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Specialized agent for multi-repository analysis, searching remote codebases, retrieving official documentation, and finding implementation examples using GitHub CLI, Context7, and Web Search. Use proactively when unfamiliar libraries or frameworks are involved, working with external dependencies, or needing examples from open-source projects to understand best practices and real-world implementations.

Configure build systems, optimize bundle size, manage exports for ESM/CJS/UMD, and publish packages to NPM with proper versioning

Standardized library design patterns for autonomous-dev including two-tier design, progressive enhancement, non-blocking enhancements, and security-first architecture. Use when creating or refactoring Python libraries.

Detect project stack from package manifests (package.json, pyproject.toml, go.mod, Cargo.toml, pubspec.yaml, CMakeLists.txt). Auto-identify frameworks, test tools, and build systems for onboarding.

License compliance checking and conflict detection

Navigate long-term life direction through reflective dialogue and decision frameworks. This skill helps you explore values, make significant decisions, and maintain intentional direction without endle

Sets up Lighthouse CI to automatically test performance, accessibility, SEO, and best practices in your CI/CD pipeline with budget enforcement and trend tracking.

Use this skill when the user mentions LimaCharlie or wants to work with endpoint detection, cloud security monitoring, detection rules, or security automation. This provides an overview of LimaCharlie components and their interconnections.

Use this skill when new users want to get started with LimaCharlie, set up their first organization, or begin collecting security data. Guides beginners through org creation and helps identify what to onboard, then hands off to specialized skills.

This skill provides a comprehensive workflow for implementing Linear issues with professional software engineering practices. It automates the entire development lifecycle from issue analysis through

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Esta skill deve ser usada quando o usuário solicitar candidaturas automáticas em vagas do LinkedIn relacionadas a Inteligência Artificial (IA) no Brasil, priorizando vagas com cadastro simplificado (Easy Apply). A skill filtra vagas por palavras-chave de IA, localização no Brasil, nível de senioridade (se especificado) e tipo de candidatura simplificada.

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

Master essential Linux skills for penetration testing including navigation, file manipulation, text processing, networking, process management, permissions, and bash scripting. Linux is the preferred

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.

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

SQLite disaster recovery and streaming replication to cloud storage (S3, GCS, Azure, SFTP, NATS). Use this skill for configuring Litestream, deploying to cloud platforms, troubleshooting WAL replication issues, implementing point-in-time recovery, and setting up VFS read replicas.

Comprehensive guide for building functional tools for LiveKit voice agents using the @function_tool decorator. Use when creating tools for LiveKit agents to enable capabilities like API calls, database queries, multi-agent coordination, or any external integrations. Covers tool design, RunContext handling, interruption patterns, parameter documentation, testing, and production best practices.

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).

Guide for building production-ready LiveKit voice AI agents with multi-agent workflows and intelligent handoffs. Use when creating real-time voice agents that need to transfer control between specialized agents, implement supervisor escalation, or build complex conversational systems.

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

Navigate and load project living documentation for context from .specweave/docs/internal/. Use when implementing features and needing project context, referencing ADRs for design decisions, or accessing specs and architecture docs. Provides table of contents for all documentation types.

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.

LlamaIndex Wolfram Alpha tool for computational knowledge queries, math solving, scientific calculations, and agent integration. Triggers: wolfram alpha, computational query, math solver, scientific calculation, WolframAlphaToolSpec.

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.

Expert LLM architect specializing in large language model architecture, deployment, and optimization. Masters LLM system design, fine-tuning strategies, and production serving with focus on building scalable, efficient, and safe LLM applications.

LLM-powered documentation generation for narrative architecture docs, tutorials, and developer guides. Uses AI consultation to create contextual, human-readable documentation from code analysis and spec data.

Extract structured data from construction documents using LLMs. Process RFIs, submittals, contracts, specifications. Convert unstructured PDFs to structured JSON/Excel.

Comprehensive LLM model evaluation and ranking system. Use when users ask to compare language models, find the best model for a specific task, understand model capabilities, get pricing information, or need help selecting between GPT-4, Claude, Gemini, Llama, or other LLMs. Provides benchmark-based rankings, cost analysis, and use-case-specific recommendations across reasoning, code generation, long context, multimodal, and other capabilities.

Top orchestrator for complete doc system. Delegates to ln-110 coordinator (project docs via 5 L3 workers) + ln-120-150 workers. Phase 4: global cleanup. Idempotent.

Coordinates project documentation creation. Gathers context once, detects project type, delegates to 5 L3 workers (ln-111-115). L2 Coordinator invoked by ln-100.

Creates 2 backend docs (api_spec.md, database_schema.md). L3 Worker invoked CONDITIONALLY when hasBackend or hasDatabase detected.

Creates test documentation (testing-strategy.md + tests/README.md). Establishes testing philosophy and Story-Level Test Task Pattern. L2 Worker in ln-100-documents-pipeline workflow.

Builds interactive HTML presentation with 6 tabs (Overview, Requirements, Architecture/C4, Tech Spec, Roadmap, Guides). Creates presentation/README.md hub. L2 Worker under ln-100-documents-pipeline.

CREATE/REPLAN Stories for Epic (5-10 Stories). Delegates ln-001-standards-researcher for standards research. Decompose-First Pattern. Auto-discovers team/Epic.

Orchestrates test planning pipeline (research → manual → auto tests). Coordinates ln-511, ln-512, ln-513. Invoked by ln-500-story-quality-gate.

Researches real-world problems, competitor solutions, and customer complaints before test planning. Posts findings as Linear comment for ln-512 and ln-513.

Performs manual testing of Story AC via executable bash scripts saved to tests/manual/. Creates reusable test suites per Story. Worker for ln-510.

Plans automated tests (E2E/Integration/Unit) using Risk-Based Testing after manual testing. Calculates priorities, delegates to ln-301-task-creator. Worker for ln-510.

Semantic content auditor (L3 Worker). Verifies document content matches stated SCOPE, aligns with project goals, and reflects actual codebase state. Called by ln-600 for each project document. Returns scope_alignment and fact_accuracy scores with findings.

Architecture audit worker (L3). Checks DRY (7 types), KISS/YAGNI, layer breaks, error handling, DI patterns. Returns findings with severity, location, effort, recommendations.

Code principles audit worker (L3). Checks DRY (7 types), KISS/YAGNI, TODOs, error handling, DI patterns. Returns findings with severity, location, effort, recommendations.

Test suite audit coordinator (L2). Delegates to 5 workers (Business Logic, E2E, Value, Coverage, Isolation). Aggregates results, creates Linear task in Epic 0.

L3 Worker. Analyzes single pattern implementation, calculates 4 scores (compliance, completeness, quality, implementation), identifies gaps and issues. Usually invoked by ln-640, can also analyze a specific pattern on user request.

L3 Worker. Audits architectural layer boundaries, detects violations (code in wrong layers), checks pattern coverage. Invoked by ln-640 once per audit.

Coordinates project structure migration to Clean Architecture

Coordinates Docker, CI/CD, and environment configuration setup via auto-detection