claw-agent-protocol

name	claw-agent-protocol
description	Interact with the Claw Agent Protocol (CAP), a lightweight MCP server providing canonical, real-time access to personal data for AI agents. Use when working with user personal data across Gmail, Calendar, Notion, Slack, tasks, contacts, or any CAP-connected data source. Enables structured querying, data organization, and task-oriented views of user information.

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

Shelf	Resource URI	Description
Identity	cap://identity	People, orgs, contacts
Comms	cap://comms	Messages, emails, threads
Calendar	cap://calendar	Events, availability
Docs	cap://docs	Notes, files, snippets
Tasks	cap://tasks	Tasks, projects, milestones

Available Views

View	Tool Name	Description
Today Briefing	today_briefing	Calendar, tasks, comms for today
Client Pipeline	client_pipeline	Contacts, comms, tasks by client
Knowledge Search	knowledge_search	Search all docs and notes

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=[e-mail]&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.