hivemind

A shared memory system for teams and organisations, backed by Activeloop. It stores session summaries and raw session records so information can be recalled across coding-agent sessions.

In plain words
What is it for?
Use it to look up past sessions, search shared summaries or records by keyword, and retrieve information previously recorded by other users or agents.
Why use it?
It helps prevent important project or organisation context from being lost between sessions. It requires checking both the agent's built-in notes and the shared Hivemind memory.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/activeloopai/hivemind/skills
Any agent
npx skills add activeloopai/hivemind --skill skills
Clone the repo
git clone --depth 1 https://github.com/activeloopai/hivemind

Made for: Claude Code, Codex.

Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,266 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00032 $0.01266
Opus 5 $0.00016 $0.00633
Sonnet 5 $0.00006 $0.00253
Haiku 4.5 $0.00003 $0.00127

Measured yesterday against content hash cbae821afdaa, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

hivemind 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 yesterday.

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.

harnesses/openclaw/skills/SKILL.md · 88 lines

How it starts

The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Hivemind Memory

You have TWO memory sources. ALWAYS check BOTH when the user asks you to recall, remember, or look up ANY information:

  1. Your built-in memory — personal per-project notes from the host agent
  2. Hivemind global memory — global memory shared across all sessions, users, and agents in the org, accessed via the tools below

Memory Structure

/index.md                           ← START HERE — table of all sessions
/summaries/
  <username>/
    <session-id>.md                 ← AI-generated wiki summary per session
/sessions/
  <username>/
    <user_org_ws_slug>.jsonl        ← raw session data
  1. First: call hivemind_index() — table of all sessions with dates, projects, descriptions
  2. If you need details: call hivemind_read("/summaries/<username>/<session>.md")
  3. If you need raw data: call hivemind_read("/sessions/<username>/<file>.jsonl")
  4. Keyword search: call hivemind_search("keyword") — substring search across both summaries and sessions, returns path:line hits

Do NOT jump straight to reading raw JSONL files. Always start with hivemind_index and summaries.

Organization Management

  • /hivemind_login — sign in via device flow
  • /hivemind_capture — toggle capture on/off (off = no data sent)
  • /hivemind_whoami — show current org and workspace
  • /hivemind_orgs — list organizations
  • /hivemind_switch_org <name-or-id> — switch organization
  • /hivemind_workspaces — list workspaces
  • /hivemind_switch_workspace <id> — switch workspace
  • /hivemind_version — show installed version and check npm for updates
  • /hivemind_update — shows how to install (ask the agent, or run hivemind update in your terminal)
  • /hivemind_autoupdate [on|off] — toggle the agent-facing update nudge (on by default: when a newer version is available, the agent is prompted to install it via exec if you ask to update)

Skill Management (skillify)

Hivemind also mines reusable Claude skills from agent sessions and stores them in a per-org Deeplake table. Openclaw itself doesn't run sessions to mine, but you can pull skills others have already mined for the user. These run in the user's terminal (the openclaw plugin does not register them as /hivemind_* commands):

Read the full file on GitHub · 88 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. yesterday First seen · 88 lines · 32 tokens per session scan A cbae821afdaa

Subscribe to this mod's changes

hivemind is a skill published in the GitHub repository activeloopai/hivemind (1,590 stars, last pushed 11d ago), licensed Apache-2.0. It adds 32 tokens to every session and 1,266 once invoked, about $0.0002 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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