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Hivemind Goals

OrganizationPopular
activeloopai
hivemind-goals

Create, track and update team goals via the Deeplake virtual filesystem at memory/goal/. 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/claude-code/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

Track goals as Markdown files inside the Deeplake virtual filesystem. Each file is one row in a dedicated team-shared table — the path encodes the structural metadata, the file body holds the human-readable description.

When to use this skill

Activate when the user expresses any of:

  • "I want to track X / aim for X / track my progress on Y"
  • "add a goal", "what are my goals?"
  • "mark this as done", "close that goal"
  • "shipping X by Friday", "5 PRs this week", any measurable target
  • "create a task", "add a todo", "remind me to fix X", any work item (the goals system absorbs the old hivemind tasks CLI — there is no separate task store)

For "list my goals" → run ls ~/.deeplake/memory/goal/<userName>/opened/ and ls ~/.deeplake/memory/goal/<userName>/in_progress/. If empty, ask the user if they want to create one.

Path conventions (LEARN THESE)

~/.deeplake/memory/goal/<owner>/<status>/<goal_id>.md
  • <owner> — user identifier (use the userName from hivemind whoami or the credentials)
  • <status> — one of opened, in_progress, closed
  • <goal_id> — UUIDv4 you generate at create time

Path encoding is the source of truth. The owner, status, and goal_id come from the path — NOT from the file body. Do NOT write owner/status/goal_id inside the file content.

File body format

Goal file body — plain markdown, free form:

ship the goals-graph feature

Notes: route every write through the VFS, no separate CLI.
Due: 2026-05-30.

The first line is the human-readable label (what goal list and the SessionStart banner show). Anything else is free notes.

Operations

1. Create a new goal

When the user expresses a new goal:

  1. Get the current owner with hivemind whoami (use the userName, e.g. emanuele.fenocchi).
  2. Generate a UUIDv4: uuidgen (do NOT use node -e — Node is not available under the VFS path).
  3. Write the goal file via Bash (Write / Edit are denied on memory paths; only Bash is intercepted and routed to SQL):
    bash
    cat > ~/.deeplake/memory/goal/<owner>/opened/<uuid>.md <<'EOF'
    <goal description here, multiple lines OK>
    EOF
    For a single-line goal, echo '<text>' > ~/.deeplake/memory/goal/<owner>/opened/<uuid>.md is equivalent.
  4. Respond to the user that the goal is created.

1a. Capture a task for later (with resumable context)

Use this 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", "capture this in Hivemind". The value is NOT the one-liner — it's storing enough context to resume cold in a future session without the user re-explaining anything.

Write it via the CLI (not the VFS heredoc) so the row is tagged agent: capture, which separates parked side-tasks from hand-made goals:

bash
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; agreed to defer until the retry-backoff work lands"
  • Line 1 is the label — keep it short; it's what goal list and the SessionStart banner show.
  • Fill Start here / Files / Branch / Run / Why from the live conversation — you already know the files you just touched and the branch. Include only the lines you can fill; omit the rest. Start here: is the most important — the concrete first action.
  • Pass the whole package as one double-quoted argument (the newlines are preserved into the stored body).
  • Confirm to the user: the label + that it'll resume cleanly next session.

1b. Resume a parked task (automatic context transfer)

When the user says "let's work on that task / that goal", "let's start the <X> task", or "pick up the parked <X>", pull its stored context back into the session and continue — the user should NOT have to re-explain anything.

  1. Find it: hivemind goal list --mine and match the user's reference to a goal_id (by label / topic). If ambiguous, show the candidates and ask which one.
  2. Transfer the context: hivemind goal get <goal_id> prints the full package (Start here / Files / Branch / Run / Why). Read it as your working context — goal list only shows the first line, so always use goal get for the full body.
  3. Flip to in_progress: mv ~/.deeplake/memory/goal/<owner>/opened/<uuid>.md ~/.deeplake/memory/goal/<owner>/in_progress/<uuid>.md
  4. Act on it: open the Files:, switch to the Branch: if given, and begin from Start here:. You are now resumed — continue as if the context was never lost. Close it (section 5) when the work is done.

2. List goals

bash
ls ~/.deeplake/memory/goal/<owner>/opened/
ls ~/.deeplake/memory/goal/<owner>/in_progress/

Then cat each <uuid>.md to read the body.

3. Edit a goal description

bash
# Read the existing body, then overwrite via Bash heredoc. Edit / Write
# tools are denied on memory paths in claude-code (the hook can only
# rewrite Bash). The VFS handles version-bumping — every overwrite
# produces a fresh row in the hivemind_goals table.
cat ~/.deeplake/memory/goal/<owner>/opened/<uuid>.md   # read current
cat > ~/.deeplake/memory/goal/<owner>/opened/<uuid>.md <<'EOF'
<new body here>
EOF

4. Move a goal to in_progress

bash
mv ~/.deeplake/memory/goal/<owner>/opened/<uuid>.md ~/.deeplake/memory/goal/<owner>/in_progress/<uuid>.md

mv between status folders is an atomic version-bump. The file body carries over unchanged.

5. Close a goal

Two equivalent ways:

bash
# Explicit mv to closed (recommended — clearest intent)
mv ~/.deeplake/memory/goal/<owner>/in_progress/<uuid>.md ~/.deeplake/memory/goal/<owner>/closed/<uuid>.md

# Or: rm (the VFS interprets rm on a goal path as a soft-close)
rm ~/.deeplake/memory/goal/<owner>/opened/<uuid>.md

Important: rm does NOT actually delete the goal. It is a soft-close — the VFS writes a new version with status=closed. The goal remains in the team-shared table for audit. There is no hard-delete in v1.

6. Reassign a goal (transfer ownership)

bash
mv ~/.deeplake/memory/goal/<old-owner>/<status>/<uuid>.md ~/.deeplake/memory/goal/<new-owner>/<status>/<uuid>.md

Goal ownership lives in the path; the file body carries over unchanged.

Constraints — DO NOT do these

  • Do NOT put owner, status, or goal_id inside the file body. The path is the source of truth — duplicating in the body causes drift.
  • Do NOT use status values other than opened, in_progress, closed.
  • Do NOT rename the goal_id (the UUID in the filename) via mv. The VFS rejects goal_id renames.

Team visibility

Every write goes to a team-shared table on Deeplake (hivemind_goals). Other team members see your goals in their SessionStart context and via direct ls / cat on the same paths in their own VFS. No explicit sharing step needed.

Frequently asked questions

What does the Hivemind Goals AI skill do?

Create, track and update team goals via the Deeplake virtual filesystem at memory/goal/. 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/claude-code/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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