「make」の検索結果

Use when turning something that happened into something that makes the next run better - "write a retro", "record this paper cut", "this bug escaped verification", "make this an eval", "why do we keep hitting this", "did that change actually help", "scorecard", "what did we learn from that run", "lessons keep getting re-learned". Covers the six-step loop (capture, classify, convert, change, measure, retro), the repo artifact each step produces, and how escaped bugs become eval cases. Do NOT use to design or judge a single workflow experiment (see `workflow-experimentation`, which this loop feeds), to decide where to run work (see `dogfooding`), or to debug one failed execution in the moment.

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Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off. This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation. Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility

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Expert in building shareable generator tools that go viral - name generators, quiz makers, avatar creators, personality tests, and calculator tools. Covers the psychology of sharing, viral mechanics, and building tools people can't resist sharing with friends. Use when: generator tool, quiz maker, name generator, avatar creator, viral tool.

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GraphQL gives clients exactly the data they need - no more, no less. One endpoint, typed schema, introspection. But the flexibility that makes it powerful also makes it dangerous. Without proper controls, clients can craft queries that bring down your server. This skill covers schema design, resolvers, DataLoader for N+1 prevention, federation for microservices, and client integration with Apollo/urql. Key insight: GraphQL is a contract. The schema is the API documentation. Design it carefully.

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Use when it is time to make the backend follow the Lean model — regenerate Kotlin from Lean, implement the generated interfaces (UseCase, QueryService, Repository, Entity), wire every generated contract test, make the build green, then run the design and SQL reviews. Backend comes after the human confirmed the screens.

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Generate PPTX slide decks from a speech script or talking points — built for the 5-minute Monday morning speeches at Syla. Use when the user already has a script / 原稿 / メモ and asks to turn it into slides, a deck, or a presentation. Not for slides derived from a video — use youtube-slide-maker for that. Uses PptxGenJS directly (NOT html2pptx.js). Provides 4 slide template patterns, D3.js chart generation, and AI image integration.

Use when the user provides multiple options and wants a specific 16-minds (MBTI) personality to make the decision in that personality's judgment style. Triggers on Japanese phrases like 「ENTJで決めて」「INFPならどっち選ぶ」「INTJとして判断して」, English equivalents like "decide as ESTJ" / "which would ENFP pick", or 16-minds commands with a `--decide` flag. Requires both a personality and 2+ options to be present or extractable from the request.

Generate or edit images using the OpenAI Image API (gpt-image-2). Use when the user asks to generate, create, draw, render, illustrate, mock up, or edit an image, icon, logo, mockup, illustration, OG image, blog hero, marketing asset, or similar visual. Also use when the user supplies a reference image and asks to modify, restyle, or remix it. Triggers on: "generate an image", "create an image", "make a picture of", "edit this image", "restyle this", "make a mockup of", "draw a", "render a", "illustration of".

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。

Build AI presentation generation systems. TRIGGERS - Use when user needs help with ai-presentation-maker related tasks.

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。

Cooks and packages a shippable CARLA server from a UE 5.8 ue58-dev checkout via the CMake `package` targets (shipping/development/debug/test), scoping the cook with CARLA_MAPS_TO_COOK, and inspects an existing package for the failures a green build hides — stale pak/ucas mismatch, missing staged OpenDrive/TM road data, missing post-process profiles, and Town15's uncookable content. Use when the user asks to "package CARLA UE5", "cook a map", "build a shipping server", "make a distributable CARLA", or a package builds but renders black / has no maps.

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Packages an agent for CARLA Leaderboard submission — builds the docker image with make_docker.sh (plain or ROS variant), checks the four required roots and the egg layout it assumes, sets the agent/track/routes environment inside the image, and verifies the image can import the stack and load the agent before submission. Use when the user asks to "build the leaderboard docker image", "submit to the leaderboard", "package my agent", or make_docker.sh fails.

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Tunes the CARLA Traffic Manager that drives autopilot vehicles — global and per-vehicle speed, following distance, running lights/signs, lane-change behaviour, lane offset, TM-managed lights, deterministic seed, hybrid physics, and TM sync mode. Use when the user asks to "make the traffic faster/slower", "let cars run red lights", "keep more distance", "stop lane changes", "make traffic deterministic", or otherwise change how autopilot cars behave. For manually driving one car, use control-vehicle.

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Spawns pedestrians that wander autonomously via WalkerAIController, and destroys them in the correct order. Pairs each walker.pedestrian.* with a controller.ai.walker, places them on the navigation mesh, and sends them to random navmesh points at walking speed. Use when the user asks to "spawn pedestrians/walkers", "add people/crowds", "make pedestrians walk around", or "remove the walkers". Reads the world settings first: if the world is already synchronous it never ticks it, and if it is asynchronous it makes it synchronous and drives the clock itself.

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Adds accessibility annotations (ARIA labels, roles, alt text) to make web content accessible. Use when user asks to "add accessibility", "make accessible", "add aria labels", "wcag compliance", or "screen reader support".

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Create or update a pull request for the current branch. Trigger when the user asks to create/open/make a PR, or to edit/update/rewrite/fix the PR description, body, or summary — for both new PRs (`gh pr create`) and existing ones (`gh pr edit --body-file`). Use for any verb that lands on a PR's text content: "open a PR", "make a PR", "update the PR description", "rewrite the PR body", "fix the description".

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Windows アプリ開発 (MSIX / Microsoft Store) のパッケージング・配置・公開リファレンス。 MSIX, AppxManifest, MakeAppx, MSIX Packaging Tool, Package Bundles, App Attach, Package Support Framework, Package Identity, PackageFamilyName, PackageManager, スパースパッケージ, App Installer, サイドローディング, Windows App SDK Bootstrapper, 自己完結型デプロイ, SignTool, Azure Trusted Signing (Azure Artifact Signing), 証明書, CI/CD 署名, Partner Center, アプリ送信, 年齢レーティング, ストアリスティング, 段階的ロールアウト, StoreContext, msstore CLI, 認定プロセス, 分析レポート。

Change what something MEANS before you change what it IS — Rory Sutherland's cheapest lever. Use when a product, price, feature, or message is objectively fine but feels cheap, generic, boring, overpriced, or unjustified; when the user says 'how do we make this feel more premium/valuable/exciting', 'people don't get why it's worth it', 'how do we reposition/rename/repackage this', or wants to raise perceived value without changing the product or cutting the price. Covers renaming, recategorising, mental accounting (price framing), creating artificial value, and context-shifting. Inspired by Rory Sutherland's *Alchemy*. Routed to from psycho-logic. 日本語の相談（「高いと言われる」「安っぽく見える」「値下げせずに高級感を出したい」）にも使う。中文咨询（“被说太贵”“显得廉价”“不降价也想显得高级”）也适用。

Fix the psychologically significant moment, not the logically significant metric — Rory Sutherland on experience. Use when the outcome is fine but the journey feels bad: complaints about waiting, anxiety, uncertainty, or 'it's technically fast/cheap/correct but people still hate it'; when the user asks 'how do we reduce complaints', 'how do we make waiting/onboarding/checkout feel better', 'people are anxious during X', or is about to spend big to shave a number (wait time, load time, steps) when the real pain is how the moment feels. Covers uncertainty reduction, idle vs occupied waiting, and the peak-end rule. Inspired by Rory Sutherland's *Alchemy*. Routed to from psycho-logic. 日本語の相談（「待ち時間のクレームが多い」「手続きの途中で離脱される」「お客様を不安にさせている」）にも使う。中文咨询（“等待时间投诉多”“办理中途流失”“让客户感到不安”）也适用。

Add moments of joy, personality, and unexpected touches that make interfaces memorable and enjoyable to use. Elevates functional to delightful. Use when the user asks to add polish, personality, animations, micro-interactions, delight, or make an interface feel fun or memorable.

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Improve interface resilience through better error handling, i18n support, text overflow handling, and edge case management. Makes interfaces robust and production-ready. Use when the user asks to harden, make production-ready, handle edge cases, add error states, or fix overflow and i18n issues.

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pytestのマーク体系を設計・実装するスキル。「テストをマークで分類したい」「CIでDockerテストをスキップしたい」「Dockerコンテナ内で実行できないテストを除外したい」「make testでDockerテストを実行したい」「テストの実行対象を環境ごとに切り替えたい」「新しいマークを追加したい」「skipifを使いたい」「pytestの--strict-markersで未定義マークエラーが出る」という依頼で起動。マーク設計→pyproject.toml登録→テストコード付与→CI/ローカルコマンド整合の流れで実装する。

Resize, crop, or export any image or video into platform-ready social media assets using Adobe Creative Cloud tools. Use this skill when a user wants to prepare a photo, image, or video for one or more social platforms — Instagram, TikTok, LinkedIn, Facebook, YouTube, Snapchat, Pinterest, Threads, or X/Twitter. Triggers on: "prepare my image for Instagram", "resize for TikTok", "get this ready to post", "make versions for all platforms", "social media sizes", "crop for stories", "export for LinkedIn", "resize my video for social", "make social media assets", or any request to adapt a photo or video for specific platforms. Handles subject-aware cropping, AI canvas expansion, test previews before full runs, and same-ratio video resizing.

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Teaches the AI to design like a high-end agency. Defines the exact fonts, spacing, shadows, card structures, and animations that make a website feel expensive. Blocks all the common defaults that make AI designs look cheap or generic.

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Orchestrator for agent harness work — the setup that makes AI agents follow project rules and improve when they fail. FIRES PROACTIVELY when agents misbehave, repeat mistakes, ignore instructions, skip skills, or when AGENTS.md exists but docs/harness/manifest.json is missing. Also triggers on: harness engineering, agent scaffold, agent keeps failing, agent not following instructions, make agents reliable, agents going off rails, agent forgot context, improve agent setup, self-improving agents, agents keep making mistakes, why is my agent bad, agent quality, agent setup broken, agents ignore skills, same mistake again, fix agent behavior, tune agent instructions, set up agent infrastructure, after project setup agents still bad. Routes bootstrap vs evolution. Not multi-agent topology — agent-builder.

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。