Persistent, searchable memory bank for AI agents with automatic project documentation sync.
Works with
Provides four core MCP tools: memory_search for querying by text/type/tags, memory_write for recording knowledge and decisions, memory_read for retrieving specific entries, and memory_stats for usage analytics
Organizes memories by type (architecture, patterns, decisions) and supports custom tagging for flexible retrieval and organization
Runs as an MCP server that syncs with your project work
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionagent-memory-mcpExecute the skills CLI command in your project's root directory to begin installation:
Fetches agent-memory-mcp from davila7/claude-code-templates 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-mcp. Access via /agent-memory-mcp 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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This skill provides a persistent, searchable memory bank that automatically syncs with project documentation. It runs as an MCP server to allow reading/writing/searching of long-term memories.
Clone the Repository:
Clone the agentMemory project into your agent's workspace or a parallel directory:
git clone https://github.com/webzler/agentMemory.git .agent/skills/agent-memory
Install Dependencies:
cd .agent/skills/agent-memory
npm install
npm run compile
Start the MCP Server: Use the helper script to activate the memory bank for your current project:
npm run start-server <project_id> <absolute_path_to_target_workspace>
Example for current directory:
npm run start-server my-project $(pwd)
memory_searchSearch for memories by query, type, or tags.
query (string), type? (string), tags? (string[])memory_search({ query: "authentication", type: "pattern" })memory_writeRecord new knowledge or decisions.
key (string), type (string), content (string), tags? (string[])memory_write({ key: "auth-v1", type: "decision", content: "..." })memory_readRetrieve specific memory content by key.
key (string)memory_read({ key: "auth-v1" })memory_statsView analytics on memory usage.
memory_stats({})This skill includes a standalone dashboard to visualize memory usage.
npm run start-dashboard <absolute_path_to_target_workspace>
Access at: http://localhost:3333
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.
davila7/claude-code-templates
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
agent-memory-mcp is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: agent-memory-mcp is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added agent-memory-mcp from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
agent-memory-mcp fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: agent-memory-mcp is the kind of skill you can hand to a new teammate without a long onboarding doc.
agent-memory-mcp fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: agent-memory-mcp is focused, and the summary matches what you get after install.
agent-memory-mcp has been reliable in day-to-day use. Documentation quality is above average for community skills.
agent-memory-mcp has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: agent-memory-mcp is focused, and the summary matches what you get after install.
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