2K skills ・ Works with: OpenAI · sorted by popularity
Verify OpenClaw installation, onboarding, gateway operation, and upgrades in Parallels virtual machines running macOS, Windows, or Linux. The skill guides baseline restoration, bounded test runs, log interpretation, and focused retries when checks fail.
Launch native or web apps on a temporary Crabbox machine and display them in the chat side panel. The skill covers setup, readiness checks, screen interaction, reopening previews, and stopping apps or releasing the environment.
Delegate feature development, large code refactors, and pull request reviews to Codex, Claude Code, or OpenCode as background workers. Use isolated project checkouts, monitor progress, and arrange completion or failure notifications.
Transcribe audio files through OpenAI's transcription API and save the results as text or JSON. Select a model, specify the language, or use speaker labeling to distinguish who said each part of a recording.
Summarize articles, YouTube videos, podcasts, PDFs, and local files to understand their main points. Choose the summary length or extract source content, including available YouTube transcripts, for closer reading and further processing.
Add a model provider to OpenClaw and verify it with a real response. The skill validates configuration, keeps credentials in environment or file references, and reports test results and any default model changes.
Bundle a question and selected project files for a second AI model to review code, investigate bugs, or assess design options. Preview the files and token usage before running through an API or browser.
Restructure repeated LLM API requests so shared instructions and reference material can be cached, meaning reused across calls. Track cached input, write and storage costs, and response times to assess whether the changes actually save money.
Apply shared coding conventions to new modules, reviews, and refactoring. Guides descriptive naming, readable structure, updates that preserve existing data, and error handling, giving teams a consistent basis for checking code quality.
Review authentication, payments, and APIs for security risks using implementation examples and a predeployment checklist. Covers input validation, access permissions, secret storage, request limits, and checks for sensitive information in logs and errors.
Clarify the availability and requirements of model serving on booked Itô GPUs. The supplied implementation does not support serving, so it reports that limitation and documents future booking verification, configuration review, and explicit confirmation requirements.
Develop applications through separate planning, building, and evaluation agents. The workflow tests the running app, scores its design and functionality, and feeds specific issues back into repeated development cycles with configurable quality thresholds and iteration limits.
Add one optional Codex critique to a decision draft produced by council. With explicit consent to send the draft to OpenAI, examine weak conclusions, missing failure scenarios, and overlooked dissent before the user makes the final decision.
Review and refine code using shared conventions for clear names, readability, error handling, and updates that preserve original data. Identify duplication, oversized functions, and unnecessary complexity to make code easier to understand and maintain.
Turn audio and video into text across 99 languages, translate speech into English, and create timestamped subtitles. The skill covers model selection, transcription settings, and batch processing for recordings such as meetings and podcasts.
Set up vLLM to serve large language models through an OpenAI-compatible API or process prompts in batches. The skill covers deployment, performance monitoring, and quantization, which reduces model memory requirements.
Generate model responses in defined formats for data extraction and classification. Use JSON schemas, Pydantic data models, regular expressions, or fixed choices to produce structured results that other programs can parse and validate.
Find language models on Hugging Face Hub and run them locally with llama.cpp. The skill helps compare GGUF model files, choose compressed versions that fit available memory, and prepare commands for a local chat server or command-line session.
Build question answering and document retrieval workflows with DSPy by defining inputs, outputs, and reusable components. Use training examples and evaluation metrics to automatically optimize prompts and compare results before and after optimization.
Improve prompts, regex patterns, SQL queries, or small code snippets through repeated scoring and revision. The skill helps define measurable tests, generate new candidates with an AI model, and inspect results to find better-performing versions.
Test how API-accessible language models respond to attempts to bypass safety filters. The skill covers prompt templates, input transformations, parallel response comparisons, and Hermes configuration, with automated scoring of refusals, answer quality, and speed.
Build agent memory with Pinecone by storing searchable representations of conversations and documents. Retrieve relevant past context for answers, organize memory by session or user, and prototype semantic search that finds information by meaning.
Add an AI assistant to an existing web app so users can operate its interface with everyday instructions. Covers embedding the panel, connecting a language model, testing interactions, and protecting API keys in production.
Configure Hermes to run AI models through Actual Computer on your own hardware. Set up either the hosted relay or a local connection, select models, verify responses, and troubleshoot request errors or empty output.