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Design effective logos with AI image generation via inference.sh CLI.

Structured prompting techniques for generating high-quality videos across multiple AI platforms. \n \n Covers eight video generation models (Veo, Seedance, Wan, Grok, Kling, Runway, Pika, Sora) with model-specific optimization tips \n Provides a reusable prompt formula combining shot type, subject, action, setting, lighting, style, and technical parameters \n Includes reference tables for shot types, camera movements, lighting keywords, and visual aesthetics with practical examples \n Demonstrat

Systematic seven-step novel writing workflow with AI-assisted planning, consistency tracking, and quality validation. \n \n Guides writers through constitution (core principles), specification (story requirements), clarification (resolving ambiguities), planning (structure and technique), task breakdown (actionable steps), writing (AI-assisted drafting), and analysis (quality checks) \n Automatically activates genre-specific skills (Romance, Mystery, Fantasy) and writing technique skills (Dialog

Create explainer videos from script to final cut via inference.sh CLI.

Use this skill in two modes:

This skill provides comprehensive guidelines for authoring content in adev/src/content. It combines Google's technical writing standards with Angular-specific markdown conventions, components, and best practices.

Guide users through their first complete trade on Hyperliquid via Senpi. This skill teaches the core loop: discover top traders → create a strategy that mirrors a chosen trader → monitor → close strategy.

Essential reference guide for ADK agent development, covering spec-driven workflow, code preservation rules, and operational best practices. \n \n Read DESIGN_SPEC.md first as your primary source of truth; all implementation decisions must align with it \n Follow four mandatory phases: understand spec, build and implement, evaluate (using /adk-eval-guide ), and deploy with human approval \n Preserve all unrelated code when making modifications; only alter explicitly targeted segments and never c

Generate and maintain Terraform code following HashiCorp's official style conventions. \n \n Enforces two-space indentation, lowercase underscore naming, and standard file organization across terraform.tf , providers.tf , main.tf , variables.tf , outputs.tf , and locals.tf \n Requires type and description on all variables and outputs, with validation rules and sensitive flag support for credentials \n Prioritizes for_each over count for dynamic resources, applies security hardening (encryption,

Comprehensive evaluation methodology guide for ADK agents covering metrics, schemas, and iteration workflows. \n \n Provides eight evaluation criteria (tool trajectory, response matching, rubric-based scoring, hallucination detection, safety) with configurable thresholds and judge model options \n Includes evalset schema documentation with multi-turn conversation support, tool use trajectory specification, and session state initialization patterns \n Outlines the eval-fix loop: start small, run