent-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.
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