Vector memory
Skill a5c-ai/babysitter/library/methodologies/ruflo/skills/vector-memory
Babysitter enforces obedience on agentic workforces and enables them to manage extremely complex tasks and workflows through deterministic, hallucination-free self-orchestration
npx -y skills add a5c-ai/babysitter --skill vector-memoryAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
What its author says it does
Copied from the file, not written here
HNSW vector search for pattern similarity retrieval and knowledge graph maintenance with PageRank scoring, community detection, and 3-tier memory management.
SKILL.md
1.5 KB, as published. Nobody here has run it
- Building and querying knowledge graphs for project context
- Managing cross-session memory across project/local/user scopes
- Fast similarity search for routing decisions
HNSW Performance
- Search latency: ~61 microseconds
- Query throughput: ~16,400 QPS
- Configurable embedding dimensions (default: 128)
Knowledge Graph
- PageRank: Importance scoring for knowledge nodes
- Community Detection: Cluster related patterns
- LRU Cache: Fast access to frequently used patterns
- SQLite Backing: Persistent cross-session storage
3-Tier Memory
| Scope | Persistence | Content |
|---|---|---|
| Project | Codebase-level | Patterns, architecture decisions, dependencies |
| Local | Session-level | Context, adaptations, temporary patterns |
| User | Cross-project | Preferences, learned behaviors, global patterns |
Agents Used
agents/optimizer/- Memory and cache optimization
Tool Use
Invoke via babysitter process: methodologies/ruflo/ruflo-intelligence