「code quality」の検索結果

Use when analyzing code quality

日本語の概要は準備中です。原文の説明を表示しています。

Analyze raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite this prompt", or explicitly asks to enhance prompt quality. Also triggers on Chinese equivalents: "优化prompt", "改进prompt", "怎么写prompt", "帮我优化这个指令". DO NOT TRIGGER when: user wants the task executed directly, or says "just do it" / "直接做". DO NOT TRIGGER when user says "优化代码", "优化性能", "optimize performance", "optimize this code" — those are refactoring/performance tasks, not prompt optimization.

日本語の概要は準備中です。原文の説明を表示しています。

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.

日本語の概要は準備中です。原文の説明を表示しています。

Weekly engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware: breaks down per-person contributions with praise and growth areas.

日本語の概要は準備中です。原文の説明を表示しています。

Development workflows for WordPress ATmosphere plugin including wp-env setup, testing commands, linting, and build processes. Use when setting up development environment, running tests, checking code quality, or working with wp-env.

日本語の概要は準備中です。原文の説明を表示しています。

The Mileva Method (CRISP) — Phase 4: Spec. Full implementation readiness package including solution design, UX direction, tech stack, backlog, MVP prioritization, risk assessment, and AI architecture for Claude Code projects — CLAUDE.md, skill mapping, AI specs, sprint planning, quality gates. Triggers on "spec", "phase 4", "build ready", "implementation plan", "CLAUDE.md", "sprint planning", "backlog", "AI spec", or after Phase I exit checklist is complete.

日本語の概要は準備中です。原文の説明を表示しています。

Study a merged or shipped tdmcp implementation and extract reusable learnings: code improvements, runtime/UX lessons, test gaps, docs updates, roadmap items, and harness changes. Use when the user asks to learn from a completed feature/project/PR/build, analyze what can be improved from an implementation, or turn a real installation experience into actionable tdmcp improvements. This is a post-implementation learning harness; it does not replace tdmcp-pipeline for building or tdmcp-quality-audit for broad repo audits.

日本語の概要は準備中です。原文の説明を表示しています。

Use for analytics and data pipelines. Triggers: ETL, ELT, data quality, SQL optimization, schema drift, backfill, 데이터 파이프라인, 데이터 품질, 백필.

日本語の概要は準備中です。原文の説明を表示しています。

Use when Codex must make a high-quality judgment on any complex problem, not only technical problems. Trigger when a question has high complexity, uncertainty, cost of being wrong, long-term impact, value tradeoffs, possible structural causes, or when the user asks for 全面思考, 深度理解, 系统性分析, 帮我想清楚, 从根上看, 这件事到底怎么看, 方法论, 复杂问题拆解, 系统方案, 架构判断, 策略判断, or 根因分析. Also trigger for project and engineering high-risk scenarios such as multi-module, multi-environment, multi-data-source, multi-state, financial truth, live trading, ledger, revenue, customer assets, order governance, broker interfaces, online availability, major solution selection, and system closure.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working on imp's quantization formats, loaders, or dequant paths - GGUF Q4_0...Q8_0/Q*_K/IQ4/MXFP4, NVFP4 two-level scaling (Modelopt vs compressed-tensors layouts), FP8 E4M3, StorageTier, decode cache, KV-cache dtypes (auto/fp16/fp8/int8/int4/nvfp4/mxfp4), "which quant should I use", "which KV dtype", scale-factor layout, dequant kernel wiring, imp-quantize / AWQ, judging quant quality (PPL). Do NOT use for writing/optimizing GEMM/GEMV kernels (sm120-cuda-expert) or measuring quant perf (benchmark-cuda).

日本語の概要は準備中です。原文の説明を表示しています。

Create or refine durable engineering documentation: docs structure, navigation, source-of-truth placement, and writing quality. Use when a repo’s docs are missing, stale, duplicated, or hard to navigate, or when adding new durable engineering guidance.

日本語の概要は準備中です。原文の説明を表示しています。

Apply MCAF agile-delivery guidance for backlog quality, roles, ceremonies, and engineering feedback. Use when defining how the team plans, tracks work, and turns feedback into durable improvements.

日本語の概要は準備中です。原文の説明を表示しています。

Use webhint in .NET repositories that ship browser-facing frontends. Use when a repo needs CLI audits for accessibility, performance, security headers, PWA signals, SEO, or runtime page quality against a served site or built frontend output.

日本語の概要は準備中です。原文の説明を表示しています。

Apply MCAF documentation guidance for docs structure, navigation, source-of-truth placement, and writing quality. Use when a repo’s docs are missing, stale, duplicated, or hard to navigate, or when adding new durable engineering guidance.

日本語の概要は準備中です。原文の説明を表示しています。

Structures technology diligence with architecture review, code quality assessment, scalability analysis, and technical debt evaluation. Use when evaluating startup technology, assessing engineering teams, or reviewing technical infrastructure.

日本語の概要は準備中です。原文の説明を表示しています。

Guides developers through a human-supervised PDCA framework for AI code generation. Applies analysis, TDD, validation, and retrospection. Activates for sessions requiring systematic quality control.

日本語の概要は準備中です。原文の説明を表示しています。

Compress large context before reasoning to reduce token usage while preserving evidence. Use this whenever the user mentions huge files, long prompts, RAG payloads, prompt caching, expensive sessions, codebase context, chat history compaction, or wants the same answer quality with fewer tokens.

日本語の概要は準備中です。原文の説明を表示しています。

Generate context-aware quality checklists for code review and QA using IEEE 1028 base standards plus LLM contextual additions

日本語の概要は準備中です。原文の説明を表示しています。

GEO+SEO audit of asdlc.io — analyzes the codebase for Generative Engine Optimization across AI citability, technical infrastructure, content quality, schema markup, and knowledge base discoverability. Produces a composite GEO Score (0-100) with prioritized action plan.

日本語の概要は準備中です。原文の説明を表示しています。

Use after EZ_math_model coding has produced src/*.py and before packaging when code quality, reuse, vectorization, naming, or unnecessary complexity should be reviewed without changing behavior.

日本語の概要は準備中です。原文の説明を表示しています。

AI-First SDLC compliance rules and standards. Use when checking code quality, validating architecture, enforcing zero technical debt, or making any compliance decision.

日本語の概要は準備中です。原文の説明を表示しています。

Run the SDLC validation pipeline using direct tool invocations. No wrapper scripts required. Use when checking code quality before commits or PRs.

日本語の概要は準備中です。原文の説明を表示しています。

Scrape web pages to clean markdown using Firecrawl v2 — handles JS-heavy pages, site crawls, URL mapping, document parsing (PDF/DOCX/XLSX), LLM-powered extraction, autonomous agent scraping, and post-scrape browser interaction (Interact API). Free keyless mode available for search/scrape/parse/interact (no API key needed). Prefer over WebFetch for quality and completeness. Triggers on scrape URL, fetch page, crawl site, extract content, parse document, web to markdown, DeepWiki, Firecrawl.

日本語の概要は準備中です。原文の説明を表示しています。

Run technical quality checks across accessibility, performance, responsive design, theming, and anti-patterns. Generate a scored report with P0-P3 severity ratings. Report-only — documents issues for other skills to fix. Triggers on audit the design, check accessibility, technical review, performance audit, a11y check.

日本語の概要は準備中です。原文の説明を表示しています。