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Get Started Free →Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.
.claude/skills/activeloopai-hivemind-memory/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 274% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 67% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 25% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 102% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 9% | 0% |
You have persistent memory at ~/.deeplake/memory/ — global memory shared across all sessions, users, and agents in the org.
~/.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~/.deeplake/memory/index.md — quick scan of all sessions with dates, projects, descriptions~/.deeplake/memory/summaries/<session>.md~/.deeplake/memory/sessions/<user>/<file>.jsonlgrep -r "keyword" ~/.deeplake/memory/Do NOT jump straight to reading raw JSONL files. Always start with index.md and summaries.
Each argument is separate — do NOT quote subcommands together.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 commandsHivemind can mine reusable skills from agent session logs and share them across your team. Each argument is separate — do NOT quote subcommands together.
hivemind skillify — show current scope, team, install location, per-project statehivemind skillify pull — sync project skills from the org table to local FShivemind skillify pull --user <email> — only skills authored by that userhivemind skillify pull --users <a,b,c> — multiple authors (CSV)hivemind skillify pull --all-users — explicit "no author filter" (default)hivemind skillify pull --to <project|global> — install location (project=cwd/.claude/skills, global=~/.claude/skills)hivemind skillify pull --dry-run — preview without touching diskhivemind skillify pull --force — overwrite local files even if up-to-date (creates .bak)hivemind skillify pull <skill-name> — pull only that one skill (combines with --user)hivemind skillify push <skill-name> — upload a local skill to the org table (inverse of pull; re-push lands a new version)hivemind skillify push --from <project|global> — which local skills dir to read (default: project)hivemind skillify push --dry-run — preview without writing to the org tablehivemind skillify unpull — remove every skill previously installed by pullhivemind skillify unpull --user <email> — remove only that author's pullshivemind skillify unpull --not-mine — remove all pulls except your ownhivemind skillify unpull --dry-run — preview without touching diskhivemind skillify scope <me|team> — sharing scope for newly mined skillshivemind skillify install <project|global> — default install location for new skillshivemind skillify promote <skill-name> — move a project skill to the global locationhivemind skillify team add|remove|list <username> — manage team member listhivemind skillify mine-local — one-shot: mine skills from local sessions, no auth neededOpt-in, persisted in ~/.deeplake/config.json.
hivemind embeddings install — download deps (~600MB), symlink agents, set enabled:truehivemind embeddings enable — flip enabled:true (run install first if deps missing)hivemind embeddings disable — flip enabled:false + SIGTERM daemon (deps stay on disk)hivemind embeddings uninstall [--prune] — remove agent symlinks + disable; --prune wipes deps toohivemind embeddings status — show config + deps + per-agent link stateOnly 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 filesystem. If a task seems to require Python, rewrite it using bash tools (e.g., cat file.json | jq 'keys | length').
Do NOT spawn subagents to read deeplake memory. If a file returns empty after 2 attempts, skip it and move on. Report what you found rather than exhaustively retrying.
After installing the plugin:
node "<AUTH_CMD>" loginHIVEMIND_DEBUG=1 codex — enable verbose logging to ~/.deeplake/hook-debug.logHIVEMIND_CAPTURE=false codex — disable session capture| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 2,130 | 5,673 | +166% | 1 | 1 | 0% | 341 | 1,735 | +409% | 0 | 0 | — |
case-02 | fail→pass | 5,394 | 2,640 | -51% | 1 | 1 | 0% | 516 | 1,930 | +274% | 0 | 0 | — |
case-03 | fail→pass | 5,558 | 1,708 | -69% | 1 | 1 | 0% | 1,045 | 1,750 | +67% | 0 | 0 | — |
case-04 | fail→pass | 10,832 | 4,844 | -55% | 1 | 1 | 0% | 1,911 | 2,388 | +25% | 0 | 0 | — |
case-05 | fail→pass | 4,566 | 3,157 | -31% | 1 | 1 | 0% | 819 | 1,656 | +102% | 0 | 0 | — |
case-06 | fail→pass | 7,944 | 3,583 | -55% | 1 | 1 | 0% | 1,463 | 1,592 | +9% | 0 | 0 | — |
case-07 | fail→pass | 7,520 | 10,459 | +39% | 1 | 1 | 0% | 1,304 | 1,999 | +53% | 0 | 0 | — |
case-08 | fail→fail | 5,558 | 5,588 | +1% | 1 | 1 | 0% | 937 | 1,635 | +74% | 0 | 0 | — |
case-09 | pass→pass | 6,056 | 3,970 | -34% | 1 | 1 | 0% | 1,156 | 1,722 | +49% | 0 | 0 | — |
case-10 | fail→fail | 4,031 | 14,977 | +272% | 1 | 1 | 0% | 674 | 1,708 | +153% | 0 | 0 | — |
case-11 | fail→pass | 6,689 | 2,664 | -60% | 1 | 1 | 0% | 1,180 | 1,731 | +47% | 0 | 0 | — |
case-12 | fail→pass | 6,106 | 1,696 | -72% | 1 | 1 | 0% | 1,171 | 1,754 | +50% | 0 | 0 | — |
case-13 | fail→pass | 9,312 | 1,488 | -84% | 1 | 1 | 0% | 1,587 | 1,714 | +8% | 0 | 0 | — |
case-14 | fail→pass | 5,383 | 2,573 | -52% | 1 | 1 | 0% | 1,003 | 1,869 | +86% | 0 | 0 | — |
case-15 | fail→pass | 10,518 | 1,975 | -81% | 1 | 1 | 0% | 1,841 | 1,751 | -5% | 0 | 0 | — |
case-16 | fail→pass | 6,932 | 1,643 | -76% | 1 | 1 | 0% | 1,413 | 1,694 | +20% | 0 | 0 | — |
case-17 | fail→pass | 3,851 | 1,794 | -53% | 1 | 1 | 0% | 714 | 1,809 | +153% | 0 | 0 | — |
case-18 | fail→pass | 10,693 | 2,762 | -74% | 1 | 1 | 0% | 1,967 | 1,859 | -5% | 0 | 0 | — |
case-19 | fail→pass | 9,289 | 2,974 | -68% | 1 | 1 | 0% | 2,019 | 1,977 | -2% | 0 | 0 | — |
case-20 | fail→pass | 5,221 | 1,599 | -69% | 1 | 1 | 0% | 907 | 1,740 | +92% | 0 | 0 | — |
case-21 | pass→pass | 6,039 | 7,409 | +23% | 1 | 1 | 0% | 1,223 | 3,044 | +149% | 0 | 0 | — |
case-22 | pass→fail | 2,607 | 5,435 | +108% | 1 | 1 | 0% | 388 | 1,661 | +328% | 0 | 0 | — |
case-23 | pass→pass | 4,614 | 11,823 | +156% | 1 | 1 | 0% | 919 | 3,581 | +290% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 18 counted toward the lift figure. The other 5 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +65 percentage points is the difference between those two pass rates over the 18 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.