정보
이 스킬은 `memory/goal/` 및 `memory/kpi/` 경로의 Deeplake 파일 시스템에 Markdown 파일을 작성하여 목표, 핵심성과지표(KPI), 작업을 생성하고 관리합니다. 사용자가 목표, 타겟, 마일스톤 또는 작업이나 할 일과 같은 실행 가능한 업무 항목을 언급할 때 활성화됩니다. 이전의 레거시 CLI 도구를 대체하여 이제 상위 수준의 목표와 구체적인 업무 항목을 통합 시스템에서 처리합니다.
빠른 설치
Claude Code
추천npx skills add activeloopai/hivemind -a claude-code/plugin add https://github.com/activeloopai/hivemindgit clone https://github.com/activeloopai/hivemind.git ~/.claude/skills/hivemind-goalsClaude Code에서 이 명령을 복사하여 붙여넣어 스킬을 설치하세요
문서
Hivemind Goals
Track goals and KPIs 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", "add a KPI", "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 tasksCLI — 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
~/.deeplake/memory/kpi/<goal_id>/<kpi_id>.md
<owner>— user identifier (use the userName fromhivemind whoamior the credentials)<status>— one ofopened,in_progress,closed<goal_id>— UUIDv4 you generate at create time<kpi_id>— short slug likek-prsork-demos
Path encoding is the source of truth. The owner, status, goal_id, and kpi_id come from the path — NOT from the file body. Do NOT write owner/status/goal_id/kpi_id inside the file content.
File body format
Goal file body — plain markdown, free form:
ship the goals-graph feature
Notes: focus on KPI tracking via VFS, no separate CLI.
Due: 2026-05-30.
KPI file body — markdown with a few mandatory key:value lines so the commit-driven auto-progress worker can parse and bump:
PRs merged
- target: 5
- current: 2
- unit: count
The target:, current:, unit: lines must stay on a single line each. The first line is the human-readable name. Anything else is free notes.
Operations
1. Create a new goal
When the user expresses a new goal:
- Get the current owner with
hivemind whoami(use the userName, e.g.emanuele.fenocchi). - Generate a UUIDv4:
uuidgen(do NOT usenode -e— Node is not available under the VFS path). - Write the goal file via Bash (Write / Edit are denied on memory paths; only Bash is intercepted and routed to SQL):
For a single-line goal,cat > ~/.deeplake/memory/goal/<owner>/opened/<uuid>.md <<'EOF' <goal description here, multiple lines OK> EOFecho '<text>' > ~/.deeplake/memory/goal/<owner>/opened/<uuid>.mdis equivalent. - Respond to the user that the goal is created.
Do NOT auto-generate KPIs. A goal is created with zero KPI files by default. Generate KPIs ONLY when the user explicitly asks you to ("aggiungi KPI per …", "add metrics for this goal", "track these metrics: …"). When the user asks, write each KPI as a separate file at ~/.deeplake/memory/kpi/<goal_id>/<kpi-slug>.md with the body format documented above.
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:
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 listand the SessionStart banner show. - Fill
Start here / Files / Branch / Run / Whyfrom 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.
- Find it:
hivemind goal list --mineand match the user's reference to agoal_id(by label / topic). If ambiguous, show the candidates and ask which one. - 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 listonly shows the first line, so always usegoal getfor the full body. - Flip to in_progress:
mv ~/.deeplake/memory/goal/<owner>/opened/<uuid>.md ~/.deeplake/memory/goal/<owner>/in_progress/<uuid>.md - Act on it: open the
Files:, switch to theBranch:if given, and begin fromStart 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
ls ~/.deeplake/memory/goal/<owner>/opened/
ls ~/.deeplake/memory/goal/<owner>/in_progress/
Then cat each <uuid>.md to read the body. Optionally ls ~/.deeplake/memory/kpi/<uuid>/ and cat each KPI to surface progress.
3. Edit a goal description
# 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
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:
# 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. Add a KPI manually
cat > ~/.deeplake/memory/kpi/<uuid>/<kpi-slug>.md <<'EOF'
<KPI name>
- target: <N>
- current: 0
- unit: <unit>
EOF
7. Record progress on a KPI
Read the KPI file, increment the current: line, write it back via Bash. The
Edit tool is denied on memory paths — overwrite the full file via heredoc:
cat ~/.deeplake/memory/kpi/<uuid>/<kpi-slug>.md # read current
cat > ~/.deeplake/memory/kpi/<uuid>/<kpi-slug>.md <<'EOF'
<KPI name>
- target: 5
- current: 3
- unit: count
EOF
A surgical sed -i 's/^- current: .*/- current: 3/' also works since sed
is an allowed builtin under the VFS path.
8. Reassign a goal (transfer ownership)
mv ~/.deeplake/memory/goal/<old-owner>/<status>/<uuid>.md ~/.deeplake/memory/goal/<new-owner>/<status>/<uuid>.md
Goal ownership lives in the path. KPI files do NOT have an owner segment — they are linked to the goal by <uuid>, so they need no change when a goal is reassigned.
Constraints — DO NOT do these
- Do NOT put
owner,status,goal_id, orkpi_idinside 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. - Do NOT block on the KPI generator subprocess — always spawn it detached (
nohup … &).
Auto-progress from git commit
A PostToolUse hook listens for git commit. When it fires, it spawns the agent's native LLM in the background with the commit diff + the list of the current user's open goals. The LLM reads each goal + its KPIs, judges whether the commit advanced any KPI, and edits the relevant KPI file to bump current:. This is fire-and-forget; the user does not block on it.
To disable globally: HIVEMIND_AUTO_KPI_FROM_COMMITS=false.
Team visibility
Every write goes to a team-shared table on Deeplake (hivemind_goals or hivemind_kpis). 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.
GitHub 저장소
자주 묻는 질문
hivemind-goals Skill이란 무엇인가요?
hivemind-goals은(는) activeloopai이(가) 만든 Claude Skill입니다. Skill은 Claude가 필요할 때 불러오는 지침과 리소스를 묶어 추가 프롬프트 없이 hivemind-goals 관련 작업을 수행할 수 있게 합니다.
hivemind-goals은(는) 어떻게 설치하나요?
이 페이지의 설치 명령을 사용하세요. hivemind-goals을(를) Claude Code 플러그인으로 추가하거나 저장소를 skills 디렉터리에 복제한 다음 Claude를 다시 시작해 Skill을 불러옵니다.
hivemind-goals은(는) 어떤 카테고리에 속하나요?
hivemind-goals은(는) 메타 카테고리에 속합니다.
hivemind-goals은(는) 무료로 사용할 수 있나요?
네. hivemind-goals은(는) AIMCP에 등록되어 있으며 무료로 설치할 수 있습니다.
연관 스킬
이 스킬은 콘텐츠 콜렉션(Content Collections)을 위한 프로덕션 검증된 설정을 제공합니다. 콘텐츠 콜렉션은 Markdown/MDX 파일을 Zod 검증이 포함된 타입 안전한 데이터 콜렉션으로 변환해주는 TypeScript 최우선 도구입니다. 블로그, 문서 사이트 또는 콘텐츠 중심의 Vite + React 애플리케이션을 구축할 때 타입 안전성과 자동 콘텐츠 검증을 보장하기 위해 사용하세요. Vite 플러그인 구성과 MDX 컴파일부터 배포 최적화 및 스키마 검증에 이르기까지 모든 것을 다룹니다.
이 스킬은 개발자들이 Polymarket 예측 시장 플랫폼을 활용한 애플리케이션을 구축할 수 있도록 지원하며, 거래 및 시장 데이터를 위한 API 통합 기능을 포함합니다. 또한 WebSocket을 통한 실시간 데이터 스트리밍을 제공하여 실시간 거래와 시장 활동을 모니터링할 수 있습니다. 이를 통해 거래 전략을 구현하거나 실시간 시장 업데이트를 처리하는 도구를 생성하는 데 활용할 수 있습니다.
이 스킬은 개발자들이 명령어, 파일, LSP 작업 등 25개 이상의 이벤트 유형에 연결되는 OpenCode 플러그인을 만들 수 있도록 돕습니다. JavaScript/TypeScript 모듈을 위한 플러그인 구조, 이벤트 API 명세, 구현 패턴을 제공합니다. OpenCode AI 어시스턴트의 라이프사이클을 사용자 정의 이벤트 기반 로직으로 가로채거나, 모니터링하거나, 확장해야 할 때 사용하세요.
SGLang은 RadixAttention 프리픽스 캐싱을 활용하여 JSON, 정규식, 에이전트 워크플로우를 위한 고속 구조화 생성에 특화된 고성능 LLM 서빙 프레임워크입니다. 특히 반복되는 프리픽스가 있는 작업에서 상당히 빠른 추론 속도를 제공하여 복잡한 구조화 출력 및 다중 턴 대화에 이상적입니다. 제약 디코딩이 필요하거나 광범위한 프리픽스 공유가 있는 애플리케이션을 구축할 때는 vLLM과 같은 대안보다 SGLang을 선택하십시오.
