A persistent memory layer for AI agents that stores and analyzes six months of versioned history for social media creators.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install crowterminal
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install crowterminal using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is CrowTerminal?
CrowTerminal serves as an external brain for AI agents, solving the problem of ephemeral context windows. While typical agents lose context between sessions, CrowTerminal maintains a robust, versioned history of creator data, including hook patterns, engagement rates, and posting schedules. It allows agents to track what works over months rather than days, making it an essential addition to any suite of Openclaw Skills.
This skill is specifically architected for the influencer and creator economy, providing a specialized API that supports natural language queries and semantic field resolution. By using CrowTerminal, your agents can perform deep pattern detection, validate strategy changes against historical performance, and ingest proprietary platform data like retention curves and audience demographics that are typically hidden from standard APIs.
CrowTerminal Use Cases
- Tracking long-term evolution of content hook patterns to see which styles sustain engagement.
- Analyzing 6-month performance trends to identify the best posting times for specific creator niches.
- Validating new content strategies by comparing them against historical failures and successes.
- Ingesting manual platform data like YouTube retention curves for agent-led optimization.
- Providing agents with a persistent identity and memory across multiple execution sessions.
How CrowTerminal Works
- The agent registers via the API to receive a unique persistent identity and authentication key.
- The agent utilizes the Schema Discovery endpoint to understand available data fields using natural language aliases.
- Long-term memory is retrieved for a specific client ID, providing the agent with the latest versioned 'skill' data and historical context.
- During content planning, the agent sends its proposed strategy to the Engagement Analysis endpoint to receive recommendations based on past performance.
- Post-execution, the agent pushes new learnings or platform data back to the memory layer, creating a new versioned snapshot for future sessions.
CrowTerminal Setup
To get started with CrowTerminal within your environment, first register your agent to obtain an API key:
curl -X POST "https://api.crowterminal.com/api/agent/register" \
-H "Content-Type: application/json" \
-d '{"agentName": "OpenClaw", "agentDescription": "My personal AI agent"}'
Once you receive your key, set it as an environment variable:
export CROWTERMINAL_API_KEY="your_key_here"
You can verify the service status without authentication:
curl https://api.crowterminal.com/api/agent/status
CrowTerminal Data Schema & Taxonomy
CrowTerminal organizes data into categories designed for LLM consumption. You can explore the full schema dynamically using the /memory/schema endpoint.
| Category | Key Fields | Purpose |
|---|---|---|
| Performance | avgEngagement, peakEngagement | Tracks interaction rates and success metrics. |
| Content | hookPatterns, contentStyle | Stores creative elements and high-performing hooks. |
| Timing | bestPostingTimes, score | Maps posting schedules to engagement outcomes. |
| History | version, changes | Tracks the evolution of the creator profile over time. |
CrowTerminal Advanced Features
- Killer Endpoint: Engagement Analysis allows agents to submit current learnings and receive specific similarity scores and growth recommendations.
- Natural Language Query: Agents can query data using intuitive terms like "how is the interaction rate" instead of strict field names.
- Version Comparison: Compare different historical snapshots (diffs) to identify exactly when performance trends shifted.
- Webhook Integration: Receive real-time notifications when creator data is ingested or skills are updated.
- Sandbox Environment: Full suite of mock endpoints for testing agent logic without consuming production resources or API limits in Openclaw Skills workflows.
SKILL.md
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