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 deeplake-memorygit 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/deeplake-memory)<a href="https://agentmods.dev/skills/activeloopai/hivemind/deeplake-memory"><img src="https://agentmods.dev/badge/skills/activeloopai/hivemind/deeplake-memory/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/deeplake-memory"><img src="https://agentmods.dev/badge/skills/activeloopai/hivemind/deeplake-memory.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 | $0.00033 | $0.01480 |
| Opus 5.5 | $0.00013 | $0.00592 |
| Sonnet 5.5 | $0.00007 | $0.00296 |
| Haiku 4.5 | $0.00003 | $0.00148 |
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 5d 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.
- 5d ago Changed 007e9b797f5f
- 1mo ago First seen · 102 lines · 33 tokens per session scan A 5ed1bc70836c
hivemind-memory is a skill published in the GitHub repository activeloopai/hivemind (1,628 stars, last pushed today), licensed Apache-2.0. It adds 33 tokens to every session and 1,480 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
ccs-align
Run the CCS Align seat's hourly breathing cycle — prove the local claude-mem worker is healthy, pull needle observations through search → timeline → getobservations, land them in a seat-owned middle cache via atomic grab → append → filter exclude-marks → replace, manage exclude marks, and walk house → project → seat…
weekly-digests
Generate a serial week-by-week narrative digest of a project's full claude-mem timeline. Splits the timeline into per-ISO-week files, then runs one consecutive subagent per week — each receiving the prior week's carry-forward block — to produce one chapter per ISO week of data. Use when asked for "weekly digests"…
mode-creator
Interactively create, install, activate, and verify custom claude-mem modes, including domain-specific observation types, concept tags, optional Telegram alerts, bot setup, worker restart, and startup-context verification. Use this whenever someone asks to customize what claude-mem remembers, create or change a mode…
handoff
Generate a HANDOFF.md that captures goal, current state, files touched, failed attempts, and next steps — so a fresh Claude session can continue exactly where this one left off. Use when sessions are getting long, Claude keeps retrying the same broken solution, or the user wants to step away and resume later.
cloud-sync
Set up or check claude-mem cloud sync with cmem.ai Pro. Use when the user says "set up cloud sync", "sync my memories", "cmem pro", "cloud backup", "sync status", or wants their memory database backed up or synced to their cmem.ai account.
screenpipe
Search screen and audio history recorded by Screenpipe and optionally save selected context into claude-mem. Use when the user asks to recall desktop activity, a meeting, or something seen in another app through Screenpipe.