AI Skills for OpenAI API

Facilitates end-to-end proof of OpenClaw Telegram behavior using real user sessions for thorough testing and PR reviews.

Facilitates debugging of OpenClaw models and tools by guiding users through log selection and proof path identification.

Diagnoses connection issues in OpenClaw across various platforms, ensuring proper node pairing and authentication.

Facilitates QA testing for OpenClaw scenarios, enabling users to run, debug, and extend testing workflows effectively.

Refactors OpenClaw documentation pages for clarity and accuracy, ensuring source-audited preservation and improved usability.

Facilitates efficient inbox triage by routing messages based on intent and managing replies through a structured TaskFlow pattern.

Facilitates the coordination of multi-step tasks with durable job management, ensuring state persistence and owner context.

Enables display of HTML content on OpenClaw-connected nodes, perfect for interactive demos and visualizations.

Creates unique lobster personas for OpenClaw AI Agents, generating character traits, rules, and visual prompts based on user preferences.

Provides repo-specific guidance for managing cross-platform coding tasks and maintaining synchronization across multiple AI coding platforms.

Instructor extracts structured data from LLM responses with Pydantic validation, ensuring type safety and automatic error handling.

OpenAI's Whisper model provides robust speech recognition for transcription, translation, and multilingual audio processing.

Enables delegation of coding tasks to OpenAI Codex CLI for efficient feature development, refactoring, and PR reviews.

Enables high-throughput LLM serving with OpenAI API compatibility, optimizing inference latency and GPU memory usage.

Facilitates remote AI agent pairing with browsers, enabling scoped access and seamless collaboration through HTTP requests.

Enables semantic vector search with AgentDB for efficient document retrieval and intelligent querying in RAG systems.

This skill audits third-party AI agent skills for security risks, ensuring safe installations by detecting malicious patterns.

Automates GitHub repository management with a structured 7-stage workflow for auditing, cleanup, and competitor analysis.

Extracts structured data from LLM responses using schema-constrained decoding for reliable JSON and typed objects.

Enables the creation of type-safe AI agents in Python, ensuring structured outputs and seamless integration with various LLMs.