Hivemind Goals logo

Hivemind Goals

OrganizationPopular
activeloopai
hivemind-goals

Create, track and update team goals in Hivemind via the `hivemind` CLI. 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/hermes/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 — CLI only (Hermes)

⚠️ CRITICAL: On this runtime (Hermes), you MUST use the hivemind shell CLI for goals. DO NOT use write_file on ~/.deeplake/memory/goal/... paths — those writes go to the local filesystem and never reach the team-shared hivemind_goals table. Other team members will NOT see them.

The hivemind-memory skill describes a generic memory layout — it does NOT apply to goals. For goals, use the CLI below.

Commands (invoke via terminal tool)

hivemind goal add "<text>"                                  # create goal, prints goal_id
hivemind goal list [--mine|--all]                           # list (default --mine)
hivemind goal done <goal_id>                                # mark closed
hivemind goal progress <goal_id> <opened|in_progress|closed>

Workflow when the user expresses a goal

  1. hivemind goal add "<short description>" — capture stdout, that's the goal_id (UUID).
  2. Tell the user the goal_id and that it is now team-visible in Deeplake.

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. Tag it --agent capture so parked side-tasks are separable from hand-made goals:

hivemind goal add --agent capture "Add rate-limiting to the webhook handler

Start here: add a per-IP token bucket on the handler entry path
Files: src/webhook/handler.ts:120-160, src/webhook/limits.ts
Branch: feat/webhook-hardening
Run: pnpm test webhook
Why: bursty clients hammer the endpoint; defer until retry-backoff lands"

Line 1 is the label (what goal list shows). Fill Start here / Files / Branch / Run / Why from the conversation; Start here: matters most. Pass the whole package as one double-quoted argument so the newlines are preserved.

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 goal list --mine — match the user's reference to a goal_id.
  2. hivemind goal get <goal_id> — prints the full package (goal list shows only the first line, so always use goal get). Read it as your working context.
  3. hivemind goal progress <goal_id> in_progress — mark it started.
  4. Begin from Start here: using the Files / Branch / Run lines. Continue as if the context was never lost.

What NOT to do

  • Do NOT call write_file on any path under ~/.deeplake/memory/goal/.
  • Do NOT do mkdir / cat > to create those files manually via terminal.

If the user wants to inspect goals you created, run hivemind goal list --mine (terminal) and present the output.

Frequently asked questions

What does the Hivemind Goals AI skill do?

Create, track and update team goals in Hivemind via the `hivemind` CLI. 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/hermes/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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇