You use Claude Code daily. Your context fills up fast, Read pulls in too much source, long Bash outputs blow your budget, and the bill is adding up. You want both retrieval-side and consumption-side optimization.
pip install neuralmind
cd your-project
neuralmind build . # builds the knowledge graph + vector index
neuralmind install-hooks . # PostToolUse compression (Read/Bash/Grep)
neuralmind init-hook . # auto-rebuild on every git commit
At session start, have Claude Code call:
neuralmind_wakeup(project_path=".")
That gives the agent ~400 tokens of architecture/cluster context instead of 50K tokens of file reads.
When asking a code question, prefer neuralmind_query over raw exploration:
neuralmind_query(project_path=".", question="How does authentication flow through the middleware?")
Returns ~800–1,100 tokens with the right clusters and search hits.
Before opening a file, use neuralmind_skeleton:
neuralmind_skeleton(project_path=".", file_path="src/auth/handlers.py")
Returns the function list, rationales, call graph, and cross-file edges — ~88% cheaper than Read.
Everything else (Read, Bash, Grep you don’t route through NeuralMind) is automatically compressed by the hooks. You don’t have to think about it.
| Before | After |
|---|---|
| Session starts with “let me explore the repo” + 20 file reads | wakeup loads orientation in one call |
| Asking about a flow = reading 5 files end-to-end | query returns the relevant slice |
npm test dumps 800 lines into the agent |
Hook keeps errors + last 3 lines (~91% smaller) |
grep -r "foo" floods with 200 matches |
Capped at 25 with “N more hidden” pointer |
| Every commit drifts the index | post-commit hook rebuilds incrementally |
| “What should I open next?” is guesswork (v0.11.0+) | neuralmind next . path/to/file.py returns the files most often edited after this one, ranked by probability |
Once neuralmind watch has been running for a few sessions, the
directional synapse layer accumulates ordered (from_file, to_file)
transitions and the agent can ask:
$ neuralmind next . src/auth/handlers.py
After src/auth/handlers.py:
45.2% tests/test_auth.py
28.4% src/auth/middleware.py
12.1% docs/auth.md
Same data via the neuralmind_next_likely MCP tool — Claude Code
can call it right after finishing edits in one file to surface the
files you usually touch next, no manual prompt needed.
Need the raw file body for a specific command?
NEURALMIND_BYPASS=1 <your command>
Tune hook thresholds via env vars — see PostToolUse Compression.
Combined retrieval + consumption reduction is typically 5–10× vs vanilla Claude Code on the same tasks. Run neuralmind benchmark . --json on your repo for a concrete number.
Pop a separate terminal and run:
neuralmind serve .
Open the URL it prints. You now have an Obsidian-style graph view
of your codebase that updates in real time as Claude works. Every
time Claude calls neuralmind_query (or any other NeuralMind tool),
the relevant nodes pulse on the canvas — animated radial rings,
color-coded by event source. The sidebar shows a rolling log of
the most recent ~80 events.
The use this unlocks is trust-gap closure:
| You wonder… | The graph view answers in ~2 seconds |
|---|---|
| Is Claude looking at the right code? | Watch which nodes pulse during the prompt |
| Did the retrieval miss something obvious? | Use the replay-last-query overlay to see the L3 hits |
| Why did this answer feel wrong? | Pulse pattern usually shows it — wrong cluster, missing edge, unexpected hub |
| Has the synapse layer learned anything yet? | Hover the synapse edges; weight + activation count appear |
Pin the nodes you want to keep in focus (the visible pin glyph
shows pinned state at a glance), use the depth slider (1–3 hops)
to see how far the agent’s retrieval reached, and use Cmd/Ctrl-K
or / to jump-to-search from anywhere.
NEURALMIND_EVENT_LOG=0 disables the cross-process bridge if you
prefer the in-process feed only.
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