「make」の検索結果

🇺🇸 Product Launch & Product Hunt #1 Playbook — Multi-channel launch timing and sequencing. T-14 warm-up → T-0 launch day → T+7 momentum. Hunter selection, maker comment templates, comment cadence, anti-shadowban rules. Distilled from 30+ Product Hunt #1 daily launches across 150+ AI startups. **Supports 3 product paths: developer/maker (PH+GitHub), B2C consumer (Reddit/communities/TikTok, skip PH), B2B (LinkedIn+media).** 🇨🇳 产品发布与 Product Hunt 夺冠手册 — 多渠道发布的时序与节奏。T-14 预热 → T-0 发布日 → T+7 后续。Hunter 选择、maker comment 模板、评论节奏、防 shadowban。提炼自 30+ 次 Product Hunt 日冠、150+ AI 创业公司实战。**支持三种产品路径：开发者/maker（PH+GitHub）、2C 消费者（社区种子/TikTok，跳过 PH）、B2B（LinkedIn+媒体）。** 🇯🇵 プロダクトローンチ＆Product Hunt 1位プレイブック — マルチチャネルのローンチ時系列。T-14準備→T-0当日→T+7モメンタム。ハンター選定、メーカーコメント、コメントリズム、シャドウバン回避。**3つの製品パス対応：開発者/maker（PH+GitHub）、2C消費者（コミュニティ種・TikTok、PHスキップ）、B2B（LinkedIn+メディア）。** 🇰🇷 제품 출시 & Product Hunt 1위 플레이북 — 멀티채널 출시 타이밍과 시퀀싱. T-14 워밍업 → T-0 출시일 → T+7 모멘텀. 헌터 선정, 메이커 코멘트, 댓글 리듬, 섀도밴 방지. **3가지 제품 경로 지원: 개발자/maker(PH+GitHub), 2C 소비자(커뮤니티 씨드/TikTok, PH 건너뜀), B2B(LinkedIn+미디어).** Triggers: "product launch" | "launch strategy" | "Product Hunt" | "PH launch" | "how to launch" | "go to market" | "GTM" | "launch day" | "find a hunter" | "maker comment" | "launch sequencing" | "发布策略" | "产品发布" | "发 PH" | "Product Hunt 准备" | "找 hunter" | "ローンチ" | "출시 전략"

Luamake 构建系统指南——用于当前项目的 `luamake` / `make.lua` / Ninja 生成流程。当用户需要编写、修改、排查或理解 `make.lua`、目标定义、`lm:conf`、`deps` / `objdeps`、代码生成、Lua C 模块、Bee 运行时集成，或需要解决当前项目中由 `luamake` 驱动的构建问题时，使用此 skill。即使用户没有明确提到 `luamake`，但上下文明显是在处理本项目的构建脚本、构建目录、目标依赖或 `luamake` 生成的 Ninja 流程，也应使用此 skill。不要把它用于纯通用的 C/C++ 编译知识、与本项目无关的 CMake / xmake / Bazel / Meson 问题，或仅讨论编译器理论而未涉及 `luamake` / `make.lua` 的请求。

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Best practices for authoring clean, maintainable, and portable GNU Make Makefiles. Use when the task involves `creating or editing Makefiles`, `Makefile`, `makefile`, `*.mk`, `GNUmakefile`, `GNU Make patterns`, `build automation`, or `makefile troubleshooting`.

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。

Best practices for authoring clean, maintainable, and portable GNU Make Makefiles. Use when the task involves `creating or editing Makefiles`, `Makefile`, `makefile`, `*.mk`, `GNUmakefile`, `GNU Make patterns`, `build automation`, or `makefile troubleshooting`.

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GNU Make / Makefile リファレンス。 ターゲット、prerequisite、order-only、変数展開、自動変数、pattern rule、.PHONY、-j 並列・再帰 make、増分ビルド。 Rust / Cargo / xtask、Node.js / pnpm / Turborepo、Git hooks / CI との責務分離と開発コマンド共通化。 Makefile 設計・レビュー、Bash サンプル、診断スクリプト。

Selects, deploys, and customizes AI models on Amazon SageMaker. Training or Processing jobs, fine-tuning (SFT/DPO/RLVR/RLAIF), model selection, dataset preparation, evaluation, SageMaker or Bedrock deployment, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing which base model to customize, fine-tune, or deploy from SageMaker JumpStart or Hub, SageMakerPublicHub, or the SageMaker public model catalog, transforming or validating training data, evaluating model quality, deploying or optimizing endpoints, configuring IAM/S3 for training, or managing SageMaker Managed MLflow. Use for endpoint health, failures, latency, logs, metrics, errors. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.

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GCC 嵌入式工程构建工具（CMake + arm-none-eabi-gcc），用于扫描 CMake 型嵌入式工程、 列出预设、配置、编译、重建、清理和分析 ELF 大小。当用户提到 GCC、arm-none-eabi、 CMake 嵌入式编译、Ninja 构建、ELF 大小分析、arm-gcc、交叉编译、cmake --build、 cmake --preset 时自动触发，也兼容 /gcc 显式调用。即使用户只是说"编译一下"或 "看看固件多大"，只要上下文涉及 CMake 嵌入式 GCC 工程就应触发此 skill。

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Audits Makefiles for build correctness, portability, and recipe duplication. Use when reviewing a Makefile or before committing Makefile changes.

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Make an original animated music video (MV / PV) for a song from its audio file plus lyrics, in the Vocaloid / J-pop lyric-video tradition: 手書き/tegaki (V家手书, 手描きMV, ボカロPV), 文字PV and kinetic typography (リリックモーション, リリックビデオ, 歌詞動画, 歌词视频, 动态歌词), anime-illustration MVs with an original character — for any mood, from dark ballads to bright upbeat summer pop. Renders to MP4 with a deterministic Canvas2D/WebGL2 engine in a headless browser: JIZURA lyric motion, AI-generated art, optional Blender toon 3D, beat-locked editing, 9:16 vertical/Shorts version. Use it whenever the user wants a music video, MV, PV, lyric video or beat-synced animation for a song, e.g. "make an MV for my song", "turn this track into a lyric video", "给这首歌做个MV/PV", "做个V家手书", "この曲のMVを作って", "ボカロPV作って", "縦型ショートも" — even if they only give a .wav/.mp3 and say "make a cool video for it". Not for editing existing footage or clip compilations.

Generate beautiful, on-brand HTML artifacts — reports, diagrams, diff reviews, slide decks, plans, recaps, dashboards. Loads on creation intents ("create a plan", "make me an implementation plan", "generate a diagram", "build a slide deck", "draft a recap", "visualize this", "render this as HTML", "make me a status update") and exploration intents ("show me three layouts", "explore document shapes", "compare visual treatments", "explore a new design system", "compare replacement brand directions") and iteration intents ("make this beautiful", "polish this report", "make this less generic", "design this", "theme this", "use my brand", "iterate on this design", "review this artifact", "make this bolder", "make this quieter", "simplify this design", "animate this", "improve the look of", "give this a refresh"). Captures brand from the user's codebase, asking, or live site, then applies it to every artifact.

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Authors reproducible bioinformatics pipelines with Snakemake - rules wired by output-file pattern, wildcards and expand() for sample fan-out, checkpoints for runtime-unknown outputs, resource/retry escalation, and conda/container software deployment on HPC and cloud. Use when deciding rule-based (Snakemake) vs channel/dataflow (Nextflow) authoring; wiring rules by OUTPUT-file pattern rather than imperative order; using wildcards + expand() for sample fan-out and constraining them to stop silent mis-routing; adding checkpoints when the set of outputs is unknown until a step runs (dynamic DAG); diagnosing why a job reran (or did not) under the mtime-plus-provenance trigger set; escalating memory on retry for OOM-killed jobs; and porting a Snakemake 7 `--cluster`/remote-provider command to the Snakemake 8+ executor-plugin and storage-plugin model (snakemake-executor-plugin-slurm) with `--software-deployment-method`.

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make review / make review-close があるプロジェクトで、Codex の PR レビューと修正を最大5周回す。「レビューループ」「review-loop」「make review で回して」で発火。各レビューを対象 commit と対応付け、根拠を確認して修正し、完了後にクローズする。

Builds CARLA on Unreal Engine 5.8 from the ue58-dev branch using its CMake build system — configure a preset, then build the carla-unreal / carla-unreal-editor / carla-python-api-install / launch targets — with ROS 2, DLSS, RSS and cook-scope options, and the engine-side build of the CarlaUnreal UE 5.8 fork. There is no Makefile and no Util/BuildTools in this tree, so every UE4 `make` recipe is invalid here. Use when the user asks to "build CARLA UE5", "build ue58-dev", "compile CARLA with cmake", "rebuild the Python API", or hits a CMake configure or target failure.

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When you want multiple expert perspectives on a founder/operator question — a simulated board of advisors (Jason Fried, Elon Musk, Jeff Bezos, Jensen Huang, Bob Iger, Paul Graham, Naval Ravikant, Sara Blakely). Bring a real decision — "should I raise prices?", "hire my first employee?", "raise or bootstrap?", "kill this project?" — and the council weighs in through documented frameworks, surfaces disagreements, and synthesizes a recommendation. Also use for 'maker council,' 'board of advisors,' 'what would Bezos do,' 'what would Jason Fried say,' 'channel Naval,' 'ask the council,' or how a famous founder would approach a problem. Optional live-research pass via deep-research / watch-video / last30days. Marketing questions go to marketing-skills' marketing-council. Archives to ~/.config/makerskills/maker-council/archive/. To commit to a direction, hand off to decide.

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Generate HeyGen presenter videos via the v3 Video Agent pipeline — handles Frame Check (aspect ratio correction), prompt engineering, avatar resolution, and voice selection. Required for any HeyGen video generation. Replaces deprecated endpoints with v3. Use when: (1) generating any HeyGen video (via API or otherwise), (2) sending a personalized video message (outreach, update, announcement, pitch, knowledge), (3) creating a HeyGen presenter-led explainer, tutorial, or product demo with a human face, (4) "make a video of me saying...", "send a video to my leads", "record an update for my team", "create a video pitch", "make a loom-style message", "I want to appear in this video", "generate a HeyGen video", "make a talking head video". Accepts avatar_id from heygen-avatar for identity-first HeyGen videos, or uses a stock presenter. Returns video share URL + HeyGen session URL for iteration. Chain signal: when the user wants to create/design an avatar AND make a video in the same request, run heygen-avatar first, then return here. Conjunctions to watch: "and then", "and immediately", "first...then", "X and make a video", "design [presenter] and record" = always CHAIN. If the user provides a photo AND wants a video, route to heygen-avatar first. NOT for: avatar creation or identity setup (use heygen-avatar first), cinematic footage or b-roll without a presenter, translating videos, TTS-only, or streaming avatars.

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Indie maker playbook for bootstrapped apps inspired by Pieter Levels' MAKE: The Indie Maker Blueprint. Use when a user is starting or sizing an app idea, choosing what to build next, building a minimal product, preparing a launch, diagnosing stalled growth, deciding pricing or monetization, automating operations, maintaining a per-app MAKE.md tracker, or evaluating whether to sell a product.

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Legendary blog writing that makes readers forget they're reading. This skill combines the narrative mastery of Paul Graham's essays, the technical accessibility of Julia Evans, the conversational wit of Wait But Why, and the viral mechanics of James Clear. Great blog posts don't inform—they transform. They take complex ideas and make them feel obvious. They turn strangers into superfans. They get shared not because someone should read them, but because someone needs to read them. This skill adapts to any topic: cybersecurity that reads like a thriller, AI explainers that feel like conversations, marketing insights that make you screenshot every paragraph, and coding tutorials that make you excited to build. Use when "write a blog post, blog post about, write an article, create content about, explain this topic, write about, long-form content, thought leadership, technical blog, how-to article, tutorial post, educational content, industry analysis, opinion piece, newsletter content, make this engaging, write for developers, explain like, writing, blog, content, articles, long-form, storytelling, technical-writing, thought-leadership, educational, tutorials, explainers, engagement" mentioned.

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Win #1 Product of the Day on Product Hunt. Completely reverse-engineered from 30x daily winner and 3x weekly winner campaigns — ranking algorithm decoded, launch timing optimized, hunter selection guide, community engagement approach, and post-launch momentum playbook. Use this if you're: (1) already read PH launch guides but still can't break into Top 5 and need algorithm-level deep optimization, (2) targeting Daily #1 and 5,000+ sign-ups with a proven 30+ case validated approach, or (3) an investor/accelerator coaching portfolio companies on PH launches. What's inside: PH ranking algorithm decoded: weighted factors for first 3 hours, 24 hours, and weekly ranking · Pre-launch preparation: gallery optimization, product URL selection, tagline refinement (≤60 chars) · Launch timing optimization: exact PST launch windows + scheduling guide · Community engagement KPIs: upvote targets, comment response time, maker comment strategy · Platform compliance guide: understanding PH's quality signals and organic growth · Post-launch 72-hour plan: maintaining momentum, conditions for Weekly #1 · Case studies: 3 different categories (AI Tool, App, SaaS) dissected Expected outcomes: Product Hunt Daily #1 ranking · strong community engagement on launch day · 3,000–10,000+ sign-ups (depending on product-market fit) 🇨🇳 Product Hunt 打榜完整攻略 | 🇯🇵 Product Hunt ローンチガイド | 🇰🇷 Product Hunt 런칭 가이드 Keywords: Product Hunt #1, Product Hunt launch strategy, PH ranking algorithm, PH #1 daily winner, Product Hunt tips, product launch, startup launch, ranking optimization, PH gallery optimization, Community engagement strategy, maker community engagement, product strategy, launch tactics, how to win Product Hunt, product hunt best practices

Makers 开发专家团 CLI 命令参考 Skill — EdgeOne CLI 全部命令速查、 非交互 flag、环境变量管理、本地预览、build。 触发词：CLI 命令、edgeone 命令、命令行参考、makers dev、 makers build、makers deploy、env set、env ls。

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Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

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Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deploy_ic.py for real-time endpoints that scale to zero instances via inference components, and deploy_async.py for async endpoints (also scale-to-zero). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.

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Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.

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Builds CARLA on Unreal Engine 5.8 from the ue58-dev branch using its CMake build system — configure a preset, then build the carla-unreal / carla-unreal-editor / carla-python-api-install / launch targets — with ROS 2, DLSS, RSS and cook-scope options, and the engine-side build of the CarlaUnreal UE 5.8 fork. There is no Makefile and no Util/BuildTools in this tree, so every UE4 `make` recipe is invalid here. Use when the user asks to "build CARLA UE5", "build ue58-dev", "compile CARLA with cmake", "rebuild the Python API", or hits a CMake configure or target failure.

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Turn any markdown file into a publication-quality PDF. Proper 1in margins, intelligent page breaks, page numbers, cover pages, running headers, curly quotes and em dashes, clickable TOC, diagonal DRAFT watermark. Not a draft artifact — a finished artifact. Use when asked to "make a PDF", "export to PDF", "turn this markdown into a PDF", or "generate a document". (gstack) Voice triggers (speech-to-text aliases): "make this a pdf", "make it a pdf", "export to pdf", "turn this into a pdf", "turn this markdown into a pdf", "generate a pdf", "make a pdf from", "pdf this markdown".

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。