verifier-readiness-azd
在执行前评估项目或任务的依赖、访问与可证实性准备度。
相关性达标的全量 Skills(含未精选长尾)· 共 6067 个
在执行前评估项目或任务的依赖、访问与可证实性准备度。
监控发布金丝雀、对比基线并在授权下处理回滚或事故响应。
独立复核变更的合规性、正确性和证据质量并决定接受或返工。
为变更或交付的每项断言收集新鲜证据并出具可验证证明。
把验收结果拆解成可执行、可验证的阶段、任务与契约。
将自然语言目标转换为可验证的交付并驱动端到端工作流。
集成并验证被接受的变更,准备交付、交接与回滚方案。
在同一仓库用分支或 worktree 隔离并行工作以避免交叉污染。
检查仓库与源码,用证据支撑下一步决策并找到最小路径。
在仓库中一次性设置 AZDone 约定与项目基座供其它技能使用。
复现并隔离失败,产出不含修复的诊断结论与证据。
基于严格 TDD 与公共契约构建最小可提交的代码或内容变更。
从运行证据与评审中捕获可验证、可治理的学习记录。
在实现前为各种界面设计可证据化的用户体验与原型。
将宽泛请求转化为可执行的适应性结果合同并细化目标。
用受控的长期试验与回滚策略改进 skill 工作流的协议。
指导如何升级 Remotion 及相关包、Agent Skills 和兼容组件。
构建基于 Remotion 的视频应用、架构与 Player 使用建议。
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