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Analyze and optimize CLAUDE.md files for quality improvements and token reduction. Supports analysis-only, simplification-only, or full optimization modes. Use when asked to "review", "optimize", "simplify", "improve", "check", or "reduce tokens" in CLAUDE.md.
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Структурированная 6-шаговая процедура для улучшения, реновации или перестройки существующих пайплайнов, отдельных папок проектов, структур документации или стек-технологий. Вызывается как "pipeline optimizer" (для целых тематических пайплайнов, например, пайплайнов разработки ПО, исследований или создания игр) или "project-folder optimizer" (для отдельных папок проектов внутри пайплайна, например, единичного инструмента ПО или проекта статьи). Активируется при выполнении таких задач, как "улучшить пайплайн X", "оптимизировать стек", "перестроить Y", "реновация", "рефакторинг пайплайна", "навести порядок в папке проекта", "улучшить структуру папки", "унифицировать конвенции", "консолидация документации", "интегрировать в существующую систему" или при любом существенном вмешательстве в устоявшиеся структуры. Обеспечивает анализ существующего фонда, уточнение целей, эскиз идеального состояния, план устранения разрывов, эмпирическое выявление проблемных мест и повторное тестирование с помощью свежих субагентов. Предотвращает появление параллельных стандартов, дублирование и сбои в пайплайне.
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结构化的6步骤流程，用于改进、改造或重构现有的流水线（pipeline）、独立项目文件夹、文档结构或软件技术栈。可调用为“pipeline optimizer”（针对整个主题流水线，例如软件、研究或游戏开发流水线）或“project-folder optimizer”（针对流水线内的独立项目文件夹，例如单个软件工具或论文项目）。触发于诸如“改进流水线X”、“优化技术栈”、“重构Y”、“改造”、“流水线重构”、“清理项目文件夹”、“改进文件夹结构”、“统一规范”、“文档整合”、“集成到现有系统”或针对既有结构的任何实质性干预等任务。提供既有资产调查、目的明确化、理想蓝图草图、差距规划、实证痛点识别以及使用全新的子智能体进行重新测试。防止并行标准、重复建设和流水线中断。
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Procedimiento estructurado de 6 pasos para mejorar, renovar o reconstruir pipelines existentes, carpetas de proyectos individuales, estructuras de documentación o stacks de software. Direccionable como "pipeline optimizer" (para pipelines temáticos completos, p. ej. de software, investigación o desarrollo de videojuegos) o "project-folder optimizer" (para carpetas de proyectos individuales dentro de un pipeline, p. ej. una herramienta de software o proyecto de artículo académico). Se activa ante tareas como "mejorar pipeline X", "optimizar el stack", "reconstruir Y", "renovación", "refactorización de pipeline", "limpiar carpeta de proyecto", "mejorar estructura de carpetas", "unificar convenciones", "consolidación de documentación", "integrar en sistema existente" o cualquier intervención sustancial en estructuras establecidas. Proporciona análisis del estado actual, aclaración del propósito, boceto del estado ideal, plan de brechas, identificación empírica de puntos de dolor y reevaluación con subagentes limpios. Previene estándares paralelos, duplicación y rupturas en el pipeline.
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Optimize and convert Zenn Flavored Markdown (ZFM) articles to Dev.to (Forem) compatible Markdown. Use this skill whenever the user wants to post a Zenn article to Dev.to, needs to convert markdown for cross-posting, or asks to "optimize for dev.to". It handles front matter conversion, Liquid tag embeds, KaTeX math blocks, callout transformations, and general formatting adjustments required by Dev.to.
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Optimize a single Jetpack Compose @Composable function. Use when the user wants to improve, polish, animate, or refactor ONE Composable — keywords like "optimize this composable", "make this smoother", "add animation to this", "split this composable", "fix recomposition", "this composable is laggy", "polish this UI", or when user pastes/points to a single @Composable function. Analyzes the target for recomposition issues, missing animations, size/concern splits, and stability problems, then iterates with the user via AskUserQuestion to pick which optimizations to apply, then delegates implementation to the kta-compose-developer agent and summarizes changes. Input MUST be exactly one Composable (file path + function name, or function name alone).
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Reduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
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Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.
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Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
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Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts between OpenAI, Claude, or Gemini, or build prompt evals.
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Optimize AI system latency and throughput. TRIGGERS - Use when user needs help with ai-latency-optimizer related tasks.
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Optimize embedding strategies for AI retrieval. TRIGGERS - Use when user needs help with ai-embedding-optimizer related tasks.
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Warehouse cost reduction, auto-scaling, query optimization, and lifecycle policies for data infrastructure. Activate on: data cost, warehouse credits, cost reduction, auto-scaling, lifecycle policy, cold storage, cost monitoring, resource optimization. NOT for: query performance tuning (use data-warehouse-optimizer), batch job optimization (use batch-processing-optimizer).
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CI/CD caching optimizer for dependency caching, Docker layer caching, and build speed improvements. Activate on: CI cache, build speed, dependency caching, Docker layer cache, turbo remote cache, GitHub Actions cache, pnpm store cache. NOT for: CI/CD pipeline creation (use github-actions-pipeline-builder), deployment strategy (use blue-green-deployment-orchestrator), Docker image building (use docker-multi-stage-optimizer).
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Optimizes Pine Script for performance, user experience, and visual appeal on TradingView. Use when improving script speed, reducing load time, enhancing UI, organizing inputs, improving colors and visuals, or making scripts more user-friendly. Triggers on "optimize", "improve", "faster", "better UX", "clean up", or enhancement requests.
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Use when the user asks to "optimize entity presence", "build knowledge graph", "improve knowledge panel", "entity audit", "establish brand entity", "Google does not know my brand", "no knowledge panel", or "establish my brand as an entity". Works standalone with public search and AI query testing; supercharged when you connect ~~knowledge graph + ~~SEO tool + ~~AI monitor for automated entity analysis. For structured data implementation, see schema-markup-generator. For content-level AI optimization, see geo-content-optimizer.
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Audit and optimize headings across AEM Edge Delivery Services pages for search intent, hierarchy, and consistency. Extracts all headings (H1-H6), evaluates uniqueness, keyword alignment, specificity, and structural correctness, then generates optimized heading suggestions per page. Use when improving on-page SEO, fixing heading hierarchy issues, or standardizing heading patterns across a site.
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Optimizes Dockerfiles for smaller images, faster builds, better caching, and security hardening using multi-stage builds and best practices. Use when users request "optimize Dockerfile", "reduce Docker image size", "Docker best practices", or "containerize application".
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根据开发目标选择ECC命令组，说明执行顺序与停止条件，并读取当前命令文件，按用途整理清单，帮助组合多个命令；仅提供指导，不执行命令。
使用DSPy构建问答、文档检索等AI流程，定义输入输出并组合可复用组件。利用训练示例和评估指标自动调整提示词，比较调整前后的输出结果。
将反复交给AI的任务整理为可复用技能，也可测试和改进已有技能。通过比较输出、收集用户意见和查看性能指标，修改技能指令及调用条件。
Turn a repeatable workflow into a skill, or improve an existing one through test prompts and feedback. Compare results with and without the skill, review performance measurements, and refine its description so it triggers appropriately.
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Retrieve compacted ReasoningBank context for cost-analysis queries and summarize retrieved memories, reported token savings, cache hit rate, and package availability. The savings figure is an estimate from the integration, not a measured comparison against a baseline.
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Review and rewrite Twitter drafts using insights from its open-source recommendation algorithm. The skill examines audience fit, replies, sharing, and negative reactions, then explains suggested edits and ways to refine your content strategy.
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