「control」の検索結果

Plan, generate, edit, reference, and quality-control still images with Kling AI's hosted IMAGE surfaces or Kuaishou's open Kolors checkpoints. Use for Kling IMAGE 3.0/3.0 Omni/O1/2.1 web, official Kling CLI/MCP or Open Platform integration, and local Kolors text-to-image, image-to-image, IP-Adapter, ControlNet, inpainting, or LoRA work. Do not use for Kling video, avatars, lip-sync, unofficial gateway APIs, or for assuming that current hosted Kling Image models are identical to the 2024 open Kolors weights.

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Operate Stability AI image generation, image-to-image, edit, control, background, and upscale APIs safely and reproducibly. Use when selecting or calling Stable Image Ultra, Core, Stable Diffusion 3.5, v2beta image edit/control services, or Stability open-weight image models; when debugging schemas, moderation, rate limits, cost, lifecycle, licenses, privacy, provenance, or migrations; and when planning production QA for Stability-generated images.

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Produce 3D assets with Meshy (meshy.ai) through its REST API and web platform — text-to-3D, image-to-3D, multi-image-to-3D, AI texturing/PBR, remesh/topology control, UV, and auto-rigging/animation. Use this skill when an agent must generate a mesh from a prompt or reference image, control polygon count and topology, apply or restyle textures, rig a humanoid for animation, export to GLB/FBX/OBJ/USDZ/STL for Unity/Unreal/Blender, drive Meshy's asynchronous job API (auth, polling, streaming, webhooks, credits, rate limits), review generated meshes for game/film/print readiness, or decide whether Meshy is the right tool at all. Not for editing an existing hand-modeled asset's geometry, hard-surface CAD with exact dimensions, or guaranteed-watertight manufacturing parts.

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Comprehensive quality control for CLIP-seq libraries (eCLIP, iCLIP, iCLIP2, PAR-CLIP) covering library complexity (preseq), FRiP, IDR replicate reproducibility, read-distribution metagene, SMInput vs IgG control rationale, rRNA / snoRNA contamination, fragment-length distribution, and ENCODE-compliance thresholds. Use when assessing whether a CLIP library passed, deciding lenient vs stringent peak thresholds, comparing replicates with IDR rescue and self-consistency ratios, or distinguishing failed IP from over-amplified library.

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Redpanda's Agentic Data Plane: governance infrastructure for building, running, and governing AI agents and MCP servers, plus a proxying AI Gateway for LLM providers, operated via `rpk ai` and the ADP API. Use when creating or managing AI agents (managed or self-managed) via `rpk ai agent` or `AgentRegistryService`; configuring MCP servers (remote or managed catalog, code mode, auth); setting up LLM providers or querying models via `rpk ai llm`/`rpk ai model` or the AI Gateway proxy; or configuring budgets, guardrails, or Cedar access-control policies through the governance APIs. Also covers reading agent transcripts and spending insights, and wiring OAuth clients or providers to the aigw Authorization Server. For the separate rpk cloud mcp control-plane MCP server, see `/redpanda:rpk-cloud`.

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You are an expert in @react-three/drei, the companion library for React Three Fiber that provides 100+ ready-made components and hooks for common 3D patterns. You help developers add orbit controls, environment maps, text rendering, HTML overlays, loaders, performance utilities, shaders, and abstractions — eliminating boilerplate and letting teams focus on creative work instead of Three.js plumbing.

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Node-based graphical interface for Stable Diffusion workflows. Build complex image generation pipelines by connecting nodes visually. Supports custom nodes, ControlNet, LoRA, upscaling, and advanced workflows with full control over the diffusion process.

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Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when "computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation, Claude computer, OpenAI Operator, browser agent, visual agent, RPA with AI, " mentioned.

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Browser automation powers web testing, scraping, and AI agent interactions. The difference between a flaky script and a reliable system comes down to understanding selectors, waiting strategies, and anti-detection patterns. This skill covers Playwright (recommended) and Puppeteer, with patterns for testing, scraping, and agentic browser control. Key insight: Playwright won the framework war. Unless you need Puppeteer's stealth ecosystem or are Chrome-only, Playwright is the better choice in 2025. Critical distinction: Testing automation (predictable apps you control) vs scraping/agent automation (unpredictable sites that fight back). Different problems, different solutions. Use when "playwright, puppeteer, browser automation, headless, web scraping, e2e test, end-to-end, selenium, chromium, browser test, page.click, locator, playwright, puppeteer, browser, automation, testing, scraping, headless, e2e, selenium, web-testing" mentioned.

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The science of learning through controlled experimentation. A/B testing isn't about picking winners—it's about building a culture of validated learning and reducing the cost of being wrong. This skill covers experiment design, statistical rigor, feature flagging, analysis, and building experimentation into product development. The best experimenters know that every test, positive or negative, teaches something valuable. Use when "a/b test, experiment, hypothesis, statistical significance, sample size, feature flag, variant, control, treatment, p-value, conversion rate, test winner, split test, experimentation, testing, statistics, feature-flags, hypothesis, growth, optimization, learning, validation" mentioned.

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Control CLAWDINATOR fleet lifecycle via the control API. Use for /fleet deploy or /fleet status.

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影视编剧 (Scriptwriting / Screenwriting) — 为电影 / 电视 / 流媒体剧集写戏剧蓝本的从业者认知操作系统: 决定「讲什么故事、人物是谁为何转变、场景如何被戏剧化、主题为何重要」的学科 —— 区别于导演 (调度 / 视觉实现)、制片 (融资 / 排期)、小说 (无格式 / 场景 / 视觉动作约束)。覆盖 (a) 故事与结构理论 (亚里士多德《诗学》情节 / 人物 / 思想 + 突转 peripeteia / 发现 anagnorisis + 净化 catharsis; Syd Field 范式 / 三幕 / 情节点; Freytag 金字塔; Egri 前提; McKee《故事》掌控性理念 / 鸿沟 / 价值正负转变 / 激励事件 / 渐进式复杂化; Truby《故事剖析》22 步 + 道德论证; Vogler/Campbell 英雄之旅; Blake Snyder《救猫咪》15 拍节拍表; Dan Harmon 故事环; John Yorke 五幕《进入森林》; Frank Daniel 八序列法; 起承转合东亚戏剧结构), (b) 人物 (想要 vs 需要 / 外在目标 vs 内在缺失, 人物弧光 正 / 负 / 平, flaw-wound-ghost 创伤背景, 主动 vs 被动主角的能动性, Egri 三维人物 生理 / 社会 / 心理, 反派是主角的镜子, 群像与关系网, 人物在压力下的选择中显形), (c) 对白 (潜台词 subtext, 对白即动作与冲突而非信息, 避免太直白 on the nose, 人物独特声口, 信息植入 exposition, Mamet/Sorkin 意图-阻碍式对白, 展示而非告知, 中文剧台词工艺), (d) 场景与序列工艺 (场景是有目标 / 冲突 / 转折的微型故事, 场景价值充能与转折点, 晚进早出, 八序列法, set piece, 转场, 场景头 / 动作行 / 括号提示 格式工艺), (e) 主题与前提 (掌控性理念 / 前提, 主题是被戏剧化的论证而非说教, 道德论证 Truby, 主题统一性, 这到底在讲什么), (f) 类型 (类型惯例是对观众的承诺, Snyder 10 类故事, 颠覆 vs 尊重套路, 类型片 vs 文艺片, 中国类型 主旋律 / 古装 / 年代 / 悬疑 / 甜宠 / 都市 / 军旅 / 历史正剧), (g) 格式与工业流程 (好莱坞剧本格式与 Final Draft, logline 一句话故事, treatment 故事大纲, 节拍表, 分场 / 分集大纲, spec 原创剧本 vs 委托, pitch, coverage, 美剧编剧室与 showrunner 制; 中国 大纲→人物小传→分集大纲→剧本, 长剧集 40 集结构, 网络电影 / 网大, 分账剧, 平台定制剧, 微短剧竖屏短剧), (h) 改稿与开发 (写作就是改写, 改稿 / 润色 / 推倒重写, 处理意见 / 改稿意见, 片方与平台 notes, 杀死你的宝贝, 围读, 开发地狱), (i) 改编 (IP改编 小说 / 漫画 / 游戏改编, 忠于精神而非字句, 压缩, 中国网文 IP 热), (j) 中国影视编剧产业现实 (审查 / 广电总局约束, 备案 / 立项, 平台定制剧 爱优腾芒, 分账, 编剧维权 署名权 / 欠薪 / 枪手, 注水剧, 流量 + 大IP 资本逻辑 vs 原创工艺, 导演中心制 vs 编剧话语权), (k) 编剧职业与工艺生涯 (spec vs 委托, 编剧 vs 编审 / 策划, 单兵 vs 编剧室, 在开发链条中的位置, WGA 与署名仲裁规范, 入行, 中国编剧地位与权益)。跨 电影 / 美剧流媒体 / 中国电影与长剧集。不含 导演 / 摄影 / 制片 / 融资 / 表演 / 电影理论批评为终点 / 泛小说写作 / 纪录片 / 新闻 —— 本 skill 是「戏剧化编剧」工艺: 故事 / 人物 / 场景 / 对白 / 主题 / 结构 / 格式 与围绕它们的产业流程。 (Scriptwriting / Screenwriting for film & TV (影视编剧) — the cognitive operating system of practitioners who write the dramatic blueprint of films, television, and streaming series: the discipline of deciding WHAT story is told, WHO the characters are and why they change, HOW the scenes are dramatized, and WHY it matters thematically — distinct from directing (staging/visual realization), producing (financing/scheduling), and prose fiction (which has no format/scene/visual-action constraints). Covers (a) story & structure theory (Aristotle's Poetics — plot/character/thought + reversal peripeteia/recognition anagnorisis + catharsis; Syd Field's paradigm/three-act/plot points; Freytag's pyramid; Lajos Egri's premise; Robert McKee's Story — controlling idea/the gap/value charge/inciting incident/progressive complications; John Truby's 22 steps & moral argument; Christopher Vogler/Joseph Campbell hero's journey; Blake Snyder's Save the Cat 15-beat sheet; Dan Harmon's story circle; John Yorke's 5-act Into the Woods; Frank Daniel's 8-sequence method; 起承转合 East-Asian dramatic structure), (b) character (want vs need/external goal vs internal lack, character arc positive/negative/flat, 人物弧光, flaw-wound-ghost backstory, agency active vs passive protagonist, Egri's 3-dimensional character physiology/sociology/psychology, antagonist as the protagonist's mirror, ensemble & relationship webs, character revealed through choice under pressure), (c) dialogue (subtext 潜台词, dialogue as action & conflict not information, 'on the nose' avoidance, distinct character voice, exposition handling, Mamet/Sorkin intention-obstacle dialogue, show-don't-tell, 台词 craft in Chinese drama), (d) scene & sequence craft (scene as a mini-story with goal/conflict/turn, scene value charge & turning point, enter-late-leave-early, the 8-sequence method, set pieces, transitions, slug line/action line/parenthetical format craft), (e) theme & premise (controlling idea/premise/前提, theme as dramatized argument not message, moral argument Truby, thematic unity, 'what is this really about'), (f) genre (genre conventions as audience promise, Snyder's 10 story types, subverting vs honoring tropes, 类型片 vs 文艺片, Chinese genres 主旋律/古装/年代/悬疑/甜宠/都市/军旅/历史正剧), (g) format & industry process (Holly

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。

B2B 大客户销售 (B2B 大客户销售 / 企业级复杂销售 (Enterprise B2B Sales / Complex Selling) — 面向企业客户的高价值、长周期、多干系人复杂销售的职业认知操作系统，从业者(AE/客户经理/SE 售前/销售负责人 VP Sales/CRO)、想入行者、以及服务这行(销售咨询/enablement/RevOps)的视角。覆盖: (a) 第一性张力 — 三大方法论流派之争: **买方主导的诊断式提问 (consultative/discovery-led: SPIN 提问 → 痛点 → 方案, 「像医生先诊断再开方」) ⇄ 卖方主导的教学式挑战 (Challenger: Teach-Tailor-Take control, 用商业洞见 reframe 客户认知, 「像老师挑战客户的既有想法」) ⇄ 资格审查纪律 (MEDDIC/MEDDPICC: 先严格 qualify/disqualify, 「no deal 好过 bad deal」)**; 三派不是非此即彼, 资深人混用, 但底层世界观分歧真实(问 vs 教 vs 筛); 更深层张力 — 「过程/方法论纪律 (process rigor: 预测准确、deal inspection、qualify) ⇄ 关系/信任 (relationship: champion 培养、高管关系、人情)」, 「卖结果/业务价值 (business outcome/ROI、卖给经济决策人) ⇄ 卖产品/功能 (feature selling)」, 「多线程触达买方委员会 (multithreading buying committee) ⇄ 单一 champion 依赖」; (b) 方法论正典 (最标准化、最易蒸出高质量+CLI 化) — MEDDIC/MEDDPICC (Metrics 量化指标/Economic buyer 经济决策人/Decision Criteria 决策标准/Decision Process 决策流程/Identify Pain 痛点/Champion 内部拥护者 + Paper process 签约流程/Competition 竞争), SPIN (Situation/Problem/Implication/Need-payoff 提问序列), Challenger (商业洞见 commercial insight + reframe + 建设性张力 constructive tension + Mobilizer 调动者), Solution Selling (痛点链 pain chain/愿景创建/buying vision), Sandler (前置合约 up-front contract/痛苦漏斗 pain funnel/潜艇七步), Command of the Message (Force Management: 业务价值框架/before-after/decision criteria), Gap Selling (Keenan: 现状 current state → 期望 future state → 差距 the gap/问题中心), Miller Heiman Strategic Selling (蓝表 blue sheet/buying influences: 经济/用户/技术买家 + coach), Value Selling, BANT (老式资格框架, 多被 MEDDIC 取代); (c) 行业结构与角色 — SDR/BDR (开发/约见) → AE/客户经理 (成交) → SE/售前/Solutions Engineer (技术验证/POC/demo) → CSM (客户成功/续约扩张) → Sales Manager/VP Sales/CRO; RevOps (收入运营), Sales Enablement (赋能); land and expand (先落地再扩张)、量化指标 quota/OTE(目标总收入)/配额达成率/pipeline coverage(管道覆盖率 ~3x 业内经验法则)/win rate(赢率)/sales velocity(销售速度)/ramp time(爬坡期)/ACV·ARR·NRR(年合同额/年经常性收入/净收入留存); (d) 销售周期 (the deal/opportunity lifecycle) — prospecting 开发 → discovery 需求挖掘 → qualification 资格审查(MEDDIC) → demo/technical validation/POC 技术验证 → business case/value 业务价值与 ROI → proposal 提案 → negotiation 谈判(procurement 采购/法务/security review) → close 成交 → onboarding/expansion 交付与扩张; 干系人: champion 拥护者 vs coach 线人 vs economic buyer 经济决策人 vs blocker 阻碍者; mutual action plan/close plan(双向行动计划/成交计划); (e) 产出物与节奏 — discovery call(需求电话)、demo、business case、proposal/SOW、MAP(mutual action plan)、forecast(预测: commit/best case/pipeline 类别)、pipeline review/deal review/QBR(季度业务回顾)/account plan(客户计划); (f) 职业/招聘/薪酬 — quota/OTE/comp plan(底薪+提成)/accelerator(加速器)/SPIFF/clawback、ramp、President's Club(顶尖销售俱乐部)、SDR→AE→管理 晋升路径、rep turnover(销售流失率高); (g) 争议/批判 — Challenger 是否过誉(CEB 原始数据被质疑、insight selling 批评)、MEDDIC 沦为填表剧场(box-checking theater, 填字段不真 qualify)、活动指标 vs 质量(spray-and-pray 群发/冷电话存废之争)、AI SDR/自动化淹没收件箱(2024-2025 AI BDR 泡沫 + 邮件送达率崩溃 backlash)、SDR 军团模式是否将死(Winning by Design/signal-based GTM 取代)、配额虚高与销售 burnout、季末打折促单、销售方法论的「宗教化」与培训-认证产业链、最大竞争对手其实是「no decision/维持现状」而非对手; (h) 流派/思想谱系 — 提问诊断派(Rackham SPIN/Bosworth&Eades Solution Selling/Keenan Gap Selling) vs 挑战教学派(Dixon&Adamson Challenger/Force Management Command of the Message) vs 资格纪律派(MEDDIC: Napoli&Dunkel 起源/John McMahon/MEDDICC Andy Whyte) vs 关系战略派(Miller Heiman Strategic/Large Account) vs SaaS 现代收入架构派(Aaron Ross Predictable Revenue/Mark Roberge Sales Acceleration Formula/Jacco van der Kooij Winning by Design bowtie/David Skok 指标) vs 开发-心态派(Jeb Blount Fanatical Prospecting/Sales EQ、Anthony Iannarino、Josh Braun) vs 谈判(Chris Voss Never Split the Difference 战术同理心)。不含: B2C/零售/电商导购销售、纯电话客服/客户支持、纯渠道/分销管理(channel 虽相关但聚焦直销复杂销售)、纯 marketing/需求生成(demand gen 虽上游但本 skill 聚焦销售执行)、纯 CSM 客户成功(虽相关但聚焦 new logo + expansion 的销售动作)。) Master OS — automated mastery of B2B 大客户销售 / 企业级复杂销售 (Enterprise B2B Sales / Complex Selling) — 面向企业客户的高价值、长周期、多干系人复杂销售的职业认知操作系统，从业者(AE/客户经理/SE 售前/销售负责人 VP Sales/CRO)、想入行者、以及服务这行(销售咨询/enablement/RevOps)的视角。覆盖: (a) 第一性张力 — 三大方法论流派之争: **买方主导的诊断式提问 (consultative/discovery-led: SPIN 提问 → 痛点 → 方案, 「像医生先诊断再开方」) ⇄ 卖方主导的教学式挑战 (Challenger: Teach-Tailor-Take control, 用商业洞见 reframe 客户认知, 「像老师挑战客户的既有想法」) ⇄

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数据工程 — 数据平台从业者的认知操作系统, 覆盖把数据从源系统搬运成可靠 / 可查询 / 可信赖形态供分析 / ML / 数据产品消费的全生命周期 (生成 → 摄取 → 存储 → 转换 → 服务 + 安全/数据管理/DataOps/数据架构/编排/软件工程 六条暗流, Reis & Housley 框架): 摄取与集成 (批 + CDC 变更数据捕获 Debezium + EL 工具 Fivetran/Airbyte/Meltano/dlt + Kafka Connect + schema drift) / 存储与文件表格式 (对象存储数据湖 + 列存 Parquet/ORC/Arrow/Avro + 开放表格式 Apache Iceberg/Delta Lake/Apache Hudi + lakehouse + 分区/compaction) / 转换与建模 (ELT dbt/SQLMesh + Spark + 维度建模 Kimball + Inmon + Data Vault + 大宽表 OBT + 渐变维 SCD + 增量模型 + 语义/指标层) / 编排与工作流 (Apache Airflow/Dagster/Prefect/Mage/Kestra/Apache DolphinScheduler + DAG + 幂等 + 回填 backfill + 数据资产调度) / 批流与实时 (Apache Kafka/Apache Flink/Spark Structured Streaming/Kinesis/Pulsar/Redpanda + Lambda vs Kappa + watermark/窗口/exactly-once + 流式 SQL Materialize/RisingWave + 实时 OLAP ClickHouse/Apache Druid/Apache Pinot/StarRocks/Apache Doris) / 数仓与查询引擎 (Snowflake/BigQuery/Redshift/Databricks SQL/Trino/Presto/DuckDB/Polars + 存算分离 + MPP) / 数据质量测试与可观测性 (dbt tests/Great Expectations/Soda + 数据契约 + Monte Carlo data downtime + 新鲜度/量/schema 异常检测) / 数据治理编目与血缘 (DataHub/Amundsen/OpenMetadata/Unity Catalog + 列级血缘 + PII 分类 + 访问控制 + GDPR) / DataOps 与可靠性 (数据 CI/CD + 转换版本控制 + 环境隔离 + 幂等重处理 + 数据 SLA/SLO + 计算存储 FinOps) / 数据架构范式 (现代数据栈 + lakehouse + data mesh + data fabric + 去中心化 vs 中心化所有权) / 分析工程角色 (dbt 时代连接数据工程与分析的桥) — 不含 数据科学/ML 建模本身 (是下游消费者) / BI 仪表盘制作 (serving 下游) / 数据分析报表为终点 / 'data engineer = 跑 Hadoop 的' 过时窄化 / 通用后端应用开发 (平行学科) (Data Engineering — the cognitive operating system of practitioners who design, build, and operate the data platform: moving data from source systems into reliable, queryable, trustworthy form for analytics / ML / products, covering (a) the data engineering lifecycle (generation → ingestion → storage → transformation → serving, with the undercurrents security / data management / DataOps / data architecture / orchestration / software engineering — Reis & Housley framing), (b) ingestion & integration (batch + CDC change-data-capture with Debezium, EL tools Fivetran / Airbyte / Meltano / dlt, Kafka Connect, API + file + database sources, schema drift handling), (c) storage & file/table formats (object storage data lakes, columnar formats Parquet / ORC / Arrow / Avro, open table formats Apache Iceberg / Delta Lake / Apache Hudi, lakehouse architecture, partitioning / compaction / Z-ordering), (d) transformation & modeling (ELT with dbt / SQLMesh, Spark, dimensional modeling Kimball, Inmon CIF, Data Vault, One Big Table / wide tables, normalization vs denormalization, slowly changing dimensions, incremental models, the semantic / metrics layer), (e) orchestration & workflow (Apache Airflow, Dagster, Prefect, Mage, Kestra, Apache DolphinScheduler, DAGs, idempotency, backfills, data-aware / asset-based scheduling), (f) batch vs streaming & real-time (Apache Kafka, Apache Flink, Spark Structured Streaming, Kinesis / Pulsar / Redpanda, the Lambda vs Kappa debate, watermarks / windowing / exactly-once, streaming SQL Materialize / RisingWave, real-time OLAP ClickHouse / Apache Druid / Apache Pinot / StarRocks / Apache Doris), (g) warehouses & query engines (Snowflake, BigQuery, Redshift, Databricks SQL, Trino / Presto, DuckDB, Polars, decoupled storage & compute, MPP), (h) data quality, testing & observability (dbt tests, Great Expectations, Soda, data contracts, Monte Carlo / data downtime, freshness / volume / schema anomaly detection, unit / integration testing of pipelines), (i) data governance, catalog & lineage (DataHub, Amundsen, OpenMetadata, Unity Catalog, column-level lineage, PII / data classification, access control, GDPR / data privacy), (j) DataOps & reliability (CI/CD for data, version control of transformations, environments, idempotent reprocessing, SLAs / SLOs for data, cost / FinOps for compute & storage), (k) data architecture paradigms (modern data stack, data lakehouse, data mesh, data fabric, decentralized vs centralized ownership), (l) the analytics engineering role (the dbt-era bridge between data engineering and analysis); N

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Define version control strategies for design files, components, and libraries.

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Applies the developmental psychopathology and longitudinal research frameworks of Terrie E. Moffitt (psychologist, Duke University). Use this skill whenever reasoning about human development, criminology, juvenile delinquency, self-control, mental health trajectories, or biological aging. Trigger this when asked to evaluate the origins of antisocial behavior, the impact of childhood traits on adult outcomes, the validity of psychiatric diagnoses (comorbidity), or anti-aging interventions. Apply her models to shift focus from cross-sectional snapshots to longitudinal life-course trajectories, emphasizing the 'p' factor, the self-control gradient, and the dual taxonomy of antisocial behavior.

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Calculate PI/PID controller gains using Internal Model Control (IMC) tuning rules for first-order systems.

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Programmatic security management in Neo4j — RBAC/ABAC, user lifecycle (CREATE/ALTER/DROP USER), role lifecycle (CREATE/GRANT ROLE/DROP ROLE), privilege grants and denies (GRANT/DENY/REVOKE on graph, database, DBMS), property-level access control, sub-graph access control, SHOW PRIVILEGES inspection, and auth provider config reference (LDAP, OIDC/SSO). Use when an agent needs to manage users, roles, or privileges programmatically via Cypher on the system database. Does NOT handle Cypher query writing — use neo4j-cypher-skill. Does NOT handle cluster ops or backups — use neo4j-cli-tools-skill. Property-level security and ABAC require Enterprise Edition.

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Use when adding controls, menus, HUD, subtitles or effects - input through named actions with rebinding and controller support per engine, input buffering, and the accessibility floor (remapping, colour-blind safe cues, subtitles, text scale, reduced motion and flashes).

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Phase D of a WinForms-to-Avalonia migration: build the Avalonia UI shell with composition, MVVM state and commands, responsive XAML, accessibility, localization boundaries, and behavior-focused unit/Headless tests. Use only when an explicit migration is ready to port Forms or when the orchestrator routes here. Prefer this over control-by-control ports.

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Use when a task names Krea or Krea 2, when choosing between Krea's hosted API and its open weights, when wiring the Krea 2 Image or Krea 2 Style Reference API nodes, when building with FLUX.1 Krea Dev, when someone asks about Krea Realtime or realtime or streaming video generation, or when a Krea 2 job needs ControlNet, instruction editing, identity preservation or per-layer conditioning control that core ComfyUI does not provide.

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Run the great_cto controlled Codex lifecycle with controller-owned writes, verifier evidence, human gates and optional artifact release.

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Read this skill before the first step of any request to do something in an app on the person's own computer (Notes, Finder, System Settings, any desktop app), to look at their screen, or for "computer use". Computer use (desktop control) lets Claude take screenshots of the person's desktop and control it with clicks, typing and scrolling through the Claude desktop app; its tools are named mcp__computer-use__* when the session runs in the desktop app and mcp__remote-devices__computer_* when a cloud session is linked to the person's computer; before a conversation is linked there may be no such tools, only an enable__mcp__remote-devices__Claude_Browser tool, which links it. It covers getting linked, picking the right tool, the access flow, and what to do when computer use is off or out of reach. It is not for websites, which go through Claude in Chrome or the built-in browser and their own skills.

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Use when the user wants to turn a static scene, character images, aerial map, drawn path, or route-control image into an immersive first-person FPV AI video prompt, especially Seedance/Kling/Runway/Veo style one-shot videos with numbered stop markers, red-line path control, world-map flythroughs, camera route planning, variable character counts, non-human POVs such as drones, pets, robot vacuums, character references, timed interactions, dialogue, spatial audio, and negative constraints.

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