agent-memory-mcp
Enable Persistent Memory for AI Agents
This skill provides AI agents with persistent, searchable memory to retain architecture decisions, patterns, and knowledge across sessions without manual tracking.
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Verwendung von "agent-memory-mcp". Search for all stored patterns related to authentication
Erwartetes Ergebnis:
Found 3 memories:
- auth-oauth2: OAuth2 implementation pattern
- auth-jwt: JWT token handling pattern
- auth-sessions: Session management pattern
Verwendung von "agent-memory-mcp". Write a new architecture decision about database choice
Erwartetes Ergebnis:
Memory saved successfully:
Key: db-choice-postgres
Type: decision
Content: Selected PostgreSQL for its relational integrity and JSON support
Tags: database, architecture, production
Verwendung von "agent-memory-mcp". Show memory statistics
Erwartetes Ergebnis:
Memory Stats:
- Total memories: 24
- Types: decisions(8), patterns(12), knowledge(4)
- Top tags: authentication(5), database(4), api(3)
Sicherheitsaudit
SicherAll static findings are false positives. The skill contains only documentation with setup instructions (git clone, npm commands). The flagged 'external_commands' are legitimate bash commands in documentation, not executable code. The flagged 'network' URLs are documentation references (GitHub repo URL, localhost dashboard). The flagged 'cryptographic' terms ('key', 'risk') are common English words used in their normal sense, not cryptographic algorithms.
Qualitätsbewertung
Was du bauen kannst
Retain Architecture Decisions
Store and retrieve architectural choices made during project setup so future sessions understand why certain patterns were selected.
Build Pattern Library
Collect and search reusable code patterns, snippets, and best practices discovered during development.
Track Project Context
Maintain persistent context about project goals, constraints, and conventions across agent sessions.
Probiere diese Prompts
Use memory_write to store this architecture decision: key: '{decision_key}', type: 'decision', content: '{decision_content}', tags: ['{tag1}', '{tag2}']Search the memory for authentication patterns using memory_search with query: 'authentication', type: 'pattern'
Retrieve the design discussion about '{topic}' using memory_read with key: '{memory_key}'Show memory statistics using memory_stats to understand what knowledge has been stored
Bewährte Verfahren
- Use consistent naming conventions for memory keys to make retrieval predictable
- Add relevant tags when writing memories to improve searchability
- Periodically review memory_stats to ensure the knowledge base remains organized
Vermeiden
- Storing sensitive information like API keys or passwords in memory
- Creating duplicate memories with slightly different keys instead of updating existing ones
- Using overly generic search queries that return too many unrelated results