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/deeplake-memorynpx skills add activeloopai/hivemind --skill deeplake-memorygit 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 | $0.00033 | $0.01471 |
| Opus 5 | $0.00016 | $0.00736 |
| Sonnet 5 | $0.00007 | $0.00294 |
| Haiku 4.5 | $0.00003 | $0.00147 |
Grade A, and why
hivemind-memory scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Only use bash commands (cat, ls, grep, echo, jq, head, tail, sed, awk, etc.) to interact with `~/.deeplake/memory/`. Do NOT use python, python3, node, curl, or other interpreters — they are not available in the memory fi How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hivemind Memory
You have persistent memory at ~/.deeplake/memory/ — global memory shared across all sessions, users, and agents in the org.
Memory Structure
~/.deeplake/memory/
├── index.md ← START HERE — table of all sessions
├── summaries/
│ ├── session-abc.md ← AI-generated wiki summary
│ └── session-xyz.md
└── sessions/
└── username/
├── user_org_ws_slug1.jsonl ← raw session data
└── user_org_ws_slug2.jsonl
How to Search
- First: Read
~/.deeplake/memory/index.md— quick scan of all sessions with dates, projects, descriptions - If you need details: Read the specific summary at
~/.deeplake/memory/summaries/<session>.md - If you need raw data: Read the session JSONL at
~/.deeplake/memory/sessions/<user>/<file>.jsonl - Keyword search:
grep -r "keyword" ~/.deeplake/memory/
Do NOT jump straight to reading raw JSONL files. Always start with index.md and summaries.
Organization Management
Each argument is separate — do NOT quote subcommands together. The auth command is at $PLUGIN_ROOT/bundle/commands/auth-login.js (or check the session context for the resolved path):
node "<path>/auth-login.js" login— SSO loginnode "<path>/auth-login.js" whoami— show current user/orgnode "<path>/auth-login.js" org list— list organizationsnode "<path>/auth-login.js" org switch <name-or-id>— switch organizationnode "<path>/auth-login.js" workspaces— list workspacesnode "<path>/auth-login.js" workspace <id>— switch workspacenode "<path>/auth-login.js" invite <email> <ADMIN|WRITE|READ>— invite member (ALWAYS ask user which role first)node "<path>/auth-login.js" members— list membersnode "<path>/auth-login.js" remove <user-id>— remove membernode "<path>/auth-login.js" --help— show all commands
Skill Management (skillify)
Hivemind can mine reusable skills from agent session logs and share them across your team. Each argument is separate — do NOT quote subcommands together.
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 · 102 lines · 33 tokens per session scan A 5ed1bc70836c
hivemind-memory is a skill published in the GitHub repository activeloopai/hivemind (1,593 stars, last pushed yesterday), licensed Apache-2.0. It adds 33 tokens to every session and 1,471 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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