Expert system for identifying deprecated patterns, suggesting refactoring to modern standards (Python 3.12+, ES2024+), checking test coverage, and leveraging AI-powered tools. Proactively applied when users request refactoring, updates, or analysis of legacy codebases.

Use when modifying, removing, or refactoring code that lacks test coverage. Emphasizes the danger of untested changes and the RGR workflow to add characterization tests before modifications.

Comprehensive legacy codebase analysis skill for technical debt assessment, security vulnerability scanning, performance bottleneck detection, and modernization roadmap generation. Includes 7 Python tools for automated codebase inventory, architecture health analysis, and strategic modernization planning.

This skill should be used when migrating features from src.legacy/ to the new kernel implementation or removing legacy code after reaching feature parity. Use for systematic legacy code removal, updating FEATURE_COMPARISON.md, verifying feature equivalence, and ensuring safe code retirement.

Modernize legacy applications and codebases. Use for COBOL conversion, framework upgrades, and technical debt reduction.

Refactor legacy codebases, migrate outdated frameworks, and

Provides expert legal guidance on technology law, contracts, compliance, and intellectual property matters. Specializes in data privacy regulations, software licensing, terms of service, and risk miti

Legal document analysis, contract review, and compliance guidance for business and personal legal matters

Legal and compliance expertise for corporate governance, contract analysis, regulatory compliance (SOX, GDPR, HIPAA), risk assessment, intellectual property, and litigation management. Use when reviewing contracts, ensuring compliance, or managing legal risk.

Extrai texto de documentos jurídicos escaneados em PDF usando OCR otimizado para linguagem jurídica brasileira. Use quando precisar converter PDFs escaneados (sentenças, petições, acórdãos) em texto editável com alta precisão. Suporta documentos de baixa qualidade, multi-colunas, tabelas e termos jurídicos específicos.

Framework for demonstrating AI capabilities in legal contexts. Provides detailed personas across tenant law, business contracts, startup disputes, employment claims, and consumer protection with progressive complexity scenarios. Use when: (1) Demonstrating AI-powered legal triage or intake systems, (2) Showcasing responsible AI-assisted client interactions, (3) Training staff on appropriate AI use in legal contexts, (4) Creating realistic scenarios for legal tech presentations, (5) Developing educational materials about AI in legal services, or (6) Testing AI-powered legal information systems in controlled environments.

Access legal documents and business formation services with LegalZoom.

Converts a Refound/Lenny Skill into a high-density, agent-executable Skill Pack (Agent Skills standard). Output must be in English.

Answer lesson-specific content questions. Use when a customer asks about a lesson, code example, or concept in a course.

Generate lesson content following 4-Layer Teaching Framework with standardized metadata and Docusaurus conventions

This skill should be used when educators, trainers, curriculum designers, or instructional designers need to create comprehensive lesson plans from a topic. The skill generates structured educational content with learning objectives aligned to Bloom's taxonomy, content outlines, interactive exercises, assessment quizzes, and instructor notes. It can read existing .docx and .pptx files for context and exports final lesson plans as .docx files. Triggers include requests to create lesson plans, generate curriculum, build training materials, or develop educational content.

Run Python code in lesson context with proper uv and venv handling for agent-spike project. Activate when user wants to run tests, demos, or CLI commands for lessons in lessons/ directories. Project-specific for agent-spike multi-agent learning.

Complete lessons learned standards, validation, and multi-file management. Single source of truth for all lessons learned operations including format, size limits, split procedures, and quality standards.

Use when capturing discoveries after phase completion, before shipping, or when reflecting on completed work to extract reusable patterns

Capture and review lessons learned from coding sessions. Use to record insights, read past lessons, and improve over time.

Letta framework for building stateful AI agents with long-term memory. Use for AI agent development, memory management, tool integration, and multi-agent systems.

Audit a codebase for the 12 leverage points of agentic coding. Identifies gaps and provides prioritized recommendations. Use when improving agentic coding capability, analyzing why agents fail, or optimizing a codebase for autonomous work.

Creates 4 root documentation files (CLAUDE.md, docs/README.md, documentation_standards.md, principles.md). L3 Worker invoked by ln-110-project-docs-coordinator.

CREATE/REPLAN Epics from scope (3-7 Epics). Batch Preview + Auto-extraction. Decompose-First Pattern. Auto-discovers team ID.

Creates Stories from IDEAL plan (CREATE) or appends user-requested Stories (ADD). Generates 8-section documents, validates INVEST, creates in Linear. Invoked by ln-220.

Replans Stories when Epic requirements change. Compares IDEAL vs existing, categorizes operations (KEEP/UPDATE/OBSOLETE/CREATE), executes in Linear.

RICE prioritization per Story with market research. Generates consolidated prioritization table in docs/market/[epic-slug]/prioritization.md. L2 worker called after ln-220.

Creates ALL task types (implementation, refactoring, test). Generates task documents from templates, validates type rules, creates in Linear, updates kanban. Invoked by orchestrators.

Updates ALL task types (implementation/refactoring/test). Compares IDEAL plan vs existing tasks, categorizes KEEP/UPDATE/OBSOLETE/CREATE, applies changes in Linear and kanban.

Orchestrates Story tasks. Prioritizes To Review -> To Rework -> Todo, delegates to ln-401/ln-402/ln-403/ln-404, hands Story quality to ln-500. Metadata-only loading up front.

Executes implementation tasks (Todo -> In Progress -> To Review). Follows KISS/YAGNI, guides, quality checks. Not for test tasks.

Worker that runs existing tests to catch regressions. Auto-detects framework, reports pass/fail. No status changes or task creation.

Security audit worker (L3). Scans codebase for hardcoded secrets, SQL injection, XSS, insecure dependencies, missing input validation. Returns findings with severity (Critical/High/Medium/Low), location, effort, and recommendations.

Dead code & legacy audit worker (L3). Checks unreachable code, unused imports/variables/functions, commented-out code, backward compatibility shims, deprecated patterns. Returns findings.

Orchestrates full project bootstrap from Replit export to production-ready structure

Upgrades npm/yarn/pnpm dependencies with breaking change handling

Upgrades .NET NuGet packages with breaking change handling

Upgrades Python pip/poetry/pipenv dependencies with breaking change handling

Generates .NET Clean Architecture backend structure from entity definitions

Generates Docker and docker-compose configuration for multi-container development

Generates GitHub Actions CI workflow configuration

Configures environment variables and secrets protection

Configures ESLint, Prettier, Ruff, and .NET analyzers

Configures Husky, lint-staged, commitlint, and Python pre-commit

Generates project-specific .claude/commands for Claude Code

Audits project dependencies for vulnerabilities. Multi-ecosystem support (npm, .NET, Python, Go). CVSS-based severity classification.

Coordinates logging, error handling, CORS, health checks, and API docs

Configures global exception handling middleware

Configures CORS policy for development and production

Configures health check endpoints for Kubernetes readiness/liveness/startup