KnowTrust-Graph is an agent-agnostic tool that combines knowledge management with a dynamic verification engine to track the reliability of entities and facts.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install knowtrust-graph
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install knowtrust-graph using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is KnowTrust-Graph?
KnowTrust-Graph is a sophisticated knowledge management framework designed for Openclaw Skills that bridges the gap between raw data and verifiable truth. It operates on a three-layer model: Entities, Relations, and Facts, ensuring that every piece of information is not just recorded but also qualified by its source and verification level.
The skill is built on a philosophy of Human-led, AI-assisted interaction, where the AI organizes and suggests connections while humans maintain decision-making authority. It is completely lightweight, requiring only Python and SQLite, making it ideal for developers and small teams who need a robust, local-first knowledge base without the overhead of complex vector databases or external cloud dependencies.
KnowTrust-Graph Use Cases
- Managing personal project knowledge, learning notes, and decision logs.
- Enabling small team information sharing (3-10 people) with branch support.
- Tracking the verification status and expiration of critical business constraints.
- Automating structured knowledge extraction from long chat dialogues or technical documentation.
How KnowTrust-Graph Works
- Entity & Fact Definition: Users or AI agents define entities (people, projects, concepts) and attach atomic facts to them.
- Trust Chain Attribution: Every fact is linked to a source and assigned an initial verification state (unverified).
- Dynamic Verification: As facts are checked, their state evolves from self_checked to peer_reviewed or external_confirmed based on team consensus.
- Lifecycle Management: The system tracks the freshness of data, marking old information as outdated or stale to trigger updates.
- Query & Exploration: Users search or traverse the graph using CLI commands to retrieve reliable, verified knowledge.
KnowTrust-Graph Setup
Ensure you have Python 3.8+ installed. The skill uses standard libraries and SQLite.
# Create the knowledge directory
mkdir -p ~/.zhixin
# Initialize the database
python3 ~/.zhixin/kg.py init
# Check status
python3 ~/.zhixin/kg.py stats
KnowTrust-Graph Data Schema & Taxonomy
KnowTrust-Graph organizes data into a relational structure optimized for traceability:
| Component | Description | Metadata Examples |
|---|---|---|
| Entity | Core nodes in the graph | Type (person, project, tool), Description |
| Relation | Directed edges between entities | Type (works_on, depends_on, created_by) |
| Fact | Atomic knowledge statements | Content, Fact Type, Freshness Date |
| Source | The origin of the knowledge | Reliability Rating, Author |
| Annotation | Team feedback/validation | Type (confirm, dispute, clarify) |
KnowTrust-Graph Advanced Features
- Multi-Agent Protocol: Standardized CLI interface compatible with Openclaw Skills, Claude Code, and Continue.dev.
- Semantic Search: Optional integration with Ollama for local vector-based embedding and semantic retrieval.
- Branching System: Create isolated knowledge branches for different projects or experimental data.
- Consensus Engine: Automatic calculation of Verifiability scores based on source reliability, verification depth, and consensus ratio.
SKILL.md
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