Query the local code graph (functions, classes, calls, imports) through the Deeplake mount at memory/graph/. Use when the user asks structural questions about the codebase — "what calls X?", "what does Y import?", "where is Z defined?", "what is the architecture / which subsystems exist?". The graph is an AST-derived map of the repo, queried as files (no build needed — it rebuilds automatically).
npx skills add https://github.com/activeloopai/hivemind --skill hivemind-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.
Activate when the user asks a *structural / relational* question about the code:
pushSnapshot?" / "Who uses this function?"deeplake-pull.ts import?" / "What depends on X?"GraphSnapshot defined?" / "Find the function that handles Y."impact/<symbol> (transitive blast radius)Read on the realsource file. The graph gives location + relationships, not full source.
If a file's mtime is newer than the build timestamp, read the live source.
extractor covers those three, with cross-file calls/imports resolved for
named imports. For anything else, fall back to grep/read.
cat ~/.deeplake/memory/graph/index.md
# Overview: node/edge counts, kind breakdown, top files by node count.
cat ~/.deeplake/memory/graph/query/<pattern> # START HERE (the 2-in-1)
# Search + expand the top matches with their 1-hop neighbors (callers,
# callees, imports, heritage). Multi-token AND: query/<a>+<b>.
cat ~/.deeplake/memory/graph/find/<pattern>
# 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/<handle-or-pattern>
# <handle>: a digit from a prior find/ (e.g. 3).
# <pattern>: 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/<file>
# Every symbol in a file + its cross-file neighbors (callers/callees/imports).
cat ~/.deeplake/memory/graph/impact/<pattern>
# Transitive dependents — the blast radius of changing a symbol.
cat ~/.deeplake/memory/graph/path/<from>/<to>
# 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
index.md to see subsystems and the biggest files.find/<name> (or query/<name>) → pick the handle.show/<handle> / neighborhood/<file> → callers/callees, imports.path/<from>/<to>. Change impact? impact/<symbol>.source_file:line and Read it — don't answer from the graph alone.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.
SessionStart inject prints the build age; if it's old or you've just edited a
file, prefer the live source for that file.
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.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling
Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification
Take activeloopai/hivemind-hermes-hivemind-graph from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.