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 agentmods add skills/activeloopai/hivemind/hivemind-graphnpx skills add activeloopai/hivemind --skill hivemind-graphgit clone --depth 1 https://github.com/activeloopai/hivemindWhat 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 | $0.00105 | $0.01248 |
| Opus 5 | $0.00053 | $0.00624 |
| Sonnet 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 2d 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.
- 2d ago First seen · 95 lines · 105 tokens per session scan A 13c13a44e901
hivemind-graph is a skill published in the GitHub repository activeloopai/hivemind (1,593 stars, last pushed yesterday), licensed Apache-2.0. It adds 105 tokens to every session and 1,248 once invoked, about $0.0005 per session on Opus 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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