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Hivemind Graph

OrganizationPopular
activeloopai
hivemind-graph

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's the architecture / which subsystems exist?", "what's the impact of changing this?". The graph is an AST-derived map of the repo, queried as files (no build needed — it rebuilds automatically).

Overview

Publisheractiveloopai
Repositoryhivemind
Skill namehivemind-graph
Stars
1.6K
Forks
107
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by activeloopai on GitHub. Read the source before you install it.

Installation

Install the Hivemind Graph AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/activeloopai/hivemind.git /tmp/hivemind
mkdir -p .claude/skills
cp -r /tmp/hivemind/harnesses/claude-code/skills/hivemind-graph .claude/skills/hivemind-graph
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hivemind Graph in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hivemind Graph on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hivemind Graph is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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/<symbol> (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

bash
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

Workflow

  1. Broad? Start at index.md to see subsystems and the biggest files.
  2. Looking for a symbol? find/<name> (or query/<name>) → pick the handle.
  3. Want relationships? show/<handle> / neighborhood/<file> → callers/callees, imports.
  4. Tracing a flow? path/<from>/<to>. Change impact? impact/<symbol>.
  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.

Frequently asked questions

What does the Hivemind Graph AI skill do?

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's the architecture / which subsystems exist?", "what's the impact of changing this?". The graph is an AST-derived map of the repo, queried as files (no build needed — it rebuilds automatically).

Why use Hivemind Graph on TypingMind?

Because you install it once and use it with any model. Hivemind Graph is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hivemind Graph in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/activeloopai/hivemind/tree/main/harnesses/claude-code/skills/hivemind-graph. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hivemind Graph?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hivemind Graph?

As many as you like. As long as a model supports skills, you can use Hivemind Graph with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hivemind Graph AI skill free?

Yes. It is published on GitHub by activeloopai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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