「warm-up」の検索結果

Runtime-neutral Reddit account warm-up and brand promotion skill for AI agents. Systematically grow newly registered or existing Reddit accounts into credible identities, then organically plant brand content in target subreddits to eventually promote products or tools. Use for: register or take over a Reddit account and start the full warm-up cycle, build a credible Reddit account from scratch, run automated daily tasks on a schedule, camp on a post after publishing to reply, run multiple accounts sequentially. Triggers: reddit warmup / 30-day warmup / start warmup / build reddit karma / reddit brand promotion / reddit automated posting / today's task - reddit warmup <user> / Day N warmup / enter brand phase / refresh subreddit pool / camp <user> / stop camping / pause warmup / resume warmup / delete account / promote on reddit. EXECUTION RULE: before taking any action, load the reference files listed in the Phase Router section of this skill. Do not call browser-act, write any state file, or execute any step until the required reference files have been loaded.

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。

🇺🇸 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"

Builds, inspects, tests, and analyzes bounded process-based discrete-event simulations with SimPy. Use for event scheduling, resource queues, interrupts, monitoring, independent replications, warm-up, and reproducible output analysis.

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 a structured customer interview script with JTBD probing questions, warm-up, core exploration, and wrap-up sections. Follows The Mom Test principles — no leading questions, no pitching, focus on past behavior. Use when preparing for user interviews, creating interview guides, or planning discovery research.

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 a burning-paper ring loader whose GLSL flame is drawn by a Worker on an OffscreenCanvas started from the head, so a main thread blocked by the page build cannot freeze it, with a progress count, an even-paced run into the hand-off, and a clean fade to the page. Covers the ember thread and ember gradient, porous char, worker start-up, parallel shader compile with a waited 1-px warm-up draw, hand-off without a stall, reduced motion and hidden-tab behaviour. Use for a loading screen, preloader, page loader, splash, "kindling", burn loader, ember ring, burning paper hole, fire loader, or a loader that must keep moving while heavy JavaScript, three.js or shader builds hold the main thread.

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。

Write a structured interview guide — warm-up, core exploration, and wrap-up. Use before running interviews. For analysing what comes back, use `summarize-interview`.

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。

Plan a board game night that actually lands — the right games for your group size, mix, and time, in a running order that keeps energy up. Use when asked to plan a game night, what board game should we play, games for [N] people, or what to play with a mixed group. Produces game picks matched to player count and experience, a warm-up-to-main running order, teach-time and play-time estimates, and swaps for the non-gamers or the one player who hates losing.

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 a Unity 6.3 URP task involves shaders: hand-written HLSL, Shader Graph water, toon, dissolve or outline, Custom Function nodes, a renderer feature or full-screen effect on Render Graph, compute shaders, shader variants, keywords, stripping, Shader Build Settings, PSO warm-up, or shader cost; or when a material turns pink or invisible, casts no shadow, ignores extra lights under Forward+, an effect 'does nothing', the game stutters the first time something appears, or builds bloat with variants.

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 a Unity 6.3 game is slow or stutters: low fps, frame spikes, hitches when entering areas or on first effects, GC spikes, too many draw calls or SetPass calls, CPU vs GPU bound questions, Profiler, Profile Analyzer, Memory Profiler, Frame Debugger or Project Auditor work, GPU Resident Drawer or SRP Batcher setup, LOD and Mesh LOD, shader warm-up and variants, memory or VRAM budgets, IL2CPP and build size, or proving a fix with numbers from a player.

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 setting up URP (or choosing URP vs HDRP), configuring URP assets and quality tiers for PC and phones, lighting a stylized or realistic Unity scene (sun, sky, baked or mixed lightmaps, Shadowmask, Adaptive Probe Volumes, reflection probes), or fixing blurry shadows, light leaks, gray bakes, washed-out images, tonemapping and grading, bloom too expensive on mobile, TAA, STP or MSAA choices, GPU Resident Drawer setup, shader warm-up hitches, or a custom shader that vanished or lost its baked light.

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。

Provides AWS Lambda integration patterns for PHP with Symfony using the Bref framework. Creates Lambda handler classes, configures runtime layers, sets up SQS/SNS event triggers, implements warm-up strategies, and optimizes cold starts. Use when deploying PHP/Symfony applications to AWS Lambda, configuring API Gateway integration, implementing serverless PHP applications, or optimizing Lambda performance with Bref. Triggers include "create lambda php", "deploy symfony lambda", "bref lambda aws", "php lambda cold start", "aws lambda php performance", "symfony serverless", "php serverless framework".

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 sport-specific practice sessions. Drills by skill level and time, warm-up, skill work, scrimmage, cool-down.

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。

Think and work like an expert Industrial Engineer. Use when a task calls for Industrial Engineer judgment. Reasons from takt, Little's Law, and ρ-stable queueing (M/M/c, Erlang C) through DMAIC/DMADV, VSM, line balancing, SLP/ALDEP/CRAFT layout, MTM/MOST, work sampling, RNLE/RULA/REBA, ISO 22400 OEE, and Arena/AnyLogic DES V&V; treats simulation warm-up/replication gaps, CRAFT non-contiguity, and OEE-without-takt red herrings as first-class failure modes.

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 specialized craft of writing copy that sells—headlines that stop thumbs, body copy that persuades, CTAs that convert. This is copywriting specifically tuned for advertising: paid media, performance marketing, direct response. Ad copy is different from content writing. Every word costs money (CPM/CPC). Every character competes for attention. There's no time for warm-up—you have one chance to hook, persuade, and convert. The best ad copy feels effortless but is engineered word by word. In the age of AI-generated creative at scale, ad copywriting becomes both more important (you need more variations) and more challenging (everyone has access to the same tools). The differentiation is in the strategy, the insight, and the craft that AI assists but doesn't replace. Use when "ad copy, headline, ad text, CTA, call to action, Facebook ad, Google ad, ad variations, direct response, performance copy, primary text, ad headline, ad-copy, headlines, direct-response, performance, conversion, cta, advertising" mentioned.

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 a structured user interview script with warm-up, core exploration, and wrap-up sections. Use when preparing for user research interviews to ensure consistent, insightful conversations.

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。

新号注册或老号解冻后做开业准备时用：装修Profile四要素（头像真人/昵称关键词+身份/简介价值承诺/背景图）→开蓝V(Premium)→养号3天只浏览→再发内容。Trigger："新号第一天""要不要开蓝V""怎么装修主页/头像/昵称/简介""新号怎么养号""day 1 new account""should I get Premium""set up profile""warm up account"。不适用：已运营账号日常发内容/没粉丝(→x-cold-start-playbook)、被限流封号后排查(→x-account-pitfalls)。

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 configuring or troubleshooting email deliverability for Marketing Cloud or Salesforce orgs: sender authentication (SPF, DKIM, DMARC), private sending domain setup, dedicated IP warm-up, list hygiene practices, and sender reputation monitoring. NOT for building a Marketing Cloud email — use admin/email-studio-administration. NOT for unsubscribe and consent — use admin/consent-management-marketing. Also covers the Salesforce Core deliverability surface: EmailAdministrationSettings, EmailAuthorizationSettings, EmailDomainKey (DKIM), EmailRelay, EmailDomainFilter, OrgWideEmailAddress verification, bounce management, and the 5,000-a-day outbound email cap.

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。

An innate exploratory drive that guarantees no graph node remains permanently invisible to an agent population. After a warm-up period (step > 3), each agent fires a coverage scan with probability |unseen_nodes| / |all_nodes|. When triggered, the agent teleports directly to the globally unseen node with the highest belief uncertainty, works it, and returns control to the normal action-selection loop. Probability is self-annealing: it starts near 1.0 when almost everything is unseen and decays toward 0.0 as coverage saturates, requiring no manual schedule. The mechanism is "innate" (hard-coded into the agent step loop) as distinct from the "adaptive" EFE path, which requires local neighbors and learned precision weights. Together they form a two-layer exploration architecture: adaptive for locally-guided foraging, innate for topological completeness.

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。