clawmatch

Automate end-to-end dating workflows with REST API interactions and local state management.

1|Updated Mar 30, 2026
One-click install
npx skills add https://github.com/aaronyyleon-beep/clawmatch-skill --skill clawmatch
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: clawmatch
Source: https://github.com/aaronyyleon-beep/clawmatch-skill/tree/main/skill
Command: npx skills add https://github.com/aaronyyleon-beep/clawmatch-skill --skill clawmatch

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jq, curl, and includes scripts (resource) and references (resource) components.

What problem does it solve?

ClawMatch orchestrates end-to-end AI-assisted dating workflows, enabling agents to guide users through profile setup, questionnaire review, matchmaking, blind chat, and post-match human-chat support with centralized state management.

Core Features & Use Cases

  • Automates onboarding: registration and binding, push-consent, and state tracking for a seamless user start.
  • Manages questionnaire and AgentSoul creation: prefill, dynamic questions, and soulful profiling for better-match recommendations.
  • Supports matchmaking and blind chat: secure, structured match flows, scoring, and real-time conversational channels.
  • Presents match reports and helps make decisions: retrieve, review, and provide feedback on matches.

Quick Start

Install the ClawMatch skill, complete profile binding, and start the matchmaking workflow with the provided commands.

Frequently Asked Questions about clawmatch

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate end-to-end AI-assisted dating workflows from registration to chat?

Automating AI-assisted dating workflows requires coordinating registration, questionnaire processing, matchmaking, blind chat, and post-match human chat. ClawMatch orchestrates these steps with centralized state management, applying REST API interactions for secure profile binding and match flow.

How does questionnaire processing work for AI matchmaking profiles?

Questionnaire processing for AI matchmaking involves prefilling dynamic questions and creating soulful profiles. The workflow handles questionnaire review and AgentSoul creation, enabling better-match recommendations through structured scoring and profiling.

Can I use curl and jq to manage REST API interactions for dating match flows?

Managing REST API interactions for dating match flows with curl and jq is fully supported. The workflow uses these dependencies to handle registration, push consent, questionnaire processing, and match retrieval, while maintaining local state across multiple steps.

What's the best way to guide users through blind chat and post-match human chat?

Guiding users through blind chat and post-match human chat requires secure, structured match flows and real-time conversational channels. The workflow supports scoring, decision-making, and feedback retrieval, allowing agents to transition users from automated matchmaking to human-led conversations.

Does centralized state management work for tracking multiple dating workflow steps locally?

Centralized state management for tracking dating workflow steps works locally across registration, binding, push consent, and questionnaire handling. It ensures seamless user progression by maintaining state consistency throughout the matchmaking and reporting phases.

When do I need to retrieve and review match reports in an automated dating workflow?

Retrieving and reviewing match reports in an automated dating workflow is needed after the blind chat and scoring phases conclude. The workflow presents these reports to help users make decisions and provide feedback on their matches before proceeding to soul exchange.