Results for “cli”

Use when the user wants to build, extend, or debug the Python MCP bridge that exposes Mermate actions and stage flows to OpenClaw or other MCP clients.

Creates and runs LLM-as-judge evaluators on Arize, including managing tasks, column mappings, and continuous monitoring.

Generate songs, instrumental tracks, and covers using the MiniMax Music API with basic or advanced control modes.

Generate multi-speaker dialogue audio with different voices in a single file using the inference.sh CLI.

Provides a persistent, queryable virtual filesystem backed by SurrealDB for AI agents, with a Rust core and a Python agent interface.

Routes prompts to any LLM model across multiple providers via CLI tools or APIs, with auto-discovery of new models and benchmark data.

Ingests EPUBs and ebooks into a local vector index, then answers questions and searches passages via a command-line interface.

Use the @steipete/oracle CLI to bundle a prompt plus the right files and get a second-model review (API or browser) for debugging, refactors, design checks, or cross-validation.

Plain-English translation layer for non-technical Copilot CLI users. Translates every approval prompt, error message, and technical output into clear, jargon-free English with color-coded risk indicators.

Use the @steipete/oracle CLI to bundle a prompt plus the right files and get a second-model review (API or browser) for debugging, refactors, design checks, or cross-validation.

Use the @steipete/oracle CLI to bundle a prompt plus the right files and get a second-model review (API or browser) for debugging, refactors, design checks, or cross-validation.

Use when the task requires automating a real browser from the terminal (navigation, form filling, snapshots, screenshots, data extraction, UI-flow debugging) via `playwright-cli` or the bundled wrapper script.

Drive Soup (`soup-cli`), a CLI-first tool for fine-tuning and post-training LLMs with one YAML config and one command — SFT, DPO/GRPO/ORPO/SimPO/KTO, QLoRA/DoRA/LoRA+, layer streaming for 4-8 GB GPUs, eval-gated training, and serving. Use when the user wants to `soup init`/`soup train` a model, pick a training method or quantization scheme, estimate cost/memory before training, fine-tune on a small local GPU, migrate a config from Axolotl/LLaMA-Factory/Unsloth, or serve/merge/push a trained adapter. Triggers on: "soup-cli", "soup train", "soup init", "fine-tune an LLM locally", "QLoRA on a laptop GPU", "layer streaming", "soup advise", "soup autopilot", "DPO/GRPO/ORPO training", "merge LoRA adapter".

Proposes exactly one personal side-project idea per invocation, sized to a 1-3 day MVP. Targets CLI, automation, LLM, DX, productivity, and data-viz angles; avoids clichés like TODO apps, weather apps, and pomodoro timers. Output is an 8-section brief including a ready-to-paste coding-agent prompt. Use for morning/daily idea rituals and weekend-hack ideation. Don't use for existing-product feature proposals (Spark), dialogue brainstorming (Riff), or prototype implementation (Forge).

Creates reusable command-line scripts and utilities for the Hugging Face API, enabling chaining, piping, and intermediate data processing.

Summarize recorded video clips using the LVS microservice with a VLM fallback, producing a narrative summary with timestamped events.

Build cross-platform AI chat interfaces with Makepad using MolyKit, including async utilities, chat widgets, and OpenAI-compatible client integration.

Build, debug, and extend MCP servers and clients that connect AI systems with external tools and data sources.

Generate video clips with Google Veo (Veo 3.1 / 3.0) via a Python script, supporting duration, aspect ratio, and model selection.

Ingests EPUB and ebook files into a local vector index and provides a command-line interface for asking questions about the books.

Use when adding a new agent harness (CLI-based coding agent) adapter to adapters. Covers capability audit, adapter scaffold, session parsing, auth detection, hooks/plugins wiring, tests, and docs.

Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.

Guides through interactive deployment of Azure OpenAI models with full control over version, SKU, capacity, content filtering, and advanced options.

Explains the domain concepts of the cuOpt REST server, including supported problem types, request flow, and key endpoints, without deployment or client code.

Quickly query genomic reference databases for Ensembl IDs, gene metadata, sequences, BLAST searches, and enrichment analysis using the gget CLI or Python package.

Queries Microsoft 365 Copilot data (emails, meetings, documents, Teams, people) using the WorkIQ CLI/MCP server for live context, summaries, and recommendations.

Install and configure Acontext, a memory layer for AI agents that provides persistent sessions, file storage, and skill management.

Connects error monitoring to MCP clients, enabling issue search, stack trace analysis, performance investigation, and AI-powered root cause analysis.

Coordinates shopping between buyers and merchants: publish products, manage stock, answer questions, compare prices, and create trackable orders with local or registry-backed state.

Translates a Chinese article into English Markdown, generates bilingual detailed summaries, and writes the results back to LanceDB while preserving structure.

Scores genetic compatibility between all male-female pairings in a Genomebook generation, ranking optimal mating pairs based on heterozygosity, trait complementarity, and disease risk.

Creates Azure OpenAI model deployments with intelligent intent-based routing, supporting quick presets, full customization, and capacity discovery across regions and projects.

Generate spoken audio for narration, voiceovers, IVR prompts, and accessibility reads using the OpenAI Audio API with bundled CLI and built-in voices.

Query NFT and token data, browse drops, stream events, and search across Ethereum, Base, Arbitrum, Optimism, Polygon, and more using the OpenSea API.

Classifies HTML pages, forms, and fields using machine learning to detect page types, form types, and field types from HTML content or URLs.

Scaffolds a coder-eval task suite in a repository by scanning for testable components, writing one real task, and validating it.