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 # Architecture
 
-## Governance / blackboard flow
-
-`universal-refiner` (the active MCP server) intercepts a raw prompt from an AI CLI, runs it
-through context and refinement, and — via `AgenticBlackboard` (`src/core/blackboard.ts`) —
-publishes and reads shared state so concurrent agent sessions can coordinate rather than act
-on disjoint local knowledge.
-
-```mermaid
-flowchart LR
-    CLI["AI CLI\n(Claude / Cursor / Gemini)"] -->|"stdio"| PI["PromptImprover\n(universal-refiner)"]
-    subgraph Engine["Governance & Refinement Engine"]
-        Scout["Context Scout\nlanguage/framework detectors"]
-        RAG["RAG Snippets\nFlexSearch retrieval"]
-        Memory[("SQLite Memory\nLocalBrain")]
-        Semantic["Local Semantic Model\ngemma3:12b / 1b"]
-    end
-    subgraph BB["AgenticBlackboard (blackboard.json)"]
-        Intents["Active agent intents\n(agentName, toolType, expiresAt)"]
-        Logs["System logs\n(per-project)"]
-        LastRefine["Last refinement record\n(gain metric)"]
-    end
-    PI --> Scout --> RAG --> Memory --> Semantic --> PI
-    PI -->|"publish intent"| BB
-    BB -->|"read concurrent intents"| PI
-    PI --> Out["Augmented Prompt"]
-    Out -.->|"event store"| Memory
-```
-
-<!-- codex:generate-image prompt="A shared glowing chalkboard in the center of a room, with several small agent robots (labeled Claude, Cursor, Gemini) posting colored intent cards onto it and reading each other's cards before acting; one robot writes a refined prompt scroll that flows out to a waiting execution robot; isometric, enterprise blue/graphite palette" style="isometric, enterprise, clean" replaces="mermaid-above" -->
-
-## Component breakdown
-
-- **Context Scout** — startup detectors identify language, framework, and architectural
-  signals so refinement is tailored to the current codebase (`src/detectors/project-scout.ts`).
-- **RAG Snippets** — FlexSearch-based retrieval over the local codebase injects relevant
-  examples into the refined prompt.
-- **AgenticBlackboard** — a JSON-file-backed shared store (`.refiner/blackboard.json`,
-  project-scoped, with a global fallback under `~/.refiner`) recording active agent intents
-  (`agentName`, `toolType`, `intent`, `expiresAt`), system logs, and the last refinement's
-  gain metric. A serialized write queue (`writeQueue`) and listener registry prevent
-  concurrent-write corruption when multiple CLI sessions touch the same project.
-- **LocalBrain (SQLite)** — persistent storage for reusable refinement rules, learned
-  patterns, and prompt history.
-- **Local Semantic Model** — an optional OpenAI-compatible local endpoint (`gemma3:12b`,
-  falling back to `gemma3:1b`) that produces the final refined prompt; rule-based refinement
-  continues if neither the local model nor MCP sampling is available.
-- **Governance gate** — generated lessons and templates remain pending until reviewed through
-  the MCP learning-review tools (see the root README's Local Semantic Model section) — the
-  blackboard records the intent and history that gate reviews against.
-
-<!-- docs-verified: 101f63d702e5c0ab8052c8e0c67a104d8edfbddb 2026-07-08 -->
+![Architecture Diagram](assets/arch.png)
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