Claw agent protocol
A production-grade skill for AI agents like Openclaw, Manus, & more to interact with personal data through the Claw Agent Protocol.
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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.
SKILL.md
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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:
- Resources (Shelves): Raw, normalized data accessible via canonical URIs. These represent the fundamental categories of a user's digital life.
- 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://[email protected]&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
- Always check provenance: Use the
sourcefield to understand where data originated and link back to the original source. - Respect sensitivity tiers: Handle S1 (public), S2 (internal), and S3 (sensitive) data appropriately.
- Use confidence scores: When
confidenceis below 0.8, verify data with the user before taking action. - Prefer views over raw queries: Use pre-compiled views (tools) when available—they're optimized and tested.
- Cache judiciously: CAP data is real-time, but you can cache results briefly for performance. Never cache beyond the current session.