Hivemind is a shared memory system for coding agents that records their sessions and turns recurring solutions into reusable skills. Teams use it to let agents search past traces, share learned procedures across tools and machines, and keep the resulting knowledge in their own cloud or on-premises storage. The catalogue entries are Hivemind's own skills, hooks, plugins, and commands for connecting agents to that shared memory.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add activeloopai/hivemind --skill hivemind-graphgit clone --depth 1 https://github.com/activeloopai/hivemindWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/activeloopai/hivemind/hivemind-graph)<a href="https://agentmods.dev/skills/activeloopai/hivemind/hivemind-graph"><img src="https://agentmods.dev/badge/skills/activeloopai/hivemind/hivemind-graph/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/activeloopai/hivemind/hivemind-graph"><img src="https://agentmods.dev/badge/skills/activeloopai/hivemind/hivemind-graph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00105 | $0.01248 |
| Opus 5.5 | $0.00042 | $0.00499 |
| Sonnet 5.5 | $0.00021 | $0.00250 |
| Haiku 4.5 | $0.00011 | $0.00125 |
Grade A, and why
hivemind-graph scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.tsimport?" / "What depends on X?" - "Where is
GraphSnapshotdefined?" / "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
Readon 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/importsresolved 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/<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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 95 lines · 105 tokens per session scan A 13c13a44e901
hivemind-graph is a skill published in the GitHub repository activeloopai/hivemind (1,629 stars, last pushed yesterday), licensed Apache-2.0. It adds 105 tokens to every session and 1,248 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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