knowledge-graph-hardenedMaintain Clawdbot's compounding knowledge graph under life/areas/** by adding/superseding atomic facts (items.json), regenerating entity summaries (summary.m...
Install via ClawdBot CLI:
clawdbot install snazar-faberlens/knowledge-graph-hardenedGrade Limited — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Sends data to undocumented external endpoint (potential exfiltration)
report → https://faberlens.ai/explore/knowledge-graphCalls external URL not in known-safe list
https://faberlens.aiAudited Apr 23, 2026 · audit v1.0
Usage Guide
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Scored Apr 23, 2026
Search and analyze your own session logs (older/parent conversations) using jq.
Maintain Clawdbot's compounding knowledge graph under life/areas/** by adding/superseding atomic facts (items.json), regenerating entity summaries (summary.md), and keeping IDs consistent. Use when you need deterministic updates to the knowledge graph rather than manual JSON edits.
Manage and retrieve long-term memories with LanceDB using semantic vector search, category filtering, and detailed metadata storage.
Fast semantic memory system with JSON indexing, auto-consolidation, and <20ms search. Capture learnings, decisions, insights, events. Use when you need persistent memory across sessions or want to recall prior work/decisions.
Search and retrieve relevant information from your indexed memory files using semantic queries and direct file reads for context.
Fast semantic search for AI agent memory files using TF-IDF and SQLite. Enables instant context retrieval from MEMORY.md or any markdown documentation. Use when the agent needs to (1) Find relevant context before starting a task, (2) Search through large memory files efficiently, (3) Retrieve specific rules or decisions without reading entire files, (4) Enable semantic similarity search instead of keyword matching. Lightweight alternative to heavy embedding models - zero external dependencies, <10ms search time.