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Build, enhance, audit, or migrate a React / Next.js web app with Stream (Chat, Video, Feeds, Moderation) - the default for all web React work. Scaffold Next.js + Tailwind + Shadcn + Stream React SDKs end-to-end with Steps 0-7. Add Chat/Video/Feeds/Moderation to an existing React project (enhance). Audit an existing Stream Video integration against best practices. Migrate/upgrade an SDK version (e.g. v13 -> v14). Migrate an existing Sendbird (@sendbird/chat, @sendbird/uikit-react) app to Stream Chat React. Triggers on 'build me a ... app', 'scaffold', 'create a new ...', 'add Chat to this app', 'integrate Video', 'drop Feeds into ...', 'upgrade'/'migrate'/'bump ... version', 'migrate from Sendbird', 'replace Sendbird with Stream', and React / Next.js tokens: stream-chat-react, @stream-io/video-react-sdk, @stream-io/feeds-react-sdk, useCreateChatClient, useCreateFeedsClient, StreamVideo, Channel, MessageList. Covers livestreaming, video conferencing, team messaging, direct messaging, social feed. Web React only - not React Native (use stream-react-native).
Use when designing Rails models - ActiveRecord patterns, validations, callbacks, scopes, associations, concerns, query objects, form objects
Interrogates the user to discover who they actually are — what drives them, what drains them, and the natural strengths they can't see because they come so easily — using proven self-discovery prompts (even-as-a-kid, lost-in-the-work, pit-of-my-stomach dread), the anti-questions, and outside-in questions given as homework to people who know them (perfect scenario, personal hell, invisible strengths). Presses every self-flattering label into concrete episodes, welcomes socially unacceptable motives (money, fame, proving a point), and records each finding in WHO-ME.md as a non-judgmental fact — a strength in some contexts, a hindrance in others — with the contexts where it helps and hurts. Load when the user asks who they really are, what work fits them, what drives or drains them, why they keep burning out, or what to build given who they are. Do NOT load to inventory a company's or product's strengths or to distill the one or two decisive personal advantages — the voters step, which consumes this file.
Identify which field values correlate with bad behavior (slowness, errors, anomalies, unusual values) using phi-coefficient correlation analysis over OPAL. Works on any time-series data — metrics, structured logs, span/trace data, or any dataset where rows can be split into a 'bad' and 'good' cohort by a threshold. Use when: (1) User asks for root-cause analysis on a dataset or metric (2) User wants to know what attributes / dimensions / values are most associated with a failure mode, anomaly, or unusual cohort (3) Investigating which services, hosts, regions, namespaces, or attributes drive outliers (4) User mentions phi coefficient, correlation, or outlier detection (5) User asks 'why is X slow/failing', 'what caused the errors on X', or 'what's different about the bad cohort'.
Expert guidance for Satori, the library that converts JSX/HTML and CSS into SVG (the engine behind dynamic Open Graph images and social cards). Use whenever writing or debugging Satori markup e.g. authoring JSX for OG images, choosing CSS that Satori actually supports, fixing layout that renders wrong, embedding fonts, rendering emoji or images, or resolving Satori errors like "Expected length unit" or unsupported property issues. Reach for this any time someone renders HTML/CSS to SVG or PNG with Satori, even if they do not name it.
Generate high-quality marketing images, ad creatives, launch visuals, social cards, blog headers, product mockups, thumbnails, and campaign assets. Use when the user asks to create, design, iterate on, or prompt an image for marketing, growth, social media, paid ads, landing pages, newsletters, or product launches.
Rules and guidelines for working with Spring Data JDBC in the project. ALWAYS use this skill when adding, removing, or modifying Spring Data JDBC entities (@Table from org.springframework.data.relational.core.mapping), aggregates, AggregateReference links, embedded objects, @MappedCollection associations, or Spring Data JDBC repositories (CrudRepository / ListCrudRepository). Trigger on any request that touches @Table, @Column, @MappedCollection, @Embedded annotations from spring-data-relational, AggregateReference fields, @PersistenceCreator constructors, or @Query methods on JDBC repositories. Do NOT use for JPA (jakarta.persistence) entities — use the spring-data-jpa skill instead.
Schreibregeln für natürliche, sachliche deutsche Texte, die typische KI-Verräter vermeiden. Immer anwenden, wenn auf Deutsch geschrieben wird – ob Chat-Antwort, Bericht, Artikel, E-Mail, Zusammenfassung, Dokument oder Social-Media-Post. Auch dann nutzen, wenn nicht ausdrücklich nach Stil oder Schreibregeln gefragt wird, und immer dann, wenn jemand einen Text vermenschlichen, entkünsteln oder weniger nach KI klingen lassen will. Ziel ist, dass kein deutscher Text die Muster aufweist, an denen man KI-generierte Inhalte erkennt (aufgeblähte Bedeutung, Werbesprache, Floskeln, Gedankenstrich-Häufung, Überstrukturierung, Fazit- und Herausforderungen-Abschnitte, erfundene Belege, technische Artefakte, Dialogreste). Grundlage sind die Wikipedia-Seiten Anzeichen für KI-generierte Inhalte (deutsch) und Signs of AI writing (englisch).
Collect contact details from website contact pages — email, phone, address, contact form URL, social profiles. Use when the user wants to extract contact information from a company's website.
Use when platform-reported conversions disagree with GA4/ecommerce, when you suspect Meta and Google are double-counting the same sales, or for a standing (monthly) reconciliation workbook that de-dups stacked credit against an order-ID truth set, normalizes attribution windows and currency, compares attribution models, and reads incrementality from a geo/holdout test. Not for the point-in-time R2 veto or RQS gate — use ad-account-auditor; not for the ROI/ROAS ratio math itself — use roi-calculator; not for organic dark-social share attribution or GA4 direct-traffic decomposition — use dark-social-attributor. 付费广告归因对账/去重/增量
Use when the user asks to "audit our brand narrative" or "is this message on-canon"; runs separate typed TALE truth, system, or effectiveness profiles and never averages them into one composite. Checks differentiation, canon, landing consistency, and evidence integrity. Not for launch readiness — use launch-readiness-auditor; not for social operations — use social-quality-auditor. 品牌叙事分层审计/发布前一致性放行
Use when the user asks to "map what our surfaces say today", "inventory our current messaging", or "find the gap between what we say and what we mean"; produces the narrative baseline — a surface-by-surface inventory of what every owned touchpoint (homepage, pricing, docs, decks, social bios, email footers) claims RIGHT NOW, each line labeled Measured / User-provided / Estimated, plus a per-surface gap read vs the intended message and the drift-baseline snapshot the Evaluate phase measures future drift against. Not for authoring the canon — use message-system-architect; not for scoring the surfaces or running the vetoes — use narrative-quality-auditor. 现状叙事盘点/各触点口径/意图差距/漂移基线