Skill UI

This comprehensive skill guides the user through the entire process of creating a deep, multi-layered AI persona (Soul.md). It helps define the identity, core rules, name, and visual prompts for agents designed for the OpenClaw platform. Users can initiate character creation via guided prompts or a random 'gacha' draw, ensuring a cohesive and compelling character profile.

A comprehensive multi-agent feedback loop inspired by Generative Adversarial Networks (GANs), designed to build complex, production-ready applications. It separates generation from evaluation, using a 'ruthlessly strict' evaluator (QA) to drive continuous iteration. This process ensures high design quality, robust functionality, and originality, overcoming the limitations of single-agent self-critique. Ideal for full-stack feature development and complex product specification implementation.

AgentFlow transforms your existing Kanban board (Asana, GitHub, Linear) into a self-managing, autonomous AI development pipeline. It orchestrates multiple AI agents through defined stages (Backlog to Done), enforcing deterministic quality gates, performing adversarial reviews, and tracking costs—all visible within your existing project management UI. It provides crash-proof execution and full observability for complex software development lifecycles.

AgentPhone is an API-first platform designed for developers building AI agents with advanced telephony capabilities. It allows agents to manage phone numbers, make/receive voice calls, and handle SMS messages. Developers can utilize hosted mode for simple AI interactions or webhook mode for custom, complex logic, making it ideal for building sophisticated customer support bots or virtual assistants.

Guides calling Feishu IM APIs to retrieve chat histories, thread replies, cross-chat searches, and media/file downloads while handling pagination, time filters, and permission constraints so agents can answer questions about past conversations.

Generates a precise, evidence-based installation plan for a project's component catalog (ECC). Instead of loading a full, noisy bundle, this skill analyzes the repository's actual code structure, dependencies, and CI/CD evidence to classify skills, rules, and commands into 'DAILY' (essential) and 'LIBRARY' (referenceable) buckets. It ensures only necessary components are active, optimizing the development environment.

Automates CI/CD pipeline setup, from linting and type checks to integration, E2E, security, and deployment stages, plus feedback loops for AI agents and preview deployments.

Guides AI agents through analyzing working code, spotting overly complex structures, then refactoring for clarity while preserving exact behavior and project conventions, especially after reviews or high-pressure edits.

A comprehensive guide for writing, reviewing, and structuring technical documentation for Go projects. It covers best practices for godoc comments, READMEs, contribution guidelines, and changelogs, ensuring documentation is accurate, discoverable, and tailored for both human readers and AI agents. Applicable to both libraries and CLIs.

A robust CLI tool for connecting to Lark/Feishu real-time events. It allows streaming, subscribing, and consuming various events (e.g., IM messages, reactions, meeting updates) as NDJSON output. Ideal for developing Lark bots, building real-time message processors, or implementing long-running streaming webhook handlers, especially in AI agent subprocesses.

This guide details best practices for using `lark-cli` to interact with Feishu resources. It covers initial configuration, switching between user and bot identities, and crucial authentication flows for AI agents. It provides detailed instructions on handling permission errors, implementing secure split-flow authentication, and managing high-risk write operations to ensure data integrity and user consent.

This meta-skill set provides a structured framework covering the entire software development lifecycle (SDLC). It guides developers through every phase, from initial concept refinement and requirement specification to incremental implementation, testing, code review, and deployment. It enforces critical professional behaviors like surfacing assumptions, managing ambiguity, and maintaining scope discipline to ensure high-quality, robust engineering outcomes.