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Audit mobile UX implementation against platform conventions -- navigation patterns, gesture handling, pull-to-refresh, infinite scroll, skeleton screens, haptic feedback, adaptive layouts, deep linking, and accessibility..
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Feature discovery agent — mines project docs, memory, user feedback, and competitor gaps to surface the highest-leverage features to build next. Outputs a prioritized feature backlog.
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**GUIDE SKILL** — Guide to the Skill Manager for Copilot extension. USE FOR: adding/removing skill repos, syncing skills, pushing feedback, configuring sync intervals, troubleshooting sync issues, understanding skill format. DO NOT USE FOR: creating new skills (use skill-creator), creating agents (use agent-creator).
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**WORKFLOW SKILL** — Create complete, well-structured Copilot skills from scratch. USE FOR: scaffolding new SKILL.md files, writing effective agent instructions, configuring frontmatter fields, setting up directory structure (references, FEEDBACK.md), optimizing description for discoverability, applying progressive disclosure. DO NOT USE FOR: managing/syncing existing skills (use skill-manager-guide), general coding, VS Code extension development.
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Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
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Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.
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Use this skill when designing, generating an MVP blueprint for, auditing, troubleshooting, refactoring, or explaining an agentic harness for any domain. Covers provider-neutral agent architecture for OpenAI, Anthropic, and OpenAI-compatible APIs: agent loops, tool design, record provenance, interactive presentation, user-memory lifecycles, environment-adaptive tools, speculative tool execution, late-bound capabilities, permissions, system prompts, planning, goals, always-on agents and durable runtime, adaptive agent teams, context compaction, memory, skills, MCP/external connectors, public-board communications, realtime voice agents, hardware agents and board deployment, self-refining recursive harnesses, programmable context, continual refinement, observability, evals, prompt caching, agent-legible environments, feedback loops, and safety.
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阿拉丁·AI短剧数字人肖像权合规工坊把「AI 演员/数字人/换脸/声音克隆做完却因肖像权没授权被投诉下架、数字人授权到期还在用、授权只买了一个平台却全网发、AI 形象神似明星惹官司、被举报时拿不出授权链举证」升级成一条纯本地零依赖的上架前置授权链自查工具链：多源导入(drama-cast 角色卡 _cast.json 双契约兼容/人物资产类型声明 assets.json/授权凭证登记 CSV 或 JSON/drama-publish 发行计划 _publish_plan.json 抽平台)→人物资产五分类(AI原创 ai_original/真人出演 real_actor/第三方数字人 digital_human/换脸 face_swap/声音克隆 voice_clone)逐类判定所需授权→10 条确定性授权链规则(A1缺授权凭证 A2要素缺失 A3已过期 A4即将到期 A5平台越界 A6用途越界 A7撞脸公众人物 A8声音克隆缺授权 A9转授瑕疵 A10平台报备缺失)→门禁评分(P0=25/P1=8/P2=3 扣分，100分制 A-D 档，READY可提审/REVIEW建议整改/BLOCK禁止上架)→自包含 HTML 授权链看板(门禁灯+逐条命中+整改建议)+Markdown 整改清单(可直接交制片执行)+feedback 回灌契约(缺证角色回灌 drama-cast 补授权、越界平台回灌 drama-publish 调发行、续签观察名单、报备提醒)。与 aladin-drama 系列闭环衔接(cast/publish 进，整改反馈回灌 cast 补授权、publish 调发行矩阵)。区别于内容合规工具只扫违禁词与画面红线：本款专管「人」的授权链(肖像权/声音权/数字人许可/换脸源授权)，确定性规则可复算、命中带人物名与整改方向、纯本地零依赖零API、授权信息不出本机(法务资产敏感)。触发词：短剧肖像权、数字人授权、换脸授权、声音克隆授权、AI演员合规、肖像授权到期、授权链自查、撞脸明星、数字人合规、AI形象报备、肖像权侵权、portrait rights、digital human license、face swap consent、voice clone authorization、AI短剧、阿拉丁短剧、短剧创作闭环。
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Use when the user asks to measure command execution time or optimize a feedback loop. Records explicit measurements and recommends faster alternatives such as unit tests versus integration tests. It is opt-in; it does not run on every command.
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Use when Codex needs to understand or respond to automatic comment-checker feedback emitted after an edit-like PostToolUse hook.
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Good-faith divergent brainstorming for achieving a goal. Validates assumptions, then spawns brainstormers running different techniques (first-principles, working-backwards, lateral, analogical, constraints-shift, etc.) in isolation, and synthesizes the pool into a report of standouts, reasonable ideas, and hybrid combinations. Produces feedback only — no code, no tickets, no artifacts.
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Analysis of Competing Hypotheses. Operationalizes Richards Heuer's CIA-tradition technique for systematically narrowing among multiple hypotheses against evidence. Builds an explicit hypothesis-vs-evidence matrix and focuses on disconfirmation rather than confirmation — the central insight: hypotheses cannot be proven, only disconfirmed; the surviving hypothesis is the one with the least disconfirming evidence. Spawns parallel hypothesizers (in isolation, across hypothesis-generation angles) and parallel evidence-gatherers (across evidence classes), then synthesizes into a matrix, diagnosticity analysis, sensitivity analysis, and falsification milestones. Produces feedback only — no code, no tickets, no artifacts.
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Abductive reasoning about causes. Takes a phenomenon, separates observations from interpretations, then spawns diagnosticians applying different reasoning lenses (technical, human-factors, process, incentive-structure, environmental, temporal, measurement-artifact, statistical) in isolation. Orchestrator evaluates candidate causes for fit-against-evidence, calibrates confidence honestly, and reports leading candidate(s) with distinguishing evidence needed. Produces feedback only — no code, no tickets, no artifacts.
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Prospective failure imagination for a plan, decision, or running system. Operates in two modes — **plan mode** (a plan has not yet been committed to; imagine its catastrophic failure broadly) and **scenario mode** (a specific catastrophic scenario is posed against an existing system; investigate causes against the actual code and architecture). Spawns parallel pre-mortemers in isolation across three categories of lens: standard failure classes (technical, operational, estimation, scope, adoption, dependency, team, incentive, detection, reversibility, adversarial), 0-3 ad-hoc target-specific lenses the orchestrator names when warranted, and a first-principles lens that always runs to catch what the prescribed taxonomy misses. Synthesizes into a prioritized risk register with early-warning signals. Produces feedback only — no code, no tickets, no artifacts.
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Retrospective learning from a completed experience. Takes a project, incident, decision that played out, or time period; gathers ground truth (observations) separately from recollections; spawns reflectors applying different lenses (what-worked-vs-got-lucky, what-didn't, what-surprised, system-rewards-vs-intent, decisions-that-aged, what-to-tell-past-self, patterns-that-recur) in isolation; synthesizes into updated mental models as first-class output. Produces feedback only — no code, no tickets, no artifacts.
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Problem redefinition before problem solving. Takes a stated problem, extracts its premises, then spawns reframers applying different lenses (problem-vs-symptom, scope-shift, stakeholder-shift, level-of-abstraction, time-horizon, inversion, category-shift, constraints-shift) in isolation, and synthesizes the alternatives into a report with an orchestrator recommendation. Produces feedback only — no code, no tickets, no artifacts.
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Good-faith adversarial scrutiny of an idea or plan. Spawns critical skeptics across relevant lenses, pairs them with an advocate defending the idea, and synthesizes the exchange into a report. Produces feedback only — no code, no tickets, no artifacts.
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Build a fully automated AI-powered data collection agent for any public source — job boards, prices, news, GitHub, sports, anything. Scrapes on a schedule, enriches data with a free LLM (Gemini Flash), stores results in Notion/Sheets/Supabase, and learns from user feedback. Runs 100% free on GitHub Actions. Use when the user wants to monitor, collect, or track any public data automatically.
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When the user wants to optimize, improve, or increase conversions on any marketing page or form — including homepage, landing pages, pricing pages, feature pages, lead capture forms, or contact forms. Also use when the user says 'CRO,' 'conversion rate optimization,' 'this page isn't converting,' 'improve conversions,' 'why isn't this page working,' 'my landing page sucks,' 'form abandonment,' 'nobody's converting,' 'low conversion rate,' or 'this page needs work.' Use this even if the user just shares a URL and asks for feedback. For signup/registration flows, see signup. For post-signup activation, see onboarding. For popups/modals, see popups.
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Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.
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Patterns and practices that dramatically accelerate development velocity. Covers parallel execution, automation, feedback loops, workflow optimization, and anti-pattern avoidance. Use when starting projects, planning sprints, optimizing workflows, or onboarding developers.
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Ultra-compressed code review comments. Cuts noise from PR feedback while preserving the actionable signal. Each comment is one line: location, problem, fix. Use when user says "review this PR", "code review", "review the diff", "/review", or invokes /caveman-review. Auto-triggers when reviewing pull requests.
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Used by the Reviewer agent during Phase 9 (DesignVerify) and Phase 11 (Quality) to evaluate frontend code. Assesses visual hierarchy, spacing consistency, accessibility, and detects formulaic 'AI slop' design patterns, providing actionable feedback to the Developer.
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Validate a business idea using the minimalist entrepreneur framework before building anything. Use to test if an idea is worth pursuing through manual validation and customer feedback.
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