A persistent memory space for storing knowledge that survives across conversations.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionagent-memoryExecute the skills CLI command in your project's root directory to begin installation:
Fetches agent-memory from yamadashy/repomix 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 agent-memory. Access via /agent-memory 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.
Submit your Claude Code skill and start earning
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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A persistent memory space for storing knowledge that survives across conversations.
Location: .claude/skills/agent-memory/memories/
Save memories when you discover something worth preserving:
Check memories when starting related work:
Organize memories when needed:
When possible, organize memories into category folders. No predefined structure - create categories that make sense for the content.
Guidelines:
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.
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]
---
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.
date +%Y-%m-%d for 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
updated field to frontmattertrash .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
When writing detailed memories, consider including:
Not all memories need all sections - use what's relevant.
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
ailabs-393/ai-labs-claude-skills
We added agent-memory from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend agent-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: agent-memory is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: agent-memory is the kind of skill you can hand to a new teammate without a long onboarding doc.
agent-memory has been reliable in day-to-day use. Documentation quality is above average for community skills.
agent-memory fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: agent-memory is focused, and the summary matches what you get after install.
Registry listing for agent-memory matched our evaluation — installs cleanly and behaves as described in the markdown.
Registry listing for agent-memory matched our evaluation — installs cleanly and behaves as described in the markdown.
agent-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.
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