Results for “service-installation”

Discovers, installs, and manages AI agent skills from agentskill.sh, including searching, installing mid-session, scanning for security issues, and providing feedback.

Install, configure, update, or verify mcp-stata across Claude Code, Codex, Gemini CLI, Cursor, Windsurf, and VS Code. Activate when users ask to set up the Stata toolkit or troubleshoot the installation.

Use first for install/update routing — sends setup, doctor, or MCP requests to the correct OMC setup flow

Add or upgrade PWA features to any web app: service worker, offline mode, install prompt, push notifications, and background sync. Use when asked to "make it a PWA", "offline support", "install prompt", "push notifications", "service worker", "add to home screen", "background sync", "Lighthouse PWA score", "app-like.

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

Implements an ACP (Agent Communication Protocol) server that wraps a LangGraph-based deep agent, enabling session management, streaming message handling, and tool call progress tracking.

Integrates Dify with external systems via REST APIs, webhooks, and the MCP protocol, including client and server implementations.

Discover, evaluate, install, and wrap external agent skills from ClawHub registry, skillflag-compliant CLI tools, and GitHub repos. Use when asked to find new capabilities, install a skill, browse what's available, or package a local skill for sharing.

Cheshire Skills Store index — curated Agent Skills packages for Cheshire Terminal (API, PostHog, Google Agent Registry, Skill Hub onchain, Stripe, Solana common errors, NOXA) plus the community skills collection. Use when installing store skills, browsing the store catalog, or wiring cheshireterminal.ai/skills-store.

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".