Agent skills
Skills you can use with AI coding agents, indexed from public GitHub repositories.
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ralph-wiggum
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.
v1truv1us/ai-eng-system 6
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prompt-refinement
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.
v1truv1us/ai-eng-system 6
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simplify
Review recently changed files for code reuse, quality, and efficiency issues, then fix them. Use when simplifying code, removing complexity, improving readability, or after making changes.
v1truv1us/ai-eng-system 6
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comprehensive-research
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.
v1truv1us/ai-eng-system 6
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plugin-dev
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.
v1truv1us/ai-eng-system 6
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coolify-deploy
Deploy applications to Coolify self-hosting platform. Use when deploying to Coolify, configuring build settings, setting environment variables, managing health checks, or performing rollbacks.
v1truv1us/ai-eng-system 6
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knowledge-capture
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.
v1truv1us/ai-eng-system 6
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text-cleanup
Comprehensive patterns and techniques for removing AI-generated verbosity and slop
v1truv1us/ai-eng-system 6
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monorepo-initialization
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.
v1truv1us/ai-eng-system 6
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incentive-prompting
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.
v1truv1us/ai-eng-system 6
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content-optimization
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.
v1truv1us/ai-eng-system 6
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git-worktree
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.
v1truv1us/ai-eng-system 6
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ralph-wiggum
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.
v1truv1us/ai-eng-system 6
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prompt-refinement
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.
v1truv1us/ai-eng-system 6
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simplify
Review recently changed files for code reuse, quality, and efficiency issues, then fix them. Use when simplifying code, removing complexity, improving readability, or after making changes.
v1truv1us/ai-eng-system 6
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comprehensive-research
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.
v1truv1us/ai-eng-system 6
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plugin-dev
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.
v1truv1us/ai-eng-system 6
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coolify-deploy
Deploy applications to Coolify self-hosting platform. Use when deploying to Coolify, configuring build settings, setting environment variables, managing health checks, or performing rollbacks.
v1truv1us/ai-eng-system 6
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knowledge-capture
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.
v1truv1us/ai-eng-system 6
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text-cleanup
Comprehensive patterns and techniques for removing AI-generated verbosity and slop
v1truv1us/ai-eng-system 6
-
monorepo-initialization
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.
v1truv1us/ai-eng-system 6
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incentive-prompting
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.
v1truv1us/ai-eng-system 6
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git-worktree
Manage Git worktrees for parallel development
v1truv1us/ai-eng-system 6
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prompt-refinement
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.
v1truv1us/ai-eng-system 6