TokenKnows — distill AI coding sessions into weekly reports, ADRs and a knowledge graph
About
Distill AI coding sessions (Claude Code / Codex / Cursor) into weekly reports, ADRs, incident reviews and a knowledge graph — local-first, evidence-linked.
Details
- Author
- johnnywuj81
- GitHub stars
- 2
- Downloads
- 174
- Categories
- AI, Knowledge Base, Developer Tools, Automation
Jump to
- Captures AI coding sessions from Claude Code, Codex, Cursor, and more.
- Distills into seven asset types including weekly reports, ADRs, and incident reviews.
- Every paragraph traces back to original PR, conversation, or commit.
- Local-first with three-layer LLM egress gate for privacy.
- Can run fully offline with Ollama and zero cloud keys.
- All collectors run locally without webhooks or tunnels.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
TokenKnows — distill AI coding sessions into weekly reports, ADRs and a knowledge graphCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install the TokenKnows backend (FastAPI) and web UI, then add the MCP plugin for your AI tool (Claude Code, Codex, Cursor, or VS Code). The plugin provides MCP tools like submit_session_events and distill_document, plus slash commands like /tokenknows:weekly. Run the backend with uvicorn app.main:app and frontend with npm run dev.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"tokenknows \u2014 distill ai coding sessions into weekly reports, adrs and a knowledge graph": {
"tokenknows": {
"command": "python3",
"args": [
"-m",
"venv",
".venv",
"&&",
".venv/bin/pip",
"install",
"-e",
".[dev]"
]
}
}
}
}McpServers
{
"tokenknows": {
"command": "python3",
"args": [
"-m",
"venv",
".venv",
"&&",
".venv/bin/pip",
"install",
"-e",
".[dev]"
]
}
}What is TokenKnows?
You spend hours pair-programming with Claude Code, Codex, and Cursor. The decisions, bug hunts, and design trade-offs from those sessions evaporate the moment the terminal closes. TokenKnows captures them automatically and distills them into structured, evidence-linked knowledge assets:
capture (6 collectors) → distill (5-stage LLM pipeline) → assets (7 document types) → review / redact / publish
- 📡 Captures everything — Claude Code, Codex, Cursor, VS Code, GitHub PRs/commits/issues, and local docs, all via local file watchers and API polling. No webhooks, no tunnels.
- 📝 Seven asset types — weekly reports, tech designs, ADRs, incident reviews, long-form books, reusable agent skills (SKILL.md), and an entity knowledge graph.
- 🔗 Evidence-linked — every paragraph traces back to the original PR / conversation / commit, ranked by cosine × trust × recency across ≥2 sources.
- 🔒 Local-first, zero egress by default — a three-layer LLM egress gate (instance ∧ project ∧ task) with full audit logging. Pair it with Ollama and run the whole pipeline with zero cloud keys.
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