Cgh — AI skill for Claude Code

**Local code graph, shared memory and guardrails for AI coding assistants.**

Parses your repo into a graph of files, functions, classes, Terraform resources, and Markdown documentation -- then exposes it as an MCP server so Claude Code, Cursor, Codex, Gemini, and IBM Bob can do symbol-level lookups instead of reading entire files. On top of the graph: a knowledge and session memory every connected agent shares, and a confidentiality layer (findings, egress gate, per-agent guard hooks) that decides what an agent may read and what may reach a cloud model.

**Result:** 40-60% fewer context tokens on typical navigation tasks, learnings that survive context clears, and nothing leaving the machine without a gate.

pip install cgh && cgh init && cgh serve

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Why cgh: the measured gains

cgh's job is to keep an agent's working context small and its round-trips few. It answers code questions from the graph, returning exact `file:line`, instead of the agent reading whole files or grepping. That shows up as fewer context tokens and fewer turns, at equal correctness.

The figures below come from a two-arm benchmark: the same tasks run twice against the same repo, once with cgh available and once with Read/Grep only, scored on each session's token usage, turn count, and answer correctness. Cost is compared only across tasks both arms got right, so a cheap wrong answer never reads as a saving.

**On code-navigation tasks: about 40 to 60% fewer context tokens and 20 to 40% fewer turns, correctness unchanged.**

The gap is widest on multi-file questions, where a graph beats text search. For "what breaks if I change `_backend`?", the agent has to follow call edges across a module:

	turns	context tokens
Read / Grep	13	2607
cgh	6	886

Tha