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Arize is an AI observability platform, and Phoenix is its open-source tracing and evaluation tool for LLM applications. Use this skill when the user wants to trace OpenAI or LangChain calls, run a local Phoenix server, score RAG answers for faithfulness, debug slow or wrong LLM responses, or send OpenTelemetry traces to Arize for production monitoring.

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Activepieces is an open-source workflow automation platform with a visual builder, 400+ integrations ("pieces"), code steps, branching, loops, and webhooks. Use when a user asks to self-host a Zapier or n8n alternative, automate workflows with a no-code builder, build a custom integration piece, or set up open-source business automation.

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。

Build and trace AI agents in Python with AgentScope, an open-source agent framework with a ReAct loop, tools, middleware, team pipelines and OpenTelemetry tracing. Use when building production agents that need observability, debugging an agent's model and tool calls, capping token spend per reply, or coordinating a leader with specialist agents.

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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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Expert at designing clean, consistent, and developer-friendly APIs. Covers RESTful conventions, versioning strategies, error handling, pagination, rate limiting, and OpenAPI documentation. Designs APIs that are intuitive to use and easy to evolve. Use when "API design, REST API, RESTful, API versioning, API documentation, OpenAPI, Swagger, API error handling, pagination, rate limiting, api, rest, http, versioning, pagination, openapi, swagger" mentioned.

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语音转文字 (ASR) (语音转文字 (ASR / 语音识别 / Speech-to-Text) 应用工程 (从业者视角) — 选型、集成、优化语音转文字能力，尤其面向移动端、低成本、快速识别的场景。覆盖: (a) 引擎/API 地图 — 云端 API(OpenAI gpt-4o-transcribe / Whisper API、Deepgram、AssemblyAI、Google / Azure / AWS Transcribe、讯飞、字节火山、阿里、腾讯、百度) vs 开源模型(Whisper / faster-whisper / whisper.cpp / distil-whisper、NVIDIA NeMo Parakeet / Canary、阿里 FunASR / Paraformer / SenseVoice、Moonshine、Vosk、Kaldi / k2 / icefall) vs 端侧·移动 SDK(whisper.cpp + CoreML / Metal、iOS Speech framework、Android SpeechRecognizer、Picovoice、SenseVoice 端侧); (b) 准确率(WER / CER) × 延迟(RTF / 流式) × 成本 三角权衡与选型决策树; (c) 成本优化 playbook — 端侧免费 / 批量折扣 / VAD 裁静音 / 量化(int8 / ggml) / 蒸馏 / 自托管 break-even; (d) 移动端集成 — 端侧 vs 云、流式 vs 批量、断点检测(endpointing)、隐私 / 离线; (e) 后处理 — 标点 / 数字规整(ITN) / 说话人分离(diarization) / 时间戳。学派分歧: 云 API vs 端侧自托管、通用大模型(Whisper) vs 专用流式(RNN-T / Conformer)、闭源 API vs 开源、准确率派 vs 成本派、英文优先 vs 中文 ASR(FunASR / SenseVoice / 讯飞)。不含: 文字转语音(TTS / 语音合成，方向相反)、声纹识别 / 说话人验证为主业、语音 agent / 对话式 AI、ASR 模型训练科研深水区。) Master OS — automated mastery of 语音转文字 (ASR / 语音识别 / Speech-to-Text) 应用工程 (从业者视角) — 选型、集成、优化语音转文字能力，尤其面向移动端、低成本、快速识别的场景。覆盖: (a) 引擎/API 地图 — 云端 API(OpenAI gpt-4o-transcribe / Whisper API、Deepgram、AssemblyAI、Google / Azure / AWS Transcribe、讯飞、字节火山、阿里、腾讯、百度) vs 开源模型(Whisper / faster-whisper / whisper.cpp / distil-whisper、NVIDIA NeMo Parakeet / Canary、阿里 FunASR / Paraformer / SenseVoice、Moonshine、Vosk、Kaldi / k2 / icefall) vs 端侧·移动 SDK(whisper.cpp + CoreML / Metal、iOS Speech framework、Android SpeechRecognizer、Picovoice、SenseVoice 端侧); (b) 准确率(WER / CER) × 延迟(RTF / 流式) × 成本 三角权衡与选型决策树; (c) 成本优化 playbook — 端侧免费 / 批量折扣 / VAD 裁静音 / 量化(int8 / ggml) / 蒸馏 / 自托管 break-even; (d) 移动端集成 — 端侧 vs 云、流式 vs 批量、断点检测(endpointing)、隐私 / 离线; (e) 后处理 — 标点 / 数字规整(ITN) / 说话人分离(diarization) / 时间戳。学派分歧: 云 API vs 端侧自托管、通用大模型(Whisper) vs 专用流式(RNN-T / Conformer)、闭源 API vs 开源、准确率派 vs 成本派、英文优先 vs 中文 ASR(FunASR / SenseVoice / 讯飞)。不含: 文字转语音(TTS / 语音合成，方向相反)、声纹识别 / 说话人验证为主业、语音 agent / 对话式 AI、ASR 模型训练科研深水区。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on 语音转文字 (ASR / 语音识别 / Speech-to-Text) 应用工程 (从业者视角) — 选型、集成、优化语音转文字能力，尤其面向移动端、低成本、快速识别的场景。覆盖: (a) 引擎/API 地图 — 云端 API(OpenAI gpt-4o-transcribe / Whisper API、Deepgram、AssemblyAI、Google / Azure / AWS Transcribe、讯飞、字节火山、阿里、腾讯、百度) vs 开源模型(Whisper / faster-whisper / whisper.cpp / distil-whisper、NVIDIA NeMo Parakeet / Canary、阿里 FunASR / Paraformer / SenseVoice、Moonshine、Vosk、Kaldi / k2 / icefall) vs 端侧·移动 SDK(whisper.cpp + CoreML / Metal、iOS Speech framework、Android SpeechRecognizer、Picovoice、SenseVoice 端侧); (b) 准确率(WER / CER) × 延迟(RTF / 流式) × 成本 三角权衡与选型决策树; (c) 成本优化 playbook — 端侧免费 / 批量折扣 / VAD 裁静音 / 量化(int8 / ggml) / 蒸馏 / 自托管 break-even; (d) 移动端集成 — 端侧 vs 云、流式 vs 批量、断点检测(endpointing)、隐私 / 离线; (e) 后处理 — 标点 / 数字规整(ITN) / 说话人分离(diarization) / 时间戳。学派分歧: 云 API vs 端侧自托管、通用大模型(Whisper) vs 专用流式(RNN-T / Conformer)、闭源 API vs 开源、准确率派 vs 成本派、英文优先 vs 中文 ASR(FunASR / SenseVoice / 讯飞)。不含: 文字转语音(TTS / 语音合成，方向相反)、声纹识别 / 说话人验证为主业、语音 agent / 对话式 AI、ASR 模型训练科研深水区。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「语音转文字」「语音识别」「asr」「speech to text」「stt」

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。

临床诊断思维 (临床诊断思维 / 临床推理 (Clinical Diagnostic Reasoning) — 医生『怎么想病』的元学科:从症状/体征/检查到诊断结论的认知操作系统,从业者(临床医生/住院医/规培生,尤其全科/急诊/大内科等以未分化主诉为生的科室)、医学生与医学教育者、诊断安全与质量改进研究者、以及做医疗 AI 辅助诊断产品的人的视角。覆盖: (a) 第一性张力 — **直觉模式识别 (System 1: illness scripts 疾病脚本 / pattern recognition / gestalt, 『资深人一眼认出 aunt Minnie』) ⇄ 分析性推理 (System 2: hypothetico-deductive 假设演绎 / Bayesian 概率更新)** 的 dual-process 双过程理论 (Kahneman→Croskerry/Norman 谱系), 资深≠更会分析而是『脚本库更大+校准更好』; 更深层论战 — **『认知去偏可教 (Croskerry: bias awareness / cognitive forcing strategies / diagnostic timeout) ⇄ 偏倚标签是马后炮、知识结构才是主因 (Norman/Sherbino/Monteiro: debiasing 干预 transfer 证据弱, bias 是 hindsight 标签)』** — 本行最核心学术对垒; 『概率思维 (验前概率 × 似然比 → 验后概率, Pauker-Kassirer test/treatment threshold 阈值模型) ⇄ 穷尽式排查 (rule-out everything / 防御性医疗 / VOMIT)』; 『临床 gestalt ⇄ 结构化临床决策规则 (Wells/PERC/HEART)』; 『诊断简约 Occam's razor ⇄ Hickam's dictum (病人可以同时得 N 个病)』; 『床旁体格检查复兴 (Verghese Stanford 25 / McGee 循证体检) ⇄ 影像检验替代床旁』; (b) 方法论正典 — illness script theory (Schmidt/Boshuizen: enabling conditions/fault/consequences 三段结构), problem representation + semantic qualifiers 问题表征与语义限定词 (Bowen NEJM 2006, 把病人翻成 one-liner), hypothetico-deductive model (Elstein 1978《Medical Problem Solving》: 早期假设生成+定向检验), Bayesian 工具箱 (sensitivity/specificity/LR/Fagan nomogram/SnNout-SpPin), threshold model (Pauker-Kassirer NEJM 1975/1980), 认知偏倚分类学 (anchoring/premature closure/availability/confirmation/base-rate neglect/search satisficing/diagnostic momentum — Croskerry 偏倚清单), 去偏与元认知策略 (diagnostic timeout/cognitive forcing/Ely checklist/calibration), schema-based reasoning (Clinical Problem Solvers schemas: 按 pivot 症状走分支), reflective practice 结构化反思 (Mamede/Schmidt), 诊断错误科学 (NAM 2015 报告定义 / Newman-Toker Big Three / Hardeep Singh e-triggers / SAFER Dx 框架); (c) 行业结构与角色 — 医学生→实习/住院医 (晨会 morning report/查房被 pimping/汇报训练)→主治 attending→master clinician (NEJM CPC discussant/晨会大师如 Dhaliwal); 配套生态: 医学教育者 (clinical reasoning curriculum + 评估: script concordance test/key features exam/OSCE), 诊断安全研究者 (SIDM/AHRQ), 诊断辅助与 CDS 工具开发者 (DDx generator/AI); (d) 核心工作流 — 数据采集 (病史为王 + 循证体检 + 针对性检验影像) → 问题表征 (one-liner + semantic qualifiers) → 鉴别诊断生成 (schema / 解剖定位法 / VINDICATE-M 病因筛, 按『常见可能 × 致命不能漏 can't-miss』双轴排序) → 假设定向检验 (按 LR 选检查 / 阈值决策) → working diagnosis + 安全网 (red flags 交代 / test of time / test of treatment) → 反馈校准 (follow-up / M&M / diagnostic timeout); 教学工作流: 晨会渐进披露汇报 / CPC / SNAPPS / one-minute preceptor / virtual morning report; AI 增强工作流 (2023-2026: LLM 鉴别诊断头脑风暴 / OpenEvidence 检索 / ambient scribe 释放认知带宽 + 自动化偏倚 guardrails); (e) 产出物 — one-liner, problem list, prioritized DDx, assessment & plan (按问题分层), 晨会/CPC 汇报, M&M 复盘, 诊断不确定性沟通 (『最可能是 X, 但出现 Y 红旗立刻回来』); (f) 教育与评估 — script concordance test / key feature exam / EPA / 里程碑; 中国语境: 人卫《诊断学》教材 / 执业医师考试 / 规培结业临床思维考核; (g) 争议/批判 — debiasing 之争 (Croskerry vs Norman-Sherbino『knowledge is the cure』, 去偏 RCT 效果弱 / bias 标签不可证伪), 诊断错误率数字之争 (『10-15%』经典估计 / Newman-Toker 79.5 万美国年严重伤害外推方法被质疑 / 尸检符合率), dual-process 二分被批过度简化 (连续谱 / 难以实证分离), 决策规则 vs gestalt (资深 gestalt 常不输 Wells 类规则 / 算法厌恶), vignette 研究外推性 + context/case specificity (推理高度内容绑定不可通用迁移 [Norman/Eva] — 对『教推理通用课』产业的根本批判), AI 辅助诊断 2023-2026 (GPT-4 在 NEJM CPC/vignette 追平或超医生 [Kanjee 2023 JAMA / Goh 2024 JAMA Netw Open『AI alone > physician+AI』悖论] / Google AMIE / OpenEvidence 爆发 vs 自动化偏倚 / 去技能化 / 真实环境验证缺失), 防御性医疗与过度检查 (incidentaloma 瀑布 / VOMIT), 体检衰亡之争, pimping 教学法争议; (h) 流派/思想谱系 — 决策科学/Bayesian 派 (Ledley-Lusted 1959 → Elstein/Kassirer/Sox/Pauker/Eddy → Brush) vs 认知心理/双过程派 (Kahneman-Tversky → Croskerry/Graber) vs 教育认知派 (Schmidt/Boshuizen illness scripts → Norman/Eva/Mamede/Durning 情境认知) vs 诊断安全/系统派 (NAM 2015 / Newman-Toker / Hardeep Singh / Graber-SIDM: 错误=系统×认知共因) vs 床旁临床派 (Osler 传统 → Tierney aphorisms / Verghese / McGee / Dhaliwal / Saint: 病史体检为王 + 刻意练习) vs AI/计算派 (INTERNIST-1/DXplain 专家系统 → Isabel → LLM 世代 Rodman/Topol)。不含: 具体专科治疗方案与手术决策深度 (足踝外科/种植牙等另有 skill), 中医辨证 (另有 skill), EBM 文献批判性评价方法学 (相关但本 skill 聚焦诊断推理本身), 护理诊断体系 (NANDA), 影像/病理知觉型读片训练 (相邻但独立), 精神科 DSM 结构化访谈细节, 医患沟通技巧全集 (只覆盖诊断不确定性沟通), 患者自查/自我诊断指

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。

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

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。

AI 短剧 (AI 微短剧) (AI 短剧 (AI 微短剧) (从业者视角) — 用 AIGC 工具（AI 视频生成 / AI 脚本 / AI 生图 / AI 配音 / 对口型 / 角色一致性）制作竖屏微短剧并变现的新兴行业。覆盖: (a) 创作工作流 — 选题→剧本/分镜→AI 生图→图生视频(首尾帧/运镜/参考)→配音/对口型→剪辑/调色→成片，其中角色/场景一致性是第一性难题; (b) 工具栈 — 视频生成(可灵 Kling / 即梦 Seedance / 海螺 Hailuo / Vidu / Runway Gen / OpenAI Sora / Pika / Luma / Pixverse)、脚本(LLM)、生图(Midjourney/SD/即梦)、配音(TTS)、对口型、剪辑(剪映/CapCut); (c) 商业/变现 — 国内付费投流(IAP + ROI + 平台分账, 抖音/快手/红果/微信小程序短剧)与海外订阅(ReelShort/DramaBox/ShortMax/GoodShort)两套打法、品牌定制、成本结构; (d) 合规 — 中国《人工智能生成合成内容标识办法》(2025-09 强制 AI 标识)、广电微短剧备案与分类分层审核、版权与肖像权。主战场 = 抖音/快手/视频号/B站 + 海外短剧 App。学派分歧: 纯 AI vs AI+实拍混合、国内投流 vs 海外订阅、视频工具派之争、「AI 短剧是真需求还是伪风口」。不含: 传统实拍短剧制作、通用影视编剧(已有 scriptwriting skill)、短视频信息流投放(已有 short-video-ads skill)、纯 AI 视频底层技术研发。) Master OS — automated mastery of AI 短剧 (AI 微短剧) (从业者视角) — 用 AIGC 工具（AI 视频生成 / AI 脚本 / AI 生图 / AI 配音 / 对口型 / 角色一致性）制作竖屏微短剧并变现的新兴行业。覆盖: (a) 创作工作流 — 选题→剧本/分镜→AI 生图→图生视频(首尾帧/运镜/参考)→配音/对口型→剪辑/调色→成片，其中角色/场景一致性是第一性难题; (b) 工具栈 — 视频生成(可灵 Kling / 即梦 Seedance / 海螺 Hailuo / Vidu / Runway Gen / OpenAI Sora / Pika / Luma / Pixverse)、脚本(LLM)、生图(Midjourney/SD/即梦)、配音(TTS)、对口型、剪辑(剪映/CapCut); (c) 商业/变现 — 国内付费投流(IAP + ROI + 平台分账, 抖音/快手/红果/微信小程序短剧)与海外订阅(ReelShort/DramaBox/ShortMax/GoodShort)两套打法、品牌定制、成本结构; (d) 合规 — 中国《人工智能生成合成内容标识办法》(2025-09 强制 AI 标识)、广电微短剧备案与分类分层审核、版权与肖像权。主战场 = 抖音/快手/视频号/B站 + 海外短剧 App。学派分歧: 纯 AI vs AI+实拍混合、国内投流 vs 海外订阅、视频工具派之争、「AI 短剧是真需求还是伪风口」。不含: 传统实拍短剧制作、通用影视编剧(已有 scriptwriting skill)、短视频信息流投放(已有 short-video-ads skill)、纯 AI 视频底层技术研发。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on AI 短剧 (AI 微短剧) (从业者视角) — 用 AIGC 工具（AI 视频生成 / AI 脚本 / AI 生图 / AI 配音 / 对口型 / 角色一致性）制作竖屏微短剧并变现的新兴行业。覆盖: (a) 创作工作流 — 选题→剧本/分镜→AI 生图→图生视频(首尾帧/运镜/参考)→配音/对口型→剪辑/调色→成片，其中角色/场景一致性是第一性难题; (b) 工具栈 — 视频生成(可灵 Kling / 即梦 Seedance / 海螺 Hailuo / Vidu / Runway Gen / OpenAI Sora / Pika / Luma / Pixverse)、脚本(LLM)、生图(Midjourney/SD/即梦)、配音(TTS)、对口型、剪辑(剪映/CapCut); (c) 商业/变现 — 国内付费投流(IAP + ROI + 平台分账, 抖音/快手/红果/微信小程序短剧)与海外订阅(ReelShort/DramaBox/ShortMax/GoodShort)两套打法、品牌定制、成本结构; (d) 合规 — 中国《人工智能生成合成内容标识办法》(2025-09 强制 AI 标识)、广电微短剧备案与分类分层审核、版权与肖像权。主战场 = 抖音/快手/视频号/B站 + 海外短剧 App。学派分歧: 纯 AI vs AI+实拍混合、国内投流 vs 海外订阅、视频工具派之争、「AI 短剧是真需求还是伪风口」。不含: 传统实拍短剧制作、通用影视编剧(已有 scriptwriting skill)、短视频信息流投放(已有 short-video-ads skill)、纯 AI 视频底层技术研发。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「ai 短剧」「ai 微短剧」「ai short drama」「ai 短剧制作」「ai 短剧工作流」

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。

Open a GitHub PR via API as a flowing graph — branch + push + create_pr + mergeable poll, with structural protection against pushing to main/master/etc. Use when a Claude Code or Claude.ai container needs to land changes on GitHub via the API (no git CLI required) and the prose "create branch, push, open PR, wait for mergeable" workflow keeps drifting under context pressure.

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。

ARK 套餐管理(Agent Plan / Coding Plan,个人版 + 企业版):查询持有 / 购买 / 续费 / 模型清单 / 轮换 APIKey,**以及企业版席位的全部管理操作**:列出席位(`plans team seat-list`)、给员工分配席位(`plans team seat-assign`)、查谁绑了哪个 seat、轮换席位 APIKey(`plans team rotate-apikey`)。也负责购买参数的逐步澄清，以及 `payment_failed` + Order ID 后回到已有订单补单/取消，禁止重新 buy。命中关键词:套餐 / 买 / 续费 / 列席位 / 看 seat 绑定 / 谁绑了哪个 seat / 给员工分席位 / 解绑 / 团队席位 admin 视图 / 轮换 APIKey。反触发：Token 额度包/资源包不是 Agent Plan/Coding Plan，应走 arkcli-profile 判断 platform 切面。**用量类问题(还剩多少额度 / 用了几成 / 每个 seat 用了多少)走 [arkcli-usage](../arkcli-usage/SKILL.md)。**动词路由:**列 / 绑 / 分 / 轮换 / 解绑** → 这里;**用 / 消耗 / 多少** → arkcli-usage。另含 harness-status:只读查看本机 AI Agent(claude-code/codex/opencode/openclaw/trae)上 Agent Plan 内置 MCP(豆包搜索/dataPro/OpenViking)装没装、key 就没就绪;命中:我的 MCP 装好了吗 / 查 MCP 安装状态 / 看 agent 上的 MCP / harness 状态。

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。

arkcli helper：为 Claude Code / Codex / OpenCode / OpenClaw / Hermes / ZCode 配置火山方舟 Plan 或 Platform Endpoint 的 model/provider，或给支持的 Agent（含 MCP-only Trae）配置 Harness 工具。真人 TTY 运行 `arkcli helper` 可选择模型和 Harness 能力；`helper mcp` 默认保持整组 Agent Plan MCP 行为，传 `--capability datapro|web-search|agent-memory` 只配单项，传 `--capability cua` 只给目标 Agent 安装云电脑 Skill；连 model/provider 一起配置用 `helper configure`，查状态用 `helper list`，移除用 `helper reset`。Agent Plan / Team 还支持专业数据集、豆包搜索、Agent 记忆、AI Native 应用开发底座、Agent 进化与 routing.v1 强制检索路由；上下文出现 ARKCLI ROUTING ENFORCEMENT / routing.v1，或用户要求在豆包搜索、DataPro、OpenViking、Supabase 之间强制分流时也使用本 Skill：调用这四类受管 Provider 前先提交 Route Plan，再调用唯一授权 Provider；原生 WebSearch/WebFetch/WebExtract 位于路由管理范围之外。Platform 只允许本人创建、Running、已验证为文本输出的 Endpoint，不配置 MCP、Supabase 或强制路由。豆包搜索配置成功后默认关闭目标客户端原生 WebSearch；传 `--keep-native-websearch` 可保留，并可用 `helper reset` 完整恢复 ArkCLI 配置。

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。

Interactively pick a quick-win contribution in any GitHub repo — either a labeled issue ("good first issue", "help wanted", docs) or a repo-scan candidate (typo, missing tests, i18n gap, actionable TODO). Runs a TDD dev-loop, opens a PR via gh, and returns the PR URL. Use when the user wants to auto-contribute to an open-source project end-to-end. Trigger words: auto-contribute, open a PR for me, find a good first issue, contribute to <repo>.

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。

Open Analytics' design language, extracted from the shipped product: an ink-derived neutral system, squircle surface anatomy, one small spring vocabulary, pixel-matched skeletons, and a plain-spoken copy voice, with type-checked component recipes. Use this whenever you build or restyle a dashboard, SaaS app, analytics UI, settings screen, onboarding flow, or marketing/landing page that should feel calm, dense and native, and whenever the user mentions Open Analytics' look, 'clean SaaS design', cards, dropdowns, tab bars, empty states, skeletons, or UI animation quality, even if they never say the words 'design system'.

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。

Turns open-ended requests into things to think WITH instead of answers to accept. Audits the hidden assumptions inside a topic, inverts them into sharp "what if" provocations, branches each one outward, and delivers a fully-expanded mind map image plus the written wonderings. Fire this when the person signals they want to open up a space and think alongside you — "wonder about X", "what are the weird angles on X", "help me think about what this could be", "I want to explore, not decide yet" — or when they invoke /wonder or /wonderpill. Do NOT fire when the same topic is attached to a request for concrete, usable, or decided output: a named deliverable, a count ("give me 5 names"), a deadline ("ideas I can ship this week"), or a "which / what should I pick" question — those want straight answers, not a wonder pill. When it is genuinely unclear which they want, this skill opens by asking one line rather than branching. Not for factual lookups or executing a plan already chosen.

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。

Turns open-ended requests into things to think WITH instead of answers to accept. Audits the hidden assumptions inside a topic, inverts them into sharp "what if" provocations, branches each one outward, and delivers a mind map plus written wonderings. Portable version, no vendor-specific tools required, works on any conversational assistant. Fire this when the person signals they want to open up a space and think alongside the assistant: "wonder about X", "what are the weird angles on X", "help me think about what this could be", "I want to explore, not decide yet". Do NOT fire when the same topic is attached to a request for concrete, usable, or decided output: a named deliverable, a count ("give me 5 names"), a deadline, or a "which / what should I pick" question. When it is genuinely unclear which they want, this skill opens by asking one line rather than branching. Not for factual lookups or executing a plan already chosen.

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。

Create and render OpenSCAD 3D models. Generate preview images from multiple angles, extract customizable parameters, validate syntax, and export STL files for 3D printing.

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。

This skill should be used when the user asks to "write a hook for my post", "give me opening lines", "help me start this LinkedIn post", "I need a better opener", or when the linkedin-post-writer skill needs strong opening options. Also triggered by "my posts aren't getting clicks" or "how do I get people to read my posts".

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。

You launched an ambassador program. 3 applications. One ghosted after week 2. Should you lower the bar? Offer more perks? Spam influencers? This gives you the community & ambassador operations SOP with volunteer, paid tutorial creator and code-contributor tracks — from pre-launch checklist to recruitment to tiered management to retention to governance. Built from Notion (20M users, real ambassador interviews), AFFiNE (60K stars), Asana, and ClickUp programs. By @WeiYipei. 🇨🇳 你搞了个大使计划。来了3个申请。一个第二周就消失了。该降门槛？加福利？群发KOL？这份SOP给你从启动前置条件到招募到留存到治理的完整社区/大使运营方法论。基于 Notion（2000万用户，大使真实访谈）、AFFiNE（60K stars）、Asana、ClickUp 实战案例。 🇯🇵 アンバサダープログラムを立ち上げた。応募3件。1人は2週間で消えた。基準を下げる？特典を増やす？このSOPは、事前チェックリストから採用、段階管理、リテンション、ガバナンスまでの完全なコミュニティ＆アンバサダー運営フレームワークを提供します。Notion（2000万ユーザー）、AFFiNE（60K stars）の実例から構築。 🇰🇷 앰배서더 프로그램을 시작했습니다. 지원 3건. 하나는 2주 만에 사라졌습니다. 기준을 낮출까요? 이 SOP는 사전 체크리스트부터 모집, 단계별 관리, 리텐션, 거버넌스까지 완전한 커뮤니티 & 앰배서더 운영 프레임워크를 제공합니다. Notion(2000만 사용자), AFFiNE(60K stars) 실전 사례에서 구축. Triggers: "ambassador program" | "community building" | "community management" | "DevRel" | "developer relations" | "community growth" | "大使计划" | "社区运营" | "社区搭建" | "コミュニティ運営" | "アンバサダー" | "커뮤니티 운영" | "앰배서더" | "open source community" | "user retention"

Marketing playbook for open-source projects led by non-technical founders. Covers README optimization, community building, contributor attraction, and translating OSS traction into revenue. By @WeiYipei.

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。

You launched an ambassador program. 3 applications. One ghosted after week 2. Should you lower the bar? Offer more perks? Spam influencers? This gives you the complete community & ambassador operations SOP — from pre-launch checklist to recruitment to tiered management to retention to governance. Built from Notion (20M users, real ambassador interviews), AFFiNE (60K stars), Asana, and ClickUp programs. By @WeiYipei. What's inside: • Pre-launch readiness checklist (6 conditions that must be true before starting) • 6-step community setup SOP (platform selection → rules → admin standards → monitoring → wiki → metrics) • Ambassador recruitment system (A+B qualification, application form design, scoring rubric) • 4-tier progression system (Bronze → Silver → Gold → Platinum with point thresholds) • 5-pillar incentive framework (Product quality → Beta access → Founder access → KOL path → Full engagement) • Churn early-warning system + retention playbook • Event operations SOP (6 event types × full lifecycle) • Health metrics dashboard (6 core KPIs + 5 ambassador-specific indicators) • Governance & risk control (behavior code, escalation matrix) • Real templates: recruitment post, welcome email, annual calendar 🇨🇳 你搞了个大使计划。来了3个申请。一个第二周就消失了。该降门槛？加福利？群发KOL？这份SOP给你从启动前置条件到招募到留存到治理的完整社区/大使运营方法论。基于 Notion（2000万用户，大使真实访谈）、AFFiNE（60K stars）、Asana、ClickUp 实战案例。 🇯🇵 アンバサダープログラムを立ち上げた。応募3件。1人は2週間で消えた。基準を下げる？特典を増やす？このSOPは、事前チェックリストから採用、段階管理、リテンション、ガバナンスまでの完全なコミュニティ＆アンバサダー運営フレームワークを提供します。Notion（2000万ユーザー）、AFFiNE（60K stars）の実例から構築。 🇰🇷 앰배서더 프로그램을 시작했습니다. 지원 3건. 하나는 2주 만에 사라졌습니다. 기준을 낮출까요? 이 SOP는 사전 체크리스트부터 모집, 단계별 관리, 리텐션, 거버넌스까지 완전한 커뮤니티 & 앰배서더 운영 프레임워크를 제공합니다. Notion(2000만 사용자), AFFiNE(60K stars) 실전 사례에서 구축. Triggers: "ambassador program" | "community building" | "community management" | "DevRel" | "developer relations" | "community growth" | "大使计划" | "社区运营" | "社区搭建" | "コミュニティ運営" | "アンバサダー" | "커뮤니티 운영" | "앰배서더" | "open source community" | "user retention"

Use as the follow-up to brainstorm-beagle when a spec has an Open Questions section (or quietly carries latent gaps) that need closing before planning or implementation can begin. Triggers on: "resolve the open questions", "close the gaps in this spec", "research the open items", "finalize my spec", "make this spec implementation-ready", "answer the TBDs". Also triggers whenever the user points at a brainstorm-beagle spec and asks for research, proposals, or answers to unresolved items. Orchestrates parallel research subagents when available (falls back to inline sequential research otherwise), proposes answers one at a time for user approval, then rewrites the spec in place so it arrives at planning with no known gaps. Does NOT write code, design implementation, or create plans — it only produces a complete spec.

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。

Create or update OpenClaw public-page composition for websites, landing pages, content pages, ecosystem pages, and campaigns. Use for page structure, navigation, responsive layout, media, SEO presentation, or public-site visual polish.

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。

Build or modify OpenClaw application UI using canonical semantic tokens, themes, shared CSS foundations, consumer adapters, and established local primitives. Use for product interfaces, component styling, theme work, or design-token integration.

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。