remotion-render
Remotion 视频/静帧渲染和常见渲染策略、选项指南。
相关性达标的全量 Skills(含未精选长尾)· 共 6259 个
Remotion 视频/静帧渲染和常见渲染策略、选项指南。
关于在 Remotion 使用 React 写标记和动画的指导规范。
在 Remotion 中制作和动画化地图素材的最佳实践集合。
针对 Remotion 中可交互组件的最佳实践与编辑性建议。
通过 Algolia API 检索并获取 Remotion 官方文档页面内容。
引导创建新的 Remotion 项目并搭建初始合成与依赖环境。
规定 Remotion 中字幕的 JSON 格式与必需字段,确保兼容与同步。
为 Remotion 视频项目提供结构、编码与工程化的最佳实践指导。
在浏览器环境中处理音视频文件,获取时长、尺寸等常用信息。
操作本地 Hearsay 跟踪器以测量在各大 LLM 中的品牌与 AI 语音份额。
Give this agent a real, routable IPv6 (/128) identity on the Whisper network, with safe egress and externally-verifiable identity (DNSSEC + RDAP). Keyless verification needs no account.
Prices research, tests, surveys, dashboards and experiments before running them, by computing how much the result would raise expected utility - which is zero whenever no outcome would change the decision. Use when someone proposes an A/B test, user survey, market study, competitor analysis, analytics build, consultant, pilot, or "let's gather more data", and when deciding how large a study needs to be.
Separates signal from noise in metrics you cannot observe directly, updating a belief from evidence instead of reacting to the latest reading - Bayesian updates over competing explanations, and a filter that says whether this week's move is real. Use when a metric moves and someone wants to act, when diagnosing why traffic or revenue changed, when a dashboard number contradicts intuition, or when deciding whether a trend is real yet.
Validates a plan before committing to it - checks whether the ranking survives the assumptions, finds the most likely way it fails, and makes the trade-offs explicit via a Pareto frontier instead of an invented single score. Use before a launch, price change, migration, infrastructure change or major commitment, when running a premortem, when two objectives conflict, or when a decision depends on an assumption nobody has tested.
Models what a competitor will do next and what happens if you respond - best response, equilibrium, and the difference between a rival who optimises perfectly and one who does not. Use before a price change or price war, when a competitor launches something, when deciding whether to match a rival's move, when analysing a market with few players, or when someone asks "what will they do if we do this".
Decides how far ahead to plan, what to discount future payoffs by, and which backlog items can be dropped without analysis - using receding-horizon planning, an explicit discount factor, and branch-and-bound pruning against the incumbent. Use when building a roadmap, prioritising a backlog, arguing about short-term versus long-term, setting quarterly or annual plans, or when planning has become an end in itself.
Turns past decisions into calibrated judgment - scoring old predictions against what happened, assigning credit for delayed results, and separating a bad decision from a good decision that lost. Use during a retrospective, postmortem, quarterly or monthly review, when reviewing decisions taken 30+ days ago, when attributing a result to a cause, or when someone claims to have called something.
Turns a vague "what should we do about X" into a scored decision - explicit actions, an explicit unknown, an explicit prior, and one utility scale - then picks by maximum expected utility and checks the result for framing effects. Use when facing a choice between options under uncertainty, when a decision has stalled in circular debate, when someone asks "should we do A or B", or before writing any decision record, strategy memo, or roadmap commitment.
Splits scarce time, budget or traffic across competing products, channels, campaigns or variants by treating them as a multi-armed bandit - Thompson sampling over beta posteriors, with optimism for anything not yet tried enough to judge. Use when deciding what to work on next, how to divide a marketing budget, which product to prioritise, whether to kill something that is underperforming, or when picking a winner among test variants.
根据结构化报告 JSON 生成品牌化演示网站并验证内容
从脚本生成 1920×1080 的品牌化键盘可控演示 HTML
把 Markdown 转成单文件 HTML 的响应式 Web 应用
为 AI 辅助软件开发按阶段路由合适技能与工具
用 Trafilatura 提取网页文章文本、元数据与 Markdown
将 LinkedIn 走马灯重排为 1080×1920 的 TikTok 轮播图集
跨 surface 的会话启动与自动恢复协议
从录音/稿件自动生成结构化SOP,含决策树与异常处理
为 Slack 优化生成动画 GIF,并校验尺寸和格式约束。
将技能在不同 AI 平台间移植,判断并执行必要的转换步骤。
指导从零搭建、设置与优化扩展 agent 功能的技能。
按 agentskills.io 标准创建、审计并打包可部署的 agent 技能。
扫描内部构建和运行记录,挖掘可转化为内容或收入的证据。
基于品牌与受众审查素材并输出可播报的提词器脚本。
自动生成 Proof 文档、创建短链并记录到 Airtable。
将项目拆解并跨多种 AI 工具分发可执行任务计划。
审计并更新 Claude 项目文档与自定义指令,使其匹配平台与事实变化。
分析项目文件夹并生成可直接执行、自动路由的规范包与交付清单。
采用“计划-执行”路由,把高成本规划和低成本执行分离以节省费用。
为任务决定最合适的执行模式并改写提示以匹配该模式。
将品牌战略转为专业标志与视觉识别的生成式提示与概念方案。