「output」の検索結果

Integrate on-device AI using Foundation Models framework, Core ML, and open-source LLM runtimes on Apple Silicon. Covers Foundation Models (LanguageModelSession, @Generable, @Guide, SystemLanguageModel, structured output, tool calling), Core ML (coremltools, model conversion, quantization, palettization, pruning, Neural Engine, MLTensor), MLX Swift (transformer inference, unified memory), and llama.cpp (GGUF, cross-platform LLM). Use when building tool-calling AI features, working with guided generation schemas, converting models, or running on-device inference.

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。

21 production-ready scripts for iOS app testing, building, and automation. Provides semantic UI navigation, build automation, accessibility testing, and simulator lifecycle management. Optimized for AI agents with minimal token 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。

Use when someone asks to write, rewrite, review, or improve text that appears inside a product or interface. Examples: "review the UX copy", "is there a better way to phrase this", "rewrite this error message", "write copy for this screen/flow/page", reviewing button labels, improving CLI output messages, writing onboarding copy, settings descriptions, or confirmation dialogs. Trigger whenever the request involves wording shown to end users inside software — apps, web, CLI, email notifications, modals, tooltips, empty states, or alerts. Also trigger for vague requests like "review the UX" where interface copy review is implied. Do NOT trigger for content marketing, blog posts, app store listings, API docs, brand guides, cover letters, or interview questions — this is a technical writing skill for interface language.

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 XcodeBuildMCP to build, run, launch, and debug the current iOS project on a booted simulator. Trigger when asked to run an iOS app, interact with the simulator UI, inspect on-screen state, capture logs/console output, or diagnose runtime behavior using XcodeBuildMCP 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。

Analyze Swift and mixed-language compile hotspots using build timing summaries and Swift frontend diagnostics, then produce a recommend-first source-level optimization plan. Use when a developer reports slow compilation, type-checking warnings, expensive clean-build compile phases, long CompileSwiftSources tasks, warn-long-function-bodies output, or wants to speed up Swift type checking.

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。

Cuts fluff and leads with what matters. Applies Parkinson's Law, Pareto, and Goodhart's Law so every word earns its place. Use when the user wants terse output, names this skill, or signals that responses are too padded.

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。

Choose OpenRouter models and routers, query live catalog data, use structured outputs and batch inference, and debug routing with metadata. Use when working with OpenRouter APIs, openrouter/auto, openrouter/free, model discovery, pricing, providers, batch jobs, or structured JSON responses.

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。

Formats output for ADHD-friendly action — lead with the next step, number tasks, restate state, cut fluff. Use when named (adhd), when the user mentions ADHD or focus/executive-function constraints, or when they ask for steps they can execute immediately.

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。

Generates as many digits of pi as possible and writes them into files inside a tokenMaxxing/ folder. Use when the user asks to token-maxx, fill a file with pi digits, test agent output throughput, or generate N pi files sequentially or in parallel.

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: reviewing or producing any external-facing prose — ADRs, proposals, GitHub comments, Telegram messages, cover letters, client drafts, or planner-mode wording. Removes AI-tell patterns and raises writing quality before output leaves the team.

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。

Solution architect that creates implementation blueprints before building. Maps 2-3 approaches ranked by simplicity, creates phased build plans with rollback points, and checks dependencies. Use when you need to design a solution, create a blueprint, plan before building, or take a task from the audit and need an implementation plan. Part of the architect-system loop. Reads from system/audit-report.md. Outputs to system/blueprints/.

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 review partner that compounds productivity gains over time. Use when: (1) "weekly review", "what did I accomplish this week?", "compound my gains", (2) End of work week to review progress, (3) "what patterns are emerging?", (4) "update the system map", (5) Feeling stuck and need to identify friction. Reads all system/ files to synthesize progress. Outputs to system/compounder/week-{date}.md. Feeds insights back to /audit.

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 review partner that compounds productivity gains over time. Tracks velocity, logs friction, sets next-week targets, recognizes patterns across weeks, and feeds insights back to the audit for the next loop. Use when you want a weekly review, need to identify friction, want to see patterns, or want to update your system map. Part of the architect-system loop. Outputs to system/compounder/week-{date}.md.

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。

Productivity analyst that maps your workflow, scores tasks by time cost and energy drain, and builds a prioritized 4-week automation plan. Use when you want to audit your workflow, figure out what to automate, do a productivity review, identify high-leverage tasks, feel overwhelmed and need to prioritize, or after the compounder surfaces new friction. Part of the architect-system loop. Outputs to system/audit-report.md.

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。

Productivity analyst that maps your workflow, scores tasks, and builds a prioritized automation plan. Use when: (1) "audit my workflow", "what should I automate?", "productivity review", (2) Starting a new project and need to identify high-leverage tasks, (3) Feeling overwhelmed and need to prioritize, (4) After /compounder surfaces new friction or patterns. Reads compounder insights when available. Outputs to system/audit-report.md.

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。

Senior engineering analyst for code review, plan review, and automation review. Four-lens review covering architecture, code quality, reliability, and performance, each scored 1-10. Delivers APPROVE, REVISE, or REJECT verdicts. Use when you need a code review, plan review, quality check, or want to verify work before shipping. Part of the architect-system loop. Reads from system/blueprints/ and source code. Outputs to system/reviews/.

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。

Solution architect that creates implementation blueprints before building. Use when: (1) "architect this", "design a solution", "blueprint for", (2) Taking a task from the audit report and need an implementation plan, (3) "how should I build this?", "plan before building", (4) Before any non-trivial implementation. Reads from system/audit-report.md. Outputs to system/blueprints/.

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。

Guidance for using asc cli in this repo (flags, output formats, pagination, auth, and discovery). Use when asked to run or design asc commands or interact with App Store Connect via the CLI.

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 about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

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。

Expert in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when: prompt engineering, system prompt, few-shot, chain of thought, prompt design.

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。

CLI tool for AI-powered web scraping, data extraction, search, and crawling via ScrapeGraph AI. Use when the user needs to scrape websites, extract structured data from URLs, convert pages to markdown, crawl multi-page sites, search the web for information, automate browser interactions (login, click, fill forms), get raw HTML, discover sitemaps, or generate JSON schemas. Triggers on tasks involving: (1) extracting data from websites, (2) web scraping or crawling, (3) converting webpages to markdown, (4) AI-powered web search with extraction, (5) browser automation, (6) generating output schemas for scraping. The CLI is just-scrape (npm package just-scrape).

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。

Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms).

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。

Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON 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。

Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.

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。