code-quality-review
Holistic, multi-dimensional code review skill providing prioritized, actionable feedback on correctness, security, performance, design, and accessibility.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
557 skills found
Holistic, multi-dimensional code review skill providing prioritized, actionable feedback on correctness, security, performance, design, and accessibility.
Professional trading strategy and risk management toolkit for prediction markets and crypto, featuring trend analysis, position sizing, and stop-loss frameworks.
A specialized code review agent that performs multi-dimensional analysis covering security vulnerabilities, performance optimization, code quality, and maintainability standards.
Automatically apply safe quality fixes including formatting (Black, isort), linting (Ruff auto-fixes), and resolving formatter conflicts to maintain Python code quality.
VVM (Vibe Virtual Machine) is a language for agentic programs where the LLM acts as the runtime. Orchestrate multi-agent workflows, manage state, and build resilient AI pipelines.
Generate high-quality, minimalistic, and geometric SVG logos using Python scripts. Ideal for icons, branding, and visual assets built with geometric primitives.
Intelligent research agent that automatically routes queries between fast web search, deep multi-source synthesis, and academic database lookups.
Manages free AI models from OpenRouter for OpenClaw. Ranks models by quality, configures fallbacks for rate-limit handling, and updates openclaw.json automatically.
Fullstack development agent for bkend.ai BaaS. Automates project init, auth/db setup, and API integration for Next.js applications.
A structured prompting framework to transform casual inputs into professional, modular LLM prompts with persona, context, task, format, and guardrails.
Aggregates and analyzes market sentiment for crypto and stock tickers by scanning news and social signals for quick trading vibe checks.
Classify and group meteorological and environmental variables into specific driver categories for consistent attribution analysis and environmental modeling.