Automatic quality control, linting, and static analysis procedures. Use after every code modification to ensure syntax correctness and project standards. Triggers onKeywords: lint, format, check, validate, types, static analysis.

How to check code by linting, building, and testing.

Checks code for linting issues and style compliance before commits

Check code for style and quality issues. Use when validating code before commits.

Iteratively run linters, apply auto-fixes, and resolve remaining issues using Trunk.

Expert assistant for analyzing and fixing linting and formatting issues in the KR92 Bible Voice project using Biome and TypeScript. Use when fixing lint errors, resolving TypeScript issues, applying code formatting, or reviewing code quality.

Detect and fix violations of project instructions defined in .claude/rules/. Use when checking code compliance, reviewing changes, or when the user asks about instruction violations.

lint-kb

Execute markdown validation with taxonomy-based classification and custom rules. Use when validating markdown compliance with LLM-facing writing standards or when generating structured validation reports.

Unified linting and auto-fix for Python (Ruff) and TypeScript (ESLint) in monorepo. Use when fixing lint errors, running pre-commit checks, or diagnosing persistent code quality issues. Orchestrates auto-fix first, then root-cause analysis.

lint-skill

1. Navigate to frontend directory and run lint:

Run formatters on files based on the user's conform neovim configuration. Supports multiple languages including JavaScript, TypeScript, Python, Go, Nix, Lua, and more. Use this when the user asks to format, lint, or clean up code files.

Run linting and fix code quality issues in the codebase

Cross-language linter autofix commands and common fix patterns for biome, ruff, clippy, shellcheck, and more.

Explain lint errors and propose fixes. Use when a junior developer needs help resolving common lint or format warnings.

Enforces validation pattern compliance across all agent profiles with automated detection and fixing

ring:linting-codebase

Generic linting workflow for multiple languages with auto-fix and error resolution guidance

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Unified linter runner. One command, JSON output, all issues sorted by file:line.

Generates .lintstagedrc configuration to automatically fix and format staged files before commit. Runs ESLint, Stylelint, and Prettier on staged files.

You are a Linux kernel development expert specializing in device drivers, kernel modules, and subsystem development. You follow strict kernel coding standards and use modern kernel APIs.

iOS 26/macOS 26 Liquid Glass design system with complete API coverage. Use when user asks about iOS 26 design, Liquid Glass, glassEffect modifier, GlassEffectContainer, morphing animations, HIG compliance, visual styling, or the new Apple design language.

Lisa - intelligent assistant for memory and tasks. Triggers on 'lisa', 'hey lisa', or addressing lisa directly.

Validate Lisp code (Clojure, Racket, Scheme, Common Lisp) for syntax errors, parenthesis balance, and semantic issues. This skill should be used when validating Lisp code files, checking for syntax errors before execution, or validating LLM-generated Lisp code including incomplete or partial expressions. Provides structured JSON output optimized for automated workflows.

Fetch component names from Sippy component readiness API

Query and return raw JIRA bug data for a specific project

List all server-side noridocs, optionally filtered by repository and/or path prefix.

Fetch and analyze component health regressions for OpenShift releases

Use when user wants to inventory autonomy branches with custom sorting, grouping, or filtering

Lightweight skill generator with style learning - creates simple skills using flow-based execution and style imitation. Use for quick skill scaffolding, simple workflow creation, or style-aware skill generation.

Testing patterns for litefs-py and litefs-django. Use when writing tests, setting up fixtures, understanding test organization, or configuring pytest marks. Triggers: test, pytest, unit test, integration test, property-based testing, hypothesis, fixtures, in-memory adapters.

When calling LLM APIs from Python code. When connecting to llamafile or local LLM servers. When switching between OpenAI/Anthropic/local providers. When implementing retry/fallback logic for LLM calls. When code imports litellm or uses completion() patterns.

CRITICAL: ALWAYS activate this skill BEFORE making ANY changes to .nw files. Use proactively when: (1) creating, editing, reviewing, or improving any .nw file, (2) planning to add/modify functionality in files with .nw extension, (3) user asks about literate quality, (4) user mentions noweb, literate programming, tangling, or weaving, (5) working in directories containing .nw files, (6) creating new modules/files that will be .nw format. Trigger phrases: 'create module', 'add feature', 'update', 'modify', 'fix' + any .nw file. Never edit .nw files directly without first activating this skill to ensure literate programming principles are applied. (project, gitignored)

Method×Setting matrices and systematic gap identification

This skill guides configuring Litestream for continuous SQLite backup in Rails 8+ apps. Use when setting up production backups for SQLite databases (Solid Queue, Solid Cache, Solid Cable).

Build and review production-grade web and mobile frontends using LiveKit with Next.js. Covers real-time video/audio/data communication, WebRTC connections, track management, and best practices for LiveKit React components.

Guide for creating effective prompts and instructions for LiveKit voice agents. Use when building conversational AI agents with the LiveKit Agents framework, including (1) Creating new voice agent prompts from scratch, (2) Improving existing agent instructions, (3) Optimizing prompts for text-to-speech output, (4) Integrating tool/function calling capabilities, (5) Building multi-agent systems with handoffs, (6) Ensuring voice-friendly formatting and brevity for natural conversations, (7) Iteratively improving prompts based on testing and feedback, (8) Building industry-specific agents (debt collection, healthcare, banking, customer service, front desk).

Principles for writing simple, maintainable Laravel/Livewire code. Use when writing Livewire components, tests, or Blade views. Focuses on avoiding over-engineering.

Launch or resume Living Docs Builder independently. Generates comprehensive enterprise documentation from codebase analysis with AI-powered insights. LSP-enhanced by default for accurate API extraction.

Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support

When setting up local LLM inference without cloud APIs. When running GGUF models locally. When needing OpenAI-compatible API from a local model. When building offline/air-gapped AI tools. When troubleshooting local LLM server connections.

Expert LLC operations management for ID8Labs LLC (Florida single-member LLC). 9 specialized agents providing PhD-level expertise in compliance, tax strategy, asset protection, and business operations. Triggers on keywords like LLC, taxes, expenses, annual report, EIN, compliance, bookkeeping, deductions, filing, sunbiz, quarterly, S-Corp, retirement, audit, insurance, cash flow, mentor, teach, learn.

This skill should be used when building production LLM applications in any language. It applies when implementing predictable AI features, creating structured interfaces for LLM operations, configuring language model providers, building agent systems with tools, optimizing prompts, or testing LLM-powered functionality. Covers language-agnostic patterns for type-safe contracts, modular composition, multi-provider support, and production deployment.

Use when user needs LLM system architecture, model deployment, optimization strategies, and production serving infrastructure. Designs scalable large language model applications with focus on performance, cost efficiency, and safety.

Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent-introduced cruft.

Multi-level caching strategies for LLM applications - semantic caching (Redis), prompt caching (Claude/OpenAI native), cache hierarchies, cost optimization, and Langfuse cost tracking with hierarchical trace rollup for 70-95% cost reduction