| name | knowledge_graph |
| description | Generate synthetic graph counts, ranked artificial entities, and relationship paths from input strings. Use when testing the Knowledge Graph response contract, not when analyzing real source code. Trigger with simulate knowledge graph. |
| allowed-tools | Bash(python:*), Bash(oss-lab:*) |
| version | 0.2.0 |
| author | Intent Solutions <jeremy@intentsolutions.io> |
| license | MIT |
| compatibility | Requires Python 3.11+ and an OSS Agent Lab checkout installed with pip install -e .; inputs are hashed but never read, cloned, parsed, persisted, or queried through GitNexus or cognee. |
| tags | ["knowledge-graph","graph-rag","simulation","offline","prototype"] |
| argument-hint | [query] [--source STRING] [--graph-type code|document|mixed] |
| model | inherit |
| effort | low |
| display_name | Knowledge Graph Specialist |
| source_repo | abhigyanpatwari/GitNexus |
| tier | core |
| capabilities | ["knowledge_graph","code_analysis","entity_linking","graph_rag"] |
| allowed_tools | ["build_graph","query_graph","find_relationships"] |
| output_formats | ["python_api","cli","mcp_server","agent_skill","rest_api"] |
Knowledge Graph Specialist
Overview
knowledge_graph wraps patterns from
abhigyanpatwari/GitNexus for code
entity extraction and graph construction, and
topoteretes/cognee for Graph RAG retrieval.
Given any source string, the specialist hashes that string and constructs a synthetic graph-shaped
response. It returns artificial entity lists, relevance scores, and relationship paths for exercising
downstream contracts. It does not parse or cite the named artifact.
The specialist is stateless. Each execute call creates a new graph UUID and self-contained result;
there is no previously built graph to retrieve across calls.
Those descriptions are interface goals, not current data processing. The implementation hashes the
source string and generates artificial counts, entities, scores, and paths; it has no stored graph.
Prerequisites
- Use Python 3.11+ in a local OSS Agent Lab checkout and run
pip install -e ..
- Pass only a source identifier or sample text; paths and URLs are not opened.
- Read the runtime contract before using the response.
Capabilities
- knowledge_graph: Generate graph-shaped counts and type labels from a source-string hash.
- code_analysis: Exercise a code-analysis response schema with artificial entities.
- entity_linking: Generate deterministic-style IDs and synthetic paths for contract tests.
- graph_rag: Exercise ranking and path fields without retrieval or generation.
Tools
| Tool | Description | Side Effects |
|---|
build_graph | Derive synthetic graph metadata from a source string | None |
query_graph | Generate artificial ranked entities and paths | None |
find_relationships | Generate artificial paths, types, and strength | None |
Parameters
Request-level (intent.parameters)
