Hivemind Graph

Hivemind Code Graph

A deterministic, AST-derived map of the current repository — every function, class, method, interface, type, enum, const, and module, plus the edges between them (calls, imports, extends, implements, method_of). It is queried as synthesized files under the Deeplake mount; there are no real files on disk and no network call in the read path.

The graph builds and refreshes automatically (on Stop / SessionEnd, gated by a rate limit + git diff). You never run a build command — just read it.

Use it as a fast INDEX to locate the few files/symbols that matter, then open them with Read to answer. It is not a substitute for the source.

When to use this skill

Activate when the user asks a structural / relational question about the code:

• "What calls pushSnapshot?" / "Who uses this function?"
• "What does deeplake-pull.ts import?" / "What depends on X?"
• "Where is GraphSnapshot defined?" / "Find the function that handles Y."
• "What are the main subsystems / the architecture here?"
• "If I change this signature, what's affected?" → use impact/ (transitive blast radius)

When NOT to use this skill

• Reading the body of a symbol you already located → use Read on the real

source file. The graph gives location + relationships, not full source.

• Code that isn't committed/built yet — the graph can lag uncommitted edits.

If a file's mtime is newer than the build timestamp, read the live source.

• Languages outside TypeScript, JavaScript, and Python (Go, Rust, …) — the

extractor covers those three, with cross-file calls/imports resolved for named imports. For anything else, fall back to grep/read.

Path cheat sheet

cat ~/.deeplake/memory/graph/index.md
#   Overview: node/edge counts, kind breakdown, top files by node count.

cat ~/.deeplake/memory/graph/query/   # START HERE (the 2-in-1)
#   Search + expand the top matches with their 1-hop neighbors (callers,
#   callees, imports, heritage). Multi-token AND: query/+.

cat ~/.deeplake/memory/graph/find/
#   Case-insensitive substring search on node id + label (max 50 hits).
#   Prints numbered handles [1] [2] ... saved for this worktree.

cat ~/.deeplake/memory/graph/show/
#   : a digit from a prior find/ (e.g. 3).
#   : a substring → unique node detail, or a candidate list.
#   Output: the node + its 1-hop neighbors grouped by edge relation.

cat ~/.deeplake/memory/graph/neighborhood/
#   Every symbol in a file + its cross-file neighbors (callers/callees/imports).

cat ~/.deeplake/memory/graph/impact/
#   Transitive dependents — the blast radius of changing a symbol.

cat ~/.deeplake/memory/graph/path//
#   Shortest dependency path between two symbol patterns (trace a flow across files).

cat ~/.deeplake/memory/graph/layers      # architectural layers / subsystems
cat ~/.deeplake/memory/graph/tour        # deterministic guided walkthrough

Workflow

1. Broad? Start at index.md to see subsystems and the biggest files.
2. Looking for a symbol? find/ (or query/) → pick the handle.
3. Want relationships? show/ / neighborhood/ → callers/callees, imports.
4. Tracing a flow? path//. Change impact? impact/.
5. Need the actual code? Take the source_file:line and Read it — don't answer from the graph alone.

Anti-patterns (read these)

• "Incoming (0)" does NOT mean dead code. Cross-file calls are resolved for

named imports (TS/JS/Python), but instance-method dispatch (obj.method()), dynamic calls, and nested/inner functions are NOT — a zero-incoming symbol may still be reached via one of those. Confirm in the source before calling it unused.

• The graph can be stale. It rebuilds at most once per rate-limit window. The

SessionStart inject prints the build age; if it's old or you've just edited a file, prefer the live source for that file.

• Don't try to build it. There is no user-facing build step in normal use;

the hooks handle it. Just read the mount.

• find/ is lexical, not semantic. It matches substrings, not meaning —

find/auth won't surface login/credentials unless those strings appear in the id/label. Try multiple keywords if the first misses.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: activeloopai
• Source: activeloopai/hivemind
• License: Apache-2.0
• Homepage: https://deeplake.ai/hivemind

Install and usage instructions live in the source repository linked above.