Fetches openclaw-control-center from aradotso/trending-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/openclaw-control-center
Restart Cursor to activate openclaw-control-center. Access via /openclaw-control-center in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
OpenClaw Control Center transforms OpenClaw from a black box into a local, auditable control center. It provides visibility into agent activity, token spend, task execution chains, cross-session collaboration, memory state, and document sources — with security-first defaults that keep all mutations off by default.
What It Does
Overview: System health, pending items, risk signals, and operational summary
Staff: Who is actively executing vs. queued — not just "has tasks"
Collaboration: Parent-child session handoffs and verified cross-session messages (e.g. Main ⇄ Pandas)
Tasks: Task board, approvals, execution chains, run evidence
Memory: Per-agent memory health, searchability, and source file editing
Documents: Shared and agent-core documents opened from actual source files
Settings: Connector wiring status, security risk summary, update status
Installation
git clone https://github.com/TianyiDataScience/openclaw-control-center.git
cd openclaw-control-center
npminstallcp .env.example .env
npm run build
npmtestnpm run smoke:ui
npm run dev:ui
Open:
http://127.0.0.1:4310/?section=overview&lang=zh
http://127.0.0.1:4310/?section=overview&lang=en
Use npm run dev:ui over UI_MODE=true npm run dev — more stable, especially on Windows shells.
Critical constraint: Only modify files inside control-center/. Never modify ~/.openclaw/openclaw.json.
Environment Configuration
Copy .env.example to .env and configure:
# Security defaults — do NOT change without understanding implications
READONLY_MODE=true
LOCAL_TOKEN_AUTH_REQUIRED=true
IMPORT_MUTATION_ENABLED=false
IMPORT_MUTATION_DRY_RUN=false
APPROVAL_ACTIONS_ENABLED=false
APPROVAL_ACTIONS_DRY_RUN=true
# Connection
OPENCLAW_GATEWAY_URL=http://127.0.0.1:PORT
OPENCLAW_HOME=~/.openclaw
# UI
PORT=4310
DEFAULT_LANG=zh
Security Flag Meanings
Flag
Default
Effect
READONLY_MODE
true
All state-changing endpoints disabled
LOCAL_TOKEN_AUTH_REQUIRED
true
Import/export and write APIs require local token
IMPORT_MUTATION_ENABLED
false
Import mutations blocked entirely
IMPORT_MUTATION_DRY_RUN
false
Dry-run mode for imports when enabled
APPROVAL_ACTIONS_ENABLED
false
Approval actions hard-disabled
APPROVAL_ACTIONS_DRY_RUN
true
Approval actions run as dry-run when enabled
Key Commands
# Developmentnpm run dev:ui # Start UI server (recommended)npm run dev # One-shot monitor run, no HTTP UI# Build & Testnpm run build # TypeScript compilenpmtest# Run test suitenpm run smoke:ui # Smoke test the UI endpoints# Lintnpm run lint # ESLint checknpm run lint:fix # Auto-fix lint issues
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
