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Hivemind Goals

OrganizationPopular
activeloopai
hivemind-goals

Create, track, and read team goals via Hivemind from openclaw. Use whenever the user mentions a goal, objective, target, milestone, or asks to track progress on something measurable. ALSO use when the user says "task", "todo", "work item", "remind me to", "fix X", or any actionable work item — the goal system replaced the legacy `hivemind tasks` CLI and now covers both objectives and tasks.

Overview

Publisheractiveloopai
Repositoryhivemind
Skill namehivemind-goals
Stars
1.6K
Forks
107
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by activeloopai on GitHub. Read the source before you install it.

Installation

Install the Hivemind Goals AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/activeloopai/hivemind.git /tmp/hivemind
mkdir -p .claude/skills
cp -r /tmp/hivemind/harnesses/openclaw/skills/hivemind-goals .claude/skills/hivemind-goals
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hivemind Goals in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hivemind Goals on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hivemind Goals is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Hivemind Goals (openclaw)

OpenClaw exposes purpose-built tools for goals. Use them directly — do NOT try to write files via the host filesystem.

Tools

  • hivemind_goal_add({ text }) — create a new goal. Returns goal_id (UUID). Status starts at opened.
  • hivemind_search({ query }) — search Hivemind shared memory (summaries + sessions). Use this when the user asks "what's already there" before creating a duplicate.
  • hivemind_read({ path }) — read the full content of a specific Hivemind path.
  • hivemind_index({}) — list everything in memory.

Workflow when the user expresses a goal

  1. (Optional) hivemind_search first to surface any existing related goal.
  2. hivemind_goal_add({ text: "<short description>" }) — capture the returned goal_id.
  3. Confirm to the user with the goal_id and that the goal is team-visible.

Capture a task for later (with resumable context)

When the user parks a tangential task mid-session — "save this for later", "remind me to …", "don't let me forget …", "let's do X later" — store enough context to resume cold later, not just a one-liner. Put the full package in the text of hivemind_goal_add:

hivemind_goal_add({ text:
  "Add rate-limiting to the webhook handler\n\n" +
  "Start here: add a per-IP token bucket on the handler entry path\n" +
  "Files: src/webhook/handler.ts:120-160, src/webhook/limits.ts\n" +
  "Branch: feat/webhook-hardening\n" +
  "Run: pnpm test webhook\n" +
  "Why: bursty clients hammer the endpoint; defer until retry-backoff lands" })

Line 1 is the label. Fill Start here / Files / Branch / Run / Why from the conversation; Start here: (the concrete first action) matters most. (OpenClaw's hivemind_goal_add has no provenance flag, so the row is tagged manual — that's fine; the context is what matters.)

Resume a parked task (automatic context transfer)

When the user says "let's work on that task / goal" or "pick up the <X> task":

  1. hivemind_search({ query: "<topic>" }) or hivemind_index({}) to locate the parked goal, then hivemind_read({ path: "memory/goal/<owner>/opened/<goal_id>.md" }) to pull the full context package back.
  2. Read it as your working context and begin from Start here: using the Files / Branch / Run lines — continue as if the context was never lost.

(Status-move tools aren't exposed on OpenClaw, so leave the goal where it is and just resume the work.)

What NOT to do

  • Do NOT write files anywhere under ~/.deeplake/memory/. OpenClaw's runtime does not route filesystem writes to the Deeplake tables — only the hivemind_* tools above do.
  • Do NOT use hivemind_search to create anything — it's read-only.

Frequently asked questions

What does the Hivemind Goals AI skill do?

Create, track, and read team goals via Hivemind from openclaw. Use whenever the user mentions a goal, objective, target, milestone, or asks to track progress on something measurable. ALSO use when the user says "task", "todo", "work item", "remind me to", "fix X", or any actionable work item — the goal system replaced the legacy `hivemind tasks` CLI and now covers both objectives and tasks.

Why use Hivemind Goals on TypingMind?

Because you install it once and use it with any model. Hivemind Goals is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hivemind Goals in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/activeloopai/hivemind/tree/main/harnesses/openclaw/skills/hivemind-goals. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hivemind Goals?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hivemind Goals?

As many as you like. As long as a model supports skills, you can use Hivemind Goals with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hivemind Goals AI skill free?

Yes. It is published on GitHub by activeloopai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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