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-memorynpx skills add activeloopai/hivemind --skill hivemind-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 | $0.01478 |
| Opus 5 | $0.00016 | $0.00739 |
| Sonnet 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 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 — 105 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:
- Your built-in memory (
~/.claude/) — personal per-project notes - Hivemind global memory (
~/.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 pattern="keyword" path="~/.deeplake/memory"
Do NOT jump straight to reading raw JSONL files. Always start with index.md and summaries.
Organization Management
The auth command path is injected at session start. Use the exact path from the session context. Each argument is separate — do NOT quote subcommands together:
node "<AUTH_CMD>" login— SSO loginnode "<AUTH_CMD>" whoami— show current user/orgnode "<AUTH_CMD>" org list— list organizationsnode "<AUTH_CMD>" org switch <name-or-id>— switch organizationnode "<AUTH_CMD>" workspaces— list workspacesnode "<AUTH_CMD>" workspace <id>— switch workspacenode "<AUTH_CMD>" invite <email> <ADMIN|WRITE|READ>— invite member (ALWAYS ask user which role first)node "<AUTH_CMD>" members— list membersnode "<AUTH_CMD>" remove <user-id>— remove membernode "<AUTH_CMD>" --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 · 105 lines · 33 tokens per session scan A 7e5c1b6f499b
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 1,478 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.
Other skills, from other repositories
design-is
Audit a design against Dieter Rams' ten "Good design is..." principles, then hand off a /make-plan prompt for one of three outcomes — new design, refine design, or redesign. Use when the user says "audit this design", "design review", "check this UI against Rams", "is this UI good", "critique this design", "design…
oh-my-issues
Cluster a GitHub issue backlog by root cause into a small set of plan-master issues, redirect children with a standardized comment, and bundle architectural-fix PRs that close clusters atomically. Use when an issue tracker has accumulated dozens of reports that share underlying defects, when asked to triage /…
timeline-report
Generate a "Journey Into [Project]" narrative report analyzing a project's entire development history from claude-mem's timeline. Use when asked for a timeline report, project history analysis, development journey, or full project report.
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…
smart-explore
Token-optimized structural code search using tree-sitter AST parsing. Use instead of reading full files when you need to understand code structure, find functions, or explore a codebase efficiently.