Create, track and update team goals + KPIs in Hivemind via the `hivemind` CLI. Use whenever the user mentions a goal, objective, KPI, 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.
npx skills add https://github.com/activeloopai/hivemind --skill hivemind-goals
⚠️ CRITICAL: On this runtime (Hermes), you MUST use the hivemind shell CLI for goals + KPIs. 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/KPIs. For goals/KPIs, use the CLI below.
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>
hivemind kpi add <goal_id> <kpi_id> <target> <unit> [name] # add KPI to goal
hivemind kpi list <goal_id> # list KPIs for goal
hivemind kpi bump <goal_id> <kpi_id> <delta> # increment current (int)
hivemind goal add "<short description>" — capture stdout, that's the goal_id (UUID).hivemind kpi add <goal_id> <slug> <target> <unit> per KPI.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.
When the user says "let's work on that task / goal" or "pick up the <X> task":
hivemind goal list --mine — match the user's reference to a goal_id.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.hivemind goal progress <goal_id> in_progress — mark it started.Start here: using the Files / Branch / Run lines. Continue as if the context was never lost.write_file on any path under ~/.deeplake/memory/goal/ or ~/.deeplake/memory/kpi/.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.
Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning
Comprehensive technology-agnostic prompt for analyzing and documenting project folder structures. Auto-detects project types (.NET, Java, React, Angular, Python, Node.js, Flutter), generates detailed blueprints with visualization options, naming conventions, file placement patterns, and extension templates for maintaining consistent code organization across diverse technology stacks.
Use when complex problems require systematic step-by-step reasoning with ability to revise thoughts, branch into alternative approaches, or dynamically adjust scope. Ideal for multi-stage analysis, design planning, problem decomposition, or tasks with initially unclear scope.
Multi-agent workflow examples to work together on the OpenServ Platform. Covers agent discovery, multi-agent workspaces, task dependencies, and workflow orchestration using the Platform Client. Read reference.md for the full API reference. Read openserv-agent-sdk and openserv-client for building and running agents.
> Compress natural language memory files (CLAUDE.md, todos, preferences) into caveman format to save input tokens. Preserves all technical substance, code, URLs, and structure. Compressed version overwrites the original file. Human-readable backup saved as FILE.original.md.
API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.
Patterns for automating GitHub workflows with AI assistance, inspired by [Gemini CLI](https://github.com/google-gemini/gemini-cli) and modern DevOps practices.
Groups existing components into logical business domains to plan service-based architecture. Use when asking "which components belong together?", "group these into services", "organize by domain", "component-to-domain mapping", or planning service extraction from an existing codebase. Do NOT use for identifying new domains from scratch (use domain-analysis) or analyzing coupling (use coupling-analysis).
Take activeloopai/hivemind-hermes-hivemind-goals from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.