Results for “optimize”

Builds, validates, packages, tests, and optimizes deployment-ready Skill files (SKILL.md + references/ + scripts/ + agents/) from design specifications. Three pre-build gates + 9-dimension quality gate (with D9a/b/c empirical evidence tiers) + behavioral iteration loop + automated description optimizer + blind version comparator. Use for building, reviewing, revising, testing, or optimizing Skills. Trigger on: "build this Skill," "review this Skill," "test my Skill," "optimize the description," "compare these Skill versions," "should this be a hook instead," "build a template first." Also: "my Skill doesn't activate," "my description truncates," "my SKILL.md is too long," "my Skill is overtriggering." Do NOT use when designing Skill methodology — that's design work, not packaging. Do NOT use for prompt compilation, project auditing, or prompt scoring (use rootnode-prompt-compilation, rootnode-project-audit, or rootnode-prompt-validation if available).

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Optimize ChatGPT Voice in the Codex desktop app into an ear-first control plane for free-form task coordination and opt-in workflows. Apply spoken synthesis, routing-only coordination, owning-task role contracts, project placement, explicit authority, safe speech, current-state verification, honest recovery, and the optional Slopware Dev Stack: MSL, MSW, CODER Loop, and Timebox. Load named workflows only when invoked: Freeway for parallel lanes, Decision Walkthrough for read-only authority convergence, and Tutorial for teaching-first onboarding. Use when the user invokes the voice optimizer, coordinates Codex work through voice, assigns the current voice task as coordinator, runs Freeway, starts a decision walkthrough, learns voice orchestration, routes development through CODER, authorizes Timebox, or asks CVO to install or use Slopware companions.

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Use when asked to optimize code, make a path faster, make this as fast as possible, reduce allocations, repair a regression, profile a target, grill every inefficiency, or run a subsystem or repo-wide performance campaign against a measured floor; when a performance requirement, slowness report, Core Web Vitals miss, or profiling evidence identifies a bottleneck; or for an "extremely optimize" performance campaign, optimizing suspected hot paths without waiting for benchmarks, or estimating hot and complexity-neutral cold paths while refusing complexity theater. Not for remote, credential, publish, deploy, or irreversible changes.

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Optimize a pydantic-ai agent's instructions, tool descriptions, output type, and signature inputs using the gepa CLI. Trigger when the user asks to "optimize this agent", "improve tool descriptions", iterate on prompts driven by eval failures, or otherwise improve a pydantic-ai agent against a dataset. Operates in the user's repo with full filesystem access — YOU are the reflection model; the gepa CLI handles minibatches, evaluation, and Pareto bookkeeping.

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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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Agent skill for matrix-optimizer - invoke with $agent-matrix-optimizer

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"Optimize multilingual product listings for international e-commerce including SEO localization, machine translation workflows, and cultural adaptation. Use this skill when the user needs to create product listings in multiple languages, optimize for local search, or adapt marketing content for different markets — even if they say 'translate our listings', 'optimize for local SEO', 'adapt for the Japanese market', or 'our translated listings don't convert'.".

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"Optimize SQL query performance using EXPLAIN analysis, indexing strategies, and common anti-pattern fixes. Use this skill when the user needs to speed up slow queries, design indexes, fix N+1 problems, or optimize database performance — even if they say 'this query is slow', 'optimize our database', 'which indexes do we need', or 'our dashboard takes 30 seconds to load'.".

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"Design and analyze factorial experiments to identify significant process factors and optimize settings. Use this skill when the user needs to systematically test factor effects, optimize a manufacturing process, or determine which variables matter most — even if they say 'which factors affect quality', 'optimize process settings', or 'design an experiment'.".

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Optimizes Atlassian Forge apps to reduce platform consumption and avoid unnecessary costs using Atlassian's "Optimise Forge platform costs" guidance. Use when the user asks to optimize Forge app costs, reduce Forge invocations, lower GB-seconds, reduce storage or log usage, tune memory, replace polling, improve scheduled triggers, reduce KVS writes, move work to the frontend, use bridge APIs, batch API calls, add caching, or evaluate Forge Remote trade-offs. By default, perform an audit first and offer to make the recommended changes after presenting the audit. Only modify files immediately when the user explicitly asks the agent to implement or apply optimizations.

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Runs autonomous keep/discard experiments on a codebase to optimize a single metric for a fixed duration, in the style of karpathy/autoresearch. Use when the user says "autoresearch" (optionally with a focus, e.g. "autoresearch the optimizer"), asks to run experiments on a repo overnight, to hill-climb or optimize a metric autonomously, or points at a repo with a karpathy-style program.md.

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Scaffold and operate Claude Blog Brain, a source-cited Obsidian brain for blog content creation, optimization, and management dual-optimized for Google rankings (E-E-A-T, the 2026 core updates) and AI citations (GEO/AEO), spanning writing, rewriting and freshness, SERP-informed briefs and outlines, editorial calendars and strategy, semantic topic clusters, schema and internal linking, multilingual publishing, the FLOW framework, factchecking, personas, distribution, and the blog delivery contract, grounded in the claude-blog skill. Use when the user says "claude-blog-brain", "Claude Blog Brain", "create a blog content creation, optimization, and management dual-optimized for Google rankings (E-E-A-T, the 2026 core updates) and AI citations (GEO/AEO), spanning writing, rewriting and freshness, SERP-informed briefs and outlines, editorial calendars and strategy, semantic topic clusters, schema and internal linking, multilingual publishing, the FLOW framework, factchecking, personas, distribution, and the blog delivery contract, grounded in the claude-blog skill brain", "import sources", "synthesize plan", "render report", or wants a persistent vault-backed operating system for blog content creation, optimization, and management dual-optimized for Google rankings (E-E-A-T, the 2026 core updates) and AI citations (GEO/AEO), spanning writing, rewriting and freshness, SERP-informed briefs and outlines, editorial calendars and strategy, semantic topic clusters, schema and internal linking, multilingual publishing, the FLOW framework, factchecking, personas, distribution, and the blog delivery contract, grounded in the claude-blog skill.

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Use when executing admin-level cleanup in a Salesforce org: deleting unused fields, removing inactive Flow versions, deactivating legacy automation, running Salesforce Optimizer or Org Check, and performing destructive metadata deploys. Triggers: 'clean up unused fields', 'delete inactive flows', 'run Optimizer', 'org has too many custom fields', 'remove old workflow rules', 'Flow version limit'. NOT for assessing or reporting on technical debt (use architect/technical-debt-assessment). NOT for code-level refactoring or Apex cleanup (use apex/ skills).

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Use this skill to design, configure, and troubleshoot the Field Service Lightning (FSL) Optimizer — including selecting optimization type (Global, In-Day, Resource Schedule), understanding the priority-score model, configuring travel mode (Aerial vs Street-Level Routing), and tuning optimizer behavior for specific scheduling objectives. NOT for work rules and service-objective weights on a scheduling policy — use admin/fsl-scheduling-policies. NOT for ESO adoption and territory sizing — use architect/fsl-optimization-architecture.

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Design, analyze, and optimize cold outbound email campaigns on Instantly. Handles end-to-end ICP definition, expert panel scoring (recursive to 90+), sequence copywriting, infrastructure audit, capacity planning, and an implementation/strategy doc for human review. Use when asked to build cold outbound sequences, optimize cold email, audit an outbound motion, write sales sequences, or design cold email campaigns. Supports "start from scratch" and "optimize existing" modes. Execution skill — it produces copy, math, and a doc; it does not auto-send. Requires Instantly for audit/send features.

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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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Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.

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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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Optimize prediction-pool tips (kicktipp etc.): rules + multi-book consensus odds → expected-points-max answer for every question, scores AND bonus. Triggers 'optimize my pool tips', 'predict'.

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Audit one or all supported agent harnesses (claude, codex, opencode, pi, omp, antigravity, cursor, grok) for config efficiency, hook overhead, permission friction, plugin and MCP cost, skill metadata portability, dead config, and measured session friction. Use when invoked as `$optimize-harness [harness ...] [focus]`, or when the user asks to optimize or compare agent harness configuration.

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Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo

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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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