MollyGraph
MollyGraph builds a knowledge graph that improves its own extraction model over time. It uses a three-layer NER pipeline (GLiNER2, spaCy enrichment, and GLiREL relation extraction) with speaker-anchored ingestion and per-source confidence thresholds. Queries run graph exact-match and vector similarity searches in parallel, merging and deduplicating results. The system includes an automated LoRA fine-tuning loop that only deploys new models when they beat the current one in A/B benchmarks, plus an LLM-powered audit chain for relationship verification. Backed by Neo4j and Jina embeddings, fully configured via environment variables.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-02.
Scan details: Circle-IR · 2026-09-02 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- devops-ci
- Source
- PulseMCP
- Repository
- github.com/brianmeyer/mollygraph
- Author type
- human
- Last scanned
- 2026-09-02
- Updated
- 2026-09-02
Use via MCP
Resolve MollyGraph from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.