Claw Agent Protocol (CAP) Skill

This skill enables any AI agent to interact with a user's personal data through the Claw Agent Protocol (CAP), a lightweight MCP server that provides a canonical, real-time view of personal data from various sources.

Core Concepts

CAP solves the data chaos problem: Instead of dealing with dozens of different APIs and data formats, CAP provides a single, consistent interface to all of a user's personal data.

• Real-Time Translation Layer: CAP fetches data on-demand from connected accounts (Gmail, Google Calendar, Notion, Slack, etc.) without storing it locally. Data stays at the source, queries are on-demand, security is delegated to OAuth providers.
• MCP-Native: CAP is a Model Context Protocol (MCP) server, making it compatible with any MCP-enabled client (OpenClaw, Claude Desktop, etc.).
• Canonical Schema: CAP exposes data through a consistent, canonical schema regardless of the original source. This eliminates integration complexity and improves agent reliability.

Key Constructs

CAP organizes data into two primary constructs:

1. Resources (Shelves): Raw, normalized data accessible via canonical URIs. These represent the fundamental categories of a user's digital life.
2. Tools (Views): High-level, task-oriented functions that combine data from multiple shelves to provide refined, actionable perspectives.

Available Shelves

Available Views

Usage Patterns

Querying Shelves

Query shelves using read operations on resource URIs with optional filters:

read cap://calendar?start_date=today
read cap://tasks?status=pending&priority=high
read cap://comms?from=client@example.com&unread=true

Executing Views

Call tools to execute pre-compiled views:

tools.today_briefing()
tools.client_pipeline(client_name="Acme Corp")
tools.knowledge_search(query="project requirements")

Reference Documentation

For detailed information, consult these reference files:

• Schema Reference: file.read('/home/ubuntu/skills/claw-agent-protocol/references/schema.md') - Complete schema definitions for all shelves
• Query Examples: file.read('/home/ubuntu/skills/claw-agent-protocol/references/query_examples.md') - Common query patterns and filters
• Security Guide: file.read('/home/ubuntu/skills/claw-agent-protocol/references/security.md') - Permissions, sensitivity tiers, and safe data handling
• Use Cases: file.read('/home/ubuntu/skills/claw-agent-protocol/references/use_cases.md') - 30 common scenarios for CAP usage

Utility Scripts

Use these scripts for common CAP operations:

• generate_briefing.py: Format CAP data into readable daily briefings

  python /home/ubuntu/skills/claw-agent-protocol/scripts/generate_briefing.py '<json_data>'
  

• validate_cap_data.py: Validate CAP data against schema requirements

  python /home/ubuntu/skills/claw-agent-protocol/scripts/validate_cap_data.py '<json_data>'
  

• export_cap_data.py: Export CAP data to various formats (CSV, JSON, Markdown)

  python /home/ubuntu/skills/claw-agent-protocol/scripts/export_cap_data.py --format csv --shelf calendar --output events.csv
  

• build_query.py: Generate CAP query strings from natural language

  python /home/ubuntu/skills/claw-agent-protocol/scripts/build_query.py "show me high priority tasks due this week"
  

Best Practices

1. Always check provenance: Use the source field to understand where data originated and link back to the original source.
2. Respect sensitivity tiers: Handle S1 (public), S2 (internal), and S3 (sensitive) data appropriately.
3. Use confidence scores: When confidence is below 0.8, verify data with the user before taking action.
4. Prefer views over raw queries: Use pre-compiled views (tools) when available—they're optimized and tested.
5. Cache judiciously: CAP data is real-time, but you can cache results briefly for performance. Never cache beyond the current session.