v1truv1us-ai-eng-system

Contract-first design, Hyrum's Law, One-Version Rule, error semantics, boundary validation. Use when designing APIs, module boundaries, or public interfaces.

Chrome DevTools for live runtime data - DOM inspection, console logs, network traces, performance profiling. Use when building or debugging anything that runs in a browser.

Shift Left, Faster is Safer, feature flags, quality gate pipelines, failure feedback loops. Use when setting up or modifying build and deploy pipelines.

Conducts multi-axis code review. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human.

Simplifies code for clarity without changing behavior. Use when code works but is harder to read, maintain, or extend than it should be.

Multi-phase research orchestration for thorough codebase, documentation, and external knowledge investigation. Invoked by /ai-eng/research command. Use when conducting deep analysis, exploring codebases, investigating patterns, or synthesizing findings from multiple sources.

Enhance any content type using research-backed techniques. Optimize AI prompts with step-by-step approval, improve code quality, refine database queries, enhance documentation, optimize commit messages, and improve communication. Wraps incentive-prompting skill with content-type detection.

Feed agents the right information at the right time. Use when starting a session, switching tasks, or when output quality drops due to missing or stale context.

Deploy applications to Coolify self-hosting platform. Use when deploying to Coolify, configuring build settings, setting environment variables, managing health checks, or performing rollbacks.

Guides systematic root-cause debugging. Use when tests fail, builds break, behavior does not match expectations, or any unexpected error appears.

Build and reason about import graphs, call graphs, schema graphs, and service dependency graphs. Use when tracing paths, finding god nodes, surfacing cycles, or answering architecture questions about how parts connect.

Code-as-liability mindset, compulsory vs advisory deprecation, migration patterns, zombie code removal. Use when removing old systems, migrating users, or sunsetting features.

Architecture Decision Records, API docs, inline documentation standards. Use when making architectural decisions, changing APIs, or shipping features. Document the why, not the what.

Component architecture, design systems, state management, responsive design, WCAG 2.1 AA accessibility. Use when building or modifying user-facing interfaces.

Trunk-based development, atomic commits, change sizing (~100 lines), commit-as-save-point pattern. Use when making any code change.

Manage Git worktrees for parallel development. Use when creating isolated workspaces for parallel feature work, running multiple Claude sessions simultaneously, or managing concurrent development tasks.

Relationship-aware retrieval using graph traversal, entity anchors, community expansion, and hybrid vector plus graph search. Use when chunk similarity alone misses paths, entities, or subsystem context.

Knowledge graph development for LLM applications. Graph storage selection, graph algorithms, extraction from documents/code/websites, entity extraction, Graph RAG, and visualization. Use when building knowledge graphs, graph databases, or LLM graph applications.

Structured divergent/convergent thinking to turn vague ideas into concrete proposals. Use when you have a rough concept that needs exploration before committing to a spec.

Research-backed prompting techniques for improved AI response quality (+45-115% improvement). Use when optimizing prompts, enhancing agent instructions, or when maximum response quality is critical. Invoked by /ai-eng/optimize command. Includes expert persona, stakes language, step-by-step reasoning, challenge framing, and self-evaluation techniques.

Delivers changes in thin vertical slices. Use when implementing any feature or refactor that touches more than one file or feels too large to land safely in one pass.

Build a static-first knowledge architecture using file-backed domain maps, rules, hypotheses, and durable references. Use for knowledge architecture, learning systems, decision context, and long-lived team memory without runtime memory tooling.

Document solved problems to build cumulative team knowledge. Systematically capture solutions with context, code examples, gotchas, and related links. Use after completing workflows to ensure learnings compound for future team members.

Recursively initialize AGENTS.md in monorepo subdirectories with smart detection. Creates hierarchical agent context files with proper linking to root CLAUDE.md and parent AGENTS.md. Use for setting up multi-package projects, microservices, or any project with important subdirectories that need AI agent guidance.

Normalize mixed inputs like code, docs, PDFs, screenshots, diagrams, audio, and transcripts into a structured corpus. Use when the task depends on combining multiple artifact types before analysis or retrieval.

Measure-first approach for Core Web Vitals targets, profiling workflows, bundle analysis, and anti-pattern detection. Use when performance requirements exist or you suspect regressions.

Decompose specs into small, verifiable tasks with acceptance criteria and dependency ordering. Use when you have a spec and need implementable units.

This skill should be used when creating extensions for Claude Code or OpenCode, including plugins, commands, agents, skills, and custom tools. Covers both platforms with format specifications, best practices, and the ai-eng-system build system.

Comprehensive prompt engineering for coding agents covering structured instruction design, few-shot prompting, chain-of-thought, decomposition, agent workflow patterns, and reliability techniques for multi-step pipelines.

Transform prompts into structured TCRO format with phase-specific clarification. Automatically invoked by /ai-eng/research, /ai-eng/plan, /ai-eng/work, and /ai-eng/specify commands. Use when refining vague prompts, structuring requirements, or enhancing user input quality before execution.

OWASP Top 10 prevention, auth patterns, secrets management, dependency auditing, three-tier boundary system. Use when handling user input, auth, data storage, or external integrations.

Pre-launch checklists, feature flag lifecycle, staged rollouts, rollback procedures, monitoring setup. Use when preparing to deploy to production.

Ground every framework decision in official documentation. Use when you want authoritative, source-cited code for any framework or library.

Write a structured specification before writing code. Use when starting a new project, feature, or significant change. Defines objectives, commands, structure, code style, testing strategy, and boundaries.

Red-Green-Refactor workflow. Write tests before implementation, maintain 80%+ coverage. Use when implementing logic, fixing bugs, or changing behavior.

Comprehensive patterns and techniques for removing AI-generated verbosity and slop

Review recently changed files for code reuse, quality, and efficiency issues, then fix them. Alias for code-simplification skill with multi-agent review additions.

Continuous iteration loop pattern for well-defined tasks with clear completion criteria. Use when getting tests to pass, implementing features with automatic verification, bug fixing with clear success conditions, or running automated development overnight. Provides prompt templates, safety guidelines, and integration patterns for ai-eng-system workflows.