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ターミナルで動くコーディング用エージェント基盤のPiを導入・設定し、独自の拡張や画面の作成、別アプリへの組み込み、外部ツールとの連携を支援します。

• Piの導入とモデル設定
• 独自ツールや操作画面の作成
• 別アプリへのPiの組み込み

CADファイルからカスタム部品を製造するため、Fictivのブラウザ画面で加工方法や材質を設定し、見積もり比較、製造上の警告確認、発注・追跡を進めるスキル。

• CAD部品の製造見積もりを取りたいとき
• 材質・数量・納期ごとの価格比較
• 製造上の警告やアップロード不具合の確認

実行ファイルやAPK、WASMなどの内部動作を、文字列の抽出や静的・動的解析で読み解き、難読化や解析を妨げる仕組みを調べるスキルです。

• 未知の実行ファイルの動作を理解したいとき
• 難読化やデバッガー検出処理の調査
• CTFの入力判定問題を解きたいとき

Build the effects of a close-combat energy skill as one visual language in Three.js. Lightning that reads as lightning, dynamic but never in your face: trees of strips (trunk, forks of forks, hair-fine branchlets) with a fine white core in a violet-blue glow, each channel reaching out as a stepped leader, flaring as it connects, holding its shape and fading through an afterglow, one discharge at a time; a storm-cloud orb (a lit billow surface that churns in place, 10-15 plasma-globe arcs from a small white core to its inner wall, crawlers on the rim, a hairline fresnel); ground strikes that flash when the leader arrives, crawl along the stone's own cracks and reflect in wet stone (the demo's floor is a scanned CC0 texture); a torn black shadow with dry-brush edges that hangs off the orb and streams behind it, air bursts felt as refraction, orange sparks, fractured rocks that land or float in a storm domain, and impact frames that hold, invert in two-tone ink with tapered speed lines, shake and bend the lens, limited to three flashes a second. Use for anime or dark-fantasy skill effects, lightning or thunder fists, plasma or energy balls, charge-ups, dashes with shadow trails, ground strikes, storm or domain rings, ultimates, hit-stop, impact frames, negative flashes, screen shake, "black lightning", "thunderstorm fist", "realistic lightning", "afterimage" or "skill effects only". Includes a reusable module and a five-beat demo with failure-mode switches.

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。

Searches Douyin (douyin.com) for videos by keyword and returns structured video data including author info, stats, cover, description, hashtags, and download URL. Supports date range filtering and sorting by relevance, likes, or recency. Use when user mentions Douyin search, scrape Douyin videos, collect TikTok China videos, extract douyin video data, grab douyin results, fetch douyin keyword videos, douyin video list, douyin content mining, search douyin by keyword, douyin likes filter, douyin date filter, douyin video download links, douyin creator info, douyin hashtag extraction, douyin video scraper, douyin KOL research, douyin content analysis.

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。

Production-tested setup for Tailwind CSS v4 with shadcn/ui, Vite, and React. Use when: initializing React projects with Tailwind v4, setting up shadcn/ui, implementing dark mode, debugging CSS variable issues, fixing theme switching, migrating from Tailwind v3, or encountering color/theming problems. Covers: @theme inline pattern, CSS variable architecture, dark mode with ThemeProvider, component composition, vite.config setup, common v4 gotchas, and production-tested patterns. Keywords: Tailwind v4, shadcn/ui, @tailwindcss/vite, @theme inline, dark mode, CSS variables, hsl() wrapper, components.json, React theming, theme switching, colors not working, variables broken, theme not applying, @plugin directive, typography plugin, forms plugin, prose class, @tailwindcss/typography, @tailwindcss/forms

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。

Audit whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan 8-type / 3-category leakage taxonomy; the illegitimate-proxy / sampling-bias / pretraining-contamination subtypes hand off as needs_external_check, naming but NEVER running Oren-2023 exchangeability / Shi-2023 Min-K% / Golchin-2023 Time-Travel / BIG-bench canary); a load-bearing LLM judge is conflicted (same model/family as a compared system) or unvalidated (no human-agreement, no bias control) (HP-JUDGE-VALIDITY); a declared condition/metric is dropped or switched to favor the method, or 'best' is chosen with no held-out set (HP-SELECTIVE-REPORTING). Verdict-bearing at L0/L1 from the DESCRIBED protocol — NOT repo-gated like experiment-forensics; L2 only CONFIRMS against split/preprocessing/result files. A fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per pass) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Leakage and under-reporting are usually HONEST methodological errors — every finding describes a discrepancy to CHECK, never an accusation. An LLM generating GROUND-TRUTH labels is HP-FAKE-GT (experiment-forensics) — routed there, not here. Emits eval-design-forensics.findings.json; computes NO verdict. Detect-only. Triggers: "eval design audit", "evaluation validity", "train/test leakage", "data leakage", "is the score measuring generalization", "LLM judge bias", "is the judge validated", "selective reporting", "cherry-picked results", "评估设计审计", "评测有效性", "数据泄漏", "训练测试集泄漏", "裁判模型有没有验证", "选择性报告".

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。

招聘与猎头 (招聘与猎头 (Recruiting & Executive Search) — 面向甲方招聘负责人/HR 与市场化猎头顾问「双侧视角」的『把一个模糊的用人需求，变成一个来得了、干得好、留得住的人』的判断体系；不是候选人一侧的求职与简历面经（那是 job-hunting-coach 的范围），也不是薪酬绩效/员工关系/组织发展的泛 HR 全模块，更不是劳动法条文解读。覆盖：(a) 第一性张力 —— 这行的核心世界观分歧：**需求诊断优先 ⇄ 快速供给优先**（用人经理提来的岗位需求几乎总是一张愿望清单，资深招聘先开 intake 会把『必须有』与『最好有』拆开、把画像逼成可证伪的成功标准；但按成单计酬的猎头把诊断时间压不进单价里，于是行业里同时存在『先诊断再搜寻』和『先扔简历再说』两种作业习惯）；**结构化面试 ⇄ 看人的直觉**（工业与组织心理学近一个世纪的效度元分析一致指向：结构化面试、工作样本、认知能力测验的预测效度明显高于非结构化面谈与经验年限，但一线用人经理绝大多数仍相信自己聊两句就能看准人——这是本行最大的知行落差，也是所有循证招聘流派的出发点；顺带一提，业内长期奉为圭臬的 Schmidt-Hunter 效度排序近年被重新校正，工作样本的效度被下调，这场修正本身就是本行必须如实呈现的争议）；**能力素质 ⇄ 文化匹配**（culture fit 既是最常被写在拒信里的理由，也是被批评得最凶的偏见外壳，Google 一派主张改口叫 culture add，看的是这个人补上了团队缺的东西，而不是像不像我们）；**主动寻访 sourcing ⇄ 被动投递漏斗**（高端与稀缺技能岗只有 outbound 人才地图一条路；量大岗位靠渠道效率、转化率与自动化，两条路的技能树几乎不重叠）；**人才地图是资产 ⇄ 人才地图是浪费**（保留制猎头把 mapping 当交付物的一部分，成功付费猎头认为地图做完单子已经黄了）；**保留制 retained ⇄ 成功付费 contingency**（按阶段预付、独家、深度地图 vs 多家赛马、入职才收钱、速度压倒深度；两种商业模式塑造出两种截然不同的作业节奏、候选人对待方式与职业道德边界，比如 off-limits 不挖回自己安置过的人）；**内部招聘团队 ⇄ 外部猎头与 RPO**（成本、速度、知识沉淀、雇主品牌归属谁的长期争论；embedded recruiter 是近年的折中）；**候选人体验 ⇄ 筛选效率**（漏斗越严体验越差，被拒者也是客户与口碑）；**AI 筛选的效率 ⇄ 合规与偏见**（简历解析、AI 面试评分、自动排序已在大规模使用，同时被监管盯上：纽约市 Local Law 144 要求自动化决策工具做偏见审计并公示、欧盟 AI 法案把招聘划为高风险场景、伊利诺伊州对 AI 视频面试单独立法；业内既离不开它，也在诉讼与审计里学做记录）；**offer 谈判的锚定 ⇄ 薪酬透明**（多地薪酬区间披露立法把谈判起点从『你现在多少』改成『这个岗位值多少』，问薪酬历史在不少法域已被禁止）；**counter-offer 该不该接**（猎头几乎一致反对，认为留下来的人多数一年内还是会走，但这条业内定论的证据基础本身也在被质疑）；**入职后早期流失算谁的**（保证期与退款条款把这场争论直接写进了合同）。(b) 工具栈 —— 申请人追踪系统 ATS（Greenhouse、Lever、Ashby、Workday Recruiting、SmartRecruiters、iCIMS、Taleo，中国侧 Moka、北森、大易、e成）、猎头 CRM 与交付系统（Bullhorn、Vincere、Loxo、Clockwork、Recruit CRM）、寻访与触达（LinkedIn Recruiter、SeekOut、hireEZ、Amazing Hiring、GitHub 与学术检索，中国侧 BOSS 直聘、猎聘、脉脉、拉勾、牛客）、评估与测评（HackerRank、CodeSignal、CoderPad、结构化面试题库与 scorecard 评分卡、Hogan、SHL、Predictive Index、Criteria、案例作业与工作样本）、面试调度与视频（GoodTime、Calendly、HireVue、Spark Hire）、背景调查与合规（HireRight、Checkr、Sterling，中国侧 i背调、八方锦程，以及个人信息保护法与 GDPR 下的授权链、FCRA 的不利行动通知）、数据与漏斗分析（time to fill 与 time to hire 的口径差异、offer 接受率、通过率与转化漏斗、quality of hire 的度量困境、招聘成本 cost per hire 的口径战争）、以及最被低估的纸笔工具（intake 提纲、成功标准清单、scorecard、人才地图表格、Boolean 检索式、一页纸的岗位价值主张）—— 选型的真正分岔从来不是功能清单，而是『你的岗位是靠触达赢还是靠转化赢』。(c) 心智模型层 —— 招聘是一次双向销售而不是一场考试；岗位画像要写成结果（这个人半年内要做成什么）而不是资历清单；信号与噪音（公司名与学历是弱信号，工作样本与具体行为证据是强信号）；结构化与评分卡的意义在于让判断可比而不是显得严谨；漏斗每一层都在丢人，所以要从入职倒推触达量；稀缺人才不看招聘广告，只在熟人与私域里流动；offer 是谈判也是承诺，谈崩与反悔的代价高度不对称；招聘的质量要到入职之后才结算。(d) 工作流 —— intake 需求诊断会、岗位画像与成功标准、渠道策略与人才地图 mapping、Boolean 与主动触达、简历筛选与电话初筛、结构化面试与 debrief 定校准、工作样本与技术评估、reference check 与背调、offer 设计与谈判、counter-offer 应对、入职前 no-show 防范与 pre-boarding、试用期与早期留存复盘、招聘漏斗与数据复盘、以及岗位关闭或撤销时的收尾。(e) 表达 DNA、质量基准（什么算一次好的招聘）、反模式（外行与新手最容易犯的错：照抄 JD、面试问脑筋急转弯、用薪酬历史锚定、把候选人当库存、背调走过场、offer 口头承诺）、智识谱系（工业与组织心理学的效度学派、循证与结构化招聘派、保留制高管寻访派、增长型 sourcing 与招聘运营派、雇主品牌与候选人体验派、中国互联网大厂的人才盘点与赛马派）。) Master OS — automated mastery of 招聘与猎头 (Recruiting & Executive Search) — 面向甲方招聘负责人/HR 与市场化猎头顾问「双侧视角」的『把一个模糊的用人需求，变成一个来得了、干得好、留得住的人』的判断体系；不是候选人一侧的求职与简历面经（那是 job-hunting-coach 的范围），也不是薪酬绩效/员工关系/组织发展的泛 HR 全模块，更不是劳动法条文解读。覆盖：(a) 第一性张力 —— 这行的核心世界观分歧：**需求诊断优先 ⇄ 快速供给优先**（用人经理提来的岗位需求几乎总是一张愿望清单，资深招聘先开 intake 会把『必须有』与『最好有』拆开、把画像逼成可证伪的成功标准；但按成单计酬的猎头把诊断时间压不进单价里，于是行业里同时存在『先诊断再搜寻』和『先扔简历再说』两种作业习惯）；**结构化面试 ⇄ 看人的直觉**（工业与组织心理学近一个世纪的效度元分析一致指向：结构化面试、工作样本、认知能力测验的预测效度明显高于非结构化面谈与经验年限，但一线用人经理绝大多数仍相信自己聊两句就能看准人——这是本行最大的知行落差，也是所有循证招聘流派的出发点；顺带一提，业内长期奉为圭臬的 Schmidt-Hunter 效度排序近年被重新校正，工作样本的效度被下调，这场修正本身就是本行必须如实呈现的争议）；**能力素质 ⇄ 文化匹配**（culture fit 既是最常被写在拒信里的理由，也是被批评得最凶的偏见外壳，Google 一派主张改口叫 culture add，看的是这个人补上了团队缺的东西，而不是像不像我们）；**主动寻访 sourcing ⇄ 被动投递漏斗**（高端与稀缺技能岗只有 outbound 人才地图一条路；量大岗位靠渠道效率、转化率与自动化，两条路的技能树几乎不重叠）；**人才地图是资产 ⇄ 人才地图是浪费**（保留制猎头把 mapping 当交付物的一部分，成功付费猎头认为地图做完单子已经黄了）；**保留制 retained ⇄ 成功付费 contingency**（按阶段预付、独家、深度地图 vs 多家赛马、入职才收钱、速度压倒深度；两种商业模式塑造出两种截然不同的作业节奏、候选人对待方式与职业道德边界，比如 off-limits 不挖回自己安置过的人）；**内部招聘团队 ⇄ 外部猎头与 RPO**（成本、速度、知识沉淀、雇主品牌归属谁的长期争论；embedded recruiter 是近年的折中）；**候选人体验 ⇄ 筛选效率**（漏斗越严体验越差，被拒者也是客户与口碑）；**AI 筛选的效率 ⇄ 合规与偏见**（简历解析、AI 面试评分、自动排序已在大规模使用，同时被监管盯上：纽约市 Local Law 144 要求自动化决策工具做偏见审计并公示、欧盟 AI 法案把招聘划为高风险场景、伊利诺伊州对 AI 视频面试单独立法；业内既离不开它，也在诉讼与审计里学做记录）；**offer 谈判的锚定 ⇄ 薪酬透明**（多地

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。

AIGC 创作工作流 (AIGC 创作工作流 / AIGC Creative Workflow — 以「生成式 AI 视觉创作（图像 + 视频）的工程化创作流水线」为对象的认知操作系统：从「需求/概念（要做什么画面/镜头/风格）→ 模型与工具选型（Midjourney / Stable Diffusion·SDXL·Flux / ComfyUI / 可灵 Kling / 即梦 Jimeng / Runway / Vidu / Sora）→ Prompt 与参数设计（提示词工程、风格描述、负向、种子、CFG/步数/采样器、参考图）→ 可控生成（ControlNet / LoRA / IPAdapter / inpainting / 图生图 / 局部重绘 / 区域控制）→ 批量出图与筛选 → 精修与合成（放大/超分、修手修脸、PS/AE 后期、抠图合成）→ 图生视频/文生视频（首尾帧、运镜、时长、一致性、对口型）→ 商业交付与迭代复现（参数留存、工作流文件复用、版权与可商用判断）」的完整创作-工程-交付决策链。覆盖 (a) 第一性张力 — **可控性/工程化复现（ControlNet/LoRA/ComfyUI 节点工作流/固定种子与参数/可复现可迭代）⇄ 随机性/抽卡玄学（出图随机、prompt 玄学、抽卡刷图、靠运气）**；**审美/创意/艺术指导主导（人的审美判断、概念设计、构图、镜头语言、艺术总监能力——'模型是地板，审美是天花板'）⇄ 工具/模型/参数主导（堆模型能力、堆参数、堆 LoRA、以为换个大模型就行）**；**开源生态/本地可控（Stable Diffusion / ComfyUI / Flux / civitai / liblib 本地部署可定制可控可商用、显卡门槛）⇄ 闭源商业/易用黑盒（Midjourney / 可灵 / 即梦 / Runway 开箱即用但不可精控、按量付费、黑盒）**；**图像生成（T2I/I2I：Midjourney / SDXL / Flux）⇄ 视频生成（T2V/I2V：可灵 / Runway Gen-3 / Vidu / 即梦 / Sora，2024-2025 前沿从图迁移到视频）**；**生产力/商业量产（替代或增强传统设计/插画/电商/广告/影视分镜流程、降本提效、批量）⇄ 纯艺术创作/作者表达（个人风格、实验性、作品集）**；**效率/批量（一次出几十张筛选、商单走量、自动化 pipeline）⇄ 质量/精控（单张精修、商业可用级细节、修手修脸修穿帮）**。(b) 核心工作流 / pipeline（最标准、最易蒸高质量 + CLI 化）— 单图创作链：概念/参考收集（moodboard/风格定位/竞品）→ 模型工具选型（按可控性/风格/成本/可商用判断 MJ vs SD/Flux vs 国产）→ Prompt 与参数设计（主体/风格/光影/镜头/负向词/CFG/采样器/分辨率/种子）→ 首轮出图与方向筛选 → 可控迭代（ControlNet 控构图/姿态/线稿、LoRA 控风格/角色一致性、图生图重绘、inpainting 局部修）→ 放大超分与精修（高清放大/修手修脸/后期调色合成）→ 交付与参数留存（工作流文件/种子/参数复现）。视频创作链：分镜/脚本→首帧出图（图像链产出关键帧）→图生视频（运镜/时长/首尾帧/动态强度）→多镜头一致性（角色/场景/风格统一）→对口型/配音/配乐→剪辑合成→交付。ComfyUI 工程链：节点工作流搭建（加载器/采样/ControlNet/放大/面部修复节点）→ 参数化与复用→批量队列→工作流分享（civitai/openart workflow json）。(c) 工具栈 — 闭源图像（Midjourney v6/v7、DALL·E 3、Adobe Firefly、Ideogram、Recraft）、开源图像与底座（Stable Diffusion 1.5/SDXL/SD3、Flux.1 dev/schnell（Black Forest Labs）、ComfyUI（节点式）、Automatic1111/Forge WebUI、Fooocus）、可控生成插件（ControlNet、LoRA、IPAdapter、AnimateDiff、inpainting/outpainting、ReActor 换脸、面部修复 CodeFormer/GFPGAN、放大 ESRGAN/SUPIR/Topaz）、模型/资源社区（Hugging Face、Civitai、liblib 哩布、吐司 Tusi、openart）、视频生成（可灵 Kling、即梦 Dreamina/Jimeng、Runway Gen-3/Gen-4、Luma Dream Machine、Pika、Vidu、海螺 Hailuo、Sora、Wan 通义万相、MiniMax）、辅助（Magnific 放大、Krea、Photoshop/After Effects 后期、ChatGPT/Claude 写 prompt、reverse prompt 反推）。(d) 知识正典 — AIGC 创作 canon 横跨论文/工程/社区教程：奠基论文（DDPM、Latent Diffusion/Stable Diffusion（Rombach et al）、DiT、ControlNet（Zhang et al）、LoRA、DreamBooth、Textual Inversion、SDEdit、IP-Adapter、视频扩散 SVD/Sora 技术报告）、官方文档与模型卡（Stability AI、Black Forest Labs Flux、ComfyUI 文档、Midjourney docs、可灵/即梦官方教程、Hugging Face diffusers）、社区教程与工作流（ComfyUI 官方示例、civitai 文章、B站/YouTube 创作者长教程、openart workflow）、prompt 工程资源（Midjourney 风格库、提示词指南）。(e) figures/流派 — 模型/工具创造者（Midjourney David Holz、Stability AI Emad/Robin Rombach、Black Forest Labs（Flux，原 SD 团队）、ComfyUI comfyanonymous、ControlNet 张吕敏 Lvmin Zhang、AUTOMATIC1111、可灵/即梦/快手字节团队）、创作者 KOL 与教育者（Midjourney/SD 头部创作者、ComfyUI 工作流大神、AI 视频创作者、国内 B站/抖音 AIGC 教程作者、独立 AI 艺术家）、行业分析与评测（AI 工具评测、生成式 AI 创作生态观察者）。流派分歧：开源可控派（SD/ComfyUI/Flux 本地精控）vs 闭源易用派（MJ/可灵开箱即用）、工程化派（节点工作流/可复现/参数化）vs 玄学抽卡派（prompt 玄学/堆词/抽卡）、审美主导派（'模型是地板审美是天花板'）vs 工具主导派（堆模型堆参数）、图像派 vs 视频派、艺术创作派 vs 商业生产力派、国产工具派（可灵/即梦/liblib）vs 海外工具派（MJ/Flux/Runway）。(f) 行业话术/黑话 — 出图 / 抽卡 / 刷图 / 炼丹（训练 LoRA）/ 喂图 / 垫图（参考图/图生图）/ 控图 / 锁种子 seed / CFG / 采样器 sampler / 步数 steps / 重绘幅度 denoise / 提示词 prompt / 咒语 / 负向 negative / 权重 / tag / 大模型 checkpoint / 底模 / LoRA / ControlNet / 控线稿/openpose/depth / IPAdapter / inpainting 局部重绘 / outpainting 扩图 / 高清放大 / 超分 / 修手 / 崩手 / AI 味 / 风格 / 角色一致性 / 工作流 workflow / 节点 / 跑图 / 队列 / 文生图 T2I / 图生图 I2I / 文生视频 T2V / 图生视频 I2V / 首帧/尾帧 / 运镜 / 动态强度 / 时长 / 对口型 / 一致性 / 可商用 / 训练集 / 过拟合 / 翻车 / 穿帮。(g) 争议/批判 — 「版权与训练数据」（模型训练用未授权作品、生成图版权归属、可商用风险、洗图/伪原创）、「AI 替代创作者就业」（替代画师/插画师/设计师/摄影、行业冲击与转型）、「AI 味与审美趋同」（生成图同质化、套模板、缺乏原创审美）、「玄学 prompt 神话与割韭菜」（'咒语'神秘化、'7天 AIGC 变现/月入过万'付费课割韭菜、抽卡当能力）、「工具快速迭代与淘汰焦虑」（模型月月换、工作流频繁失效、追新疲劳）、「开源 vs 闭源之争」（可控可商用 vs 易用黑盒）、「质量天花板」（修手修脸穿帮、长视频一致性、可控性仍有限）。(h) 大量隐性创作手艺 + 审美 + 工程 + 沟通软技能（prompt 的描述功底与审美词汇、参数与采样的手感、ControlNet/LoRA 的组合控制经验、节点工作流的搭建与调试、批量筛选的审美眼力、修图合成的后期功底、镜头语言与运镜设计、角色/风格一致性的控制、商业需求的翻译与交付、版权与可商用的判断）是高 tacit、靠大量实操跑图 + 审美积累 + 工程调试经验的核心。水分极高（大量'咒语速成/一键变现/AI 绘画割韭菜'营销

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。

Odoo 19 development knowledge base with 18 specialized guides covering Actions (ir.actions.*, cron jobs, server actions), Controllers (HTTP routing, endpoints, auth types), Data files (XML/CSV records, shortcuts, noupdate), API Decorators (@api.depends, @api.constrains, @api.ondelete, @api.onchange, @api.model, @api.private), SQL Constraints (models.Constraint replacing _sql_constraints), Database Indexes (models.Index), Module development (manifest, wizards, reports), Field types (Char, Text, Monetary, relational fields), Manifest configuration (__manifest__.py, dependencies, asset bundles), Mixins (mail.thread, mail.activity.mixin, mail.alias.mixin, utm.mixin), ORM Model methods (search, CRUD, domain filters, recordsets, CamelCase model naming), Migration scripts (pre/post/end hooks, data migration), OWL frontend components (hooks, services, lifecycle), Performance optimization (N+1 prevention, batch ops, _read_group), QWeb Reports (PDF/HTML, paper formats, barcodes, t-out), Security/ACL (record rules, field permissions, privilege-based groups, @api.private), Testing (TransactionCase, HttpCase, mocking, query count assertions), Transactions (savepoints, UniqueViolation, serialization failures), Translations (i18n, PO files, translatable fields), XML Views (list/form/search, kanban card templates, xpath inheritance, QWeb templates). Use when writing, reviewing, or debugging any Odoo 19 Python or XML code, creating or modifying modules, fixing performance issues, or looking up Odoo 19 API patterns and best practices. Includes CSS/SCSS asset authoring and review for Odoo addons.

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。

Your team has high autonomy — and standups are vague, weekly meetings drift into status reports, and 1:1s produce nothing but "I'm fine." This playbook fixes all three layers at once. What's inside: • Daily Standup system — 3-field format, good sample, 4 COO principles to keep it honest • Weekly Meeting — strict 25-min agenda, Scrum Master rotation, 0–10 pulse check • 1:1 Framework — 9-dimension quantified pulse, 5-group deep question bank, bilateral feedback structure • Diagnostic layer — "stuck-at" detection: info / authority / collaboration / capability / direction • FAQ for 4 common failure modes (dead standups, status-report meetings, "I'm fine" 1:1s, scaling beyond 10 people) 🇨🇳 你的团队自由度很高——但 standup 越来越糊，例会变成汇报，1:1 问不出真话。这套三层 Motivation 操作手册解决的正是这件事：standup 是信息层，例会是对齐层，1:1 是个人层。三件事做好了，motivation 不是管出来的，是长出来的。 🇯🇵 チームの自律性は高いのに、スタンドアップは曖昧で、週次ミーティングは進捗報告になり、1on1では「大丈夫です」しか返ってこない——このプレイブックは、その3層すべてを同時に解決します。 🇰🇷 팀의 자율성은 높은데 스탠드업은 두루뭉술하고, 주간 회의는 진척 보고가 되어버리고, 1:1에서는 진심을 들을 수 없다면 — 이 플레이북이 세 레이어 모두를 동시에 해결합니다. Triggers: "团队 motivation" | "standup 没人写" | "例会变成汇报" | "1:1 问不出真话" | "高自由度团队管理" | "COO 如何管人" | "team motivation" | "standup best practices" | "1on1 framework" | "weekly meeting template" | "チームモチベーション" | "팀 동기부여" | "AI startup team ops" | "remote team alignment" | "engagement下降"

Transforms Claude into a CFA-level financial analyst for specialty finance, regulatory compliance, and industry-specific analysis. Use when performing private credit pricing, insurance reserving, FP&A variance analysis, wealth planning, restructuring recovery analysis, real estate valuation, venture capital analysis, ESG assessment, regulatory capital analysis (Basel III), compliance reporting (MiFID II, GIPS), credit derivatives (CDS, CVA), convertible bond analysis, lease accounting (ASC 842/IFRS 16), pension funding/LDI, sovereign risk assessment, real option valuation, equity research (SOTP, target price), commodity spread trading, treasury operations, PPP infrastructure modelling, onshore fund structuring, offshore fund structuring, transfer pricing analysis (BEPS, Pillar Two), tax treaty optimisation, FATCA/CRS compliance, economic substance analysis, regulatory reporting (AIFMD, Form PF), AML/KYC compliance, crypto/DeFi analysis, municipal bond analysis, structured products, trade finance, fund of funds (J-curve, commitment pacing, manager selection, secondaries), bank analytics (NIM, CAMELS, CECL, deposit beta, loan book), carbon markets (credit pricing, ETS, CBAM, offsets, shadow carbon price), or private wealth (concentrated stock, philanthropic vehicles, wealth transfer, direct indexing, family governance) analysis is required. Pairs with corp-finance-mcp tools for computation.

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Use when developing in packages/agent-core-v2 (the DI × Scope agent engine) — adding or modifying a domain Service, choosing a LifecycleScope, wiring DI dependencies, splitting a domain across scopes, owning or migrating a config section, gating behavior behind an experimental flag, raising coded errors, working on the permission system, writing DI/Scope tests, porting business logic from agent-core (v1) to v2, triaging a main-branch commit against v2, or exposing a v2 domain over server-v2 while keeping the /api/v1 wire contract compatible with released clients. Self-contained guide organized by development stage (orient → design → implement → test → verify) plus align workflows for v1→v2 migration, main-branch commit triage, and server-v2 wire exposure; each file carries the rules, examples, and red lines for its step.

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。

houtu-dependencies enterprise-grade Spring Cloud microservice foundational framework complete usage guide. GroupId is io.github.lujiafa, covering unified response, exception handling, parameter parsing, session authentication, permission control, signature verification, anti-replay, distributed lock, rate limiting, database field encryption, config decryption, access logging, canary routing, weighted load balancing, Feign enhancement, Sentinel circuit breaking, service discovery, Swagger documentation, crypto utilities, HTTP client, monitoring metrics, and other enterprise-grade capabilities. When the project build file (pom.xml or build.gradle/build.gradle.kts) contains io.github.lujiafa or houtu-related dependencies, or when the user mentions houtu or houtu-dependencies, this Skill must be read and code must be generated strictly following framework conventions. Even if the user is simply doing regular Spring Boot development (e.g., writing Controller, Service, Feign), as long as the project includes houtu, the houtu approach must be used instead of native Spring. When the user explicitly wants to use the houtu framework and the project has not yet included it, proactively add the BOM and required Starters; when the project already includes it, proactively identify and import required module dependencies based on business scenarios during coding, naturally integrating framework capabilities to enhance business services based on business semantics, rather than waiting for the user to specify each one.

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。

Replace sentence-by-sentence revised or paraphrased text back into an original paper or document while preserving the original formatting, typography, paragraph settings, citation layout, and document conventions as the source of truth. Use when the user provides revised text that maps back to original sentences, paragraphs, sections, or the whole document, but may also contain clear local sentence merges or splits, and asks to replace it back into the source, keep the original formatting, do sentence-by-sentence replacement, preserve original formatting, or only swap wording without rewriting the document. Only allow minimal edits to the rewritten text when fixing citation formatting, restoring protected original content, restoring the original person reference, or minimally splitting/merging text back into original sentence slots; otherwise do not improve grammar, style, logic, or wording.

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。

Email marketing operations and automation for B2B SaaS. Use this skill for newsletter strategy, email automation sequences, lifecycle campaigns, nurture workflows, onboarding sequences, deliverability optimization, email copywriting, lead scoring, marketing automation platform setup, and cold-email sending infrastructure — secondary sending domains, SPF/DKIM/DMARC, and domain warmup — plus the contact budget: a cross-channel frequency cap across every sender (marketing, sales sequences, customer success, in-app). Also triggers on: email, newsletter, drip campaign, lifecycle, nurture, onboarding sequence, deliverability, email automation, lead scoring, marketing automation, our emails are going to spam, SPF, DKIM, DMARC, sender reputation, sending domain, cold email domains, domain warmup, mailbox setup, blacklist, bounce rate, BIMI, brand logo in inbox, VMC, Verified Mark Certificate, Common Mark Certificate, transactional email, password reset emails going to spam, DMARCbis, RFC 9989, DMARC pct tag, DMARC t=y test mode, DMARC rollout, move DMARC to reject, frequency cap, email fatigue, customers get too many emails, too many messages, sales and marketing both emailing the same person, communication limits, send frequency safeguard, global frequency capping, quiet period, message precedence.

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Use when configuring org-wide platform settings: MFA enforcement, My Domain setup and deployment, session timeout and security settings, password policies, trusted IP ranges (Network Access), and CSP Trusted Sites. Owns these settings as deployable Settings metadata (Security.settings, MyDomain.settings, Company.settings, Language.settings) and the order in which a new org is stood up. Trigger keywords: 'MFA setup', 'My Domain', 'session settings', 'password policy', 'trusted IP ranges', 'CSP trusted sites', 'Security.settings', 'SecuritySettings metadata', 'MyDomainSettings', 'settings-meta.xml', 'enhanced domains', 'deploy org settings', 'new org setup order', 'sessionTimeout enum', 'lockSessionsToIp', 'enableAdminLoginAsAnyUser'. NOT for user-level login restrictions (use user-management), NOT for permission model design (use permission-sets-vs-profiles), NOT for security audit and hardening review (use security/org-hardening-and-baseline-config).

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。

Canonical patterns for writing custom MSBuild targets. Only activate in MSBuild/.NET build context. USE FOR: diagnosing and fixing custom target authoring anti-patterns, reviewing MSBuild target definitions for correctness, diagnosing broken SDK target chains across files (e.g., Directory.Build.targets silently redefining SDK targets), fixing targets that replace CompileDependsOn instead of extending it with $(CompileDependsOn), fixing query targets that return stale results due to Outputs vs Returns misuse, fixing missing Inputs/Outputs causing unnecessary rebuilds, fixing missing FileWrites registration. Covers DependsOnTargets vs BeforeTargets vs AfterTargets, the Build→CoreBuild three-level pattern, hooking into the build pipeline, the $(XxxDependsOn) chain-extension pattern. DO NOT USE FOR: incremental build tuning (use incremental-build), parallelization (use build-parallelism), general anti-patterns (use msbuild-antipatterns), non-MSBuild build systems.

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。

Rory Sutherland's 'psycho-logic' approach to marketing, product, and business problems — solve the human perception, not just the engineering or economic metric. Use when the obvious 'logical' fix is expensive, slow, or already done by every competitor; when the user asks 'how do we make people want X', 'the product/price/funnel is fine but nobody converts', 'how do we stand out', 'is there a cheaper way to fix this', or mentions behavioral economics, nudges, irrational behavior, perception, or 'it doesn't make sense but it works'. This is the ROUTER skill: it diagnoses the problem in human terms, then dispatches to reframing, costly-signaling, counterintuitive-tests, or experience-design. Inspired by Rory Sutherland's book *Alchemy*. 日本語の相談（「商品はいいのに売れない」「値下げしかない？」「競合と差別化できない」）にも使う。中文咨询（“产品很好却卖不动”“只能降价吗？”“无法差异化”）也适用。For a catalogue of individual biases, use marketing-psychology (separate plugin) instead.

.NET advanced testing skills overview and navigation center. Triggered when users ask about "integration testing", "API testing", "containerized testing", "microservice testing", "test framework migration", "Testcontainers", "Aspire testing", and other advanced testing needs. Recommends appropriate sub-skills based on specific requirements, covering integration testing, Testcontainers, Aspire testing, framework upgrades, and other 8 advanced skills. Keywords: integration testing, integration testing, API testing, advanced testing, advanced testing, testcontainers, aspire testing, WebApplicationFactory, TestServer, database test, database testing, EF Core test, MongoDB test, Redis test, Docker test, container testing, microservice test, microservice testing, .NET Aspire, xUnit upgrade, TUnit, framework migration

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。

Phishing campaign execution methodology for authorized red team engagements. Covers end-to-end campaign lifecycle: infrastructure provisioning (GoPhish, SMTP relay configuration, domain acquisition and aging, SPF/DKIM/DMARC alignment), payload delivery vectors (Office macro weaponization, HTA droppers, ISO/IMG container abuse, LNK shortcut hijacking, OneNote embedded payloads, HTML smuggling), email authentication bypass techniques (SPF softfail exploitation, DKIM replay attacks, display name spoofing, homoglyph and cousin domain registration), credential harvesting with MFA bypass (EvilGinx2 transparent proxy, Modlishka session relay, pixel-perfect HTML cloning), spear phishing pretext development informed by OSINT, email security gateway evasion, QR code phishing (quishing), and callback phishing for initial access. Integrates with GoPhish for campaign management, EvilGinx2 for adversary-in-the-middle credential interception, King Phisher for template design, and the Social Engineering Toolkit for paylo...

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。

Spring Bootの変更をビルド、コード解析、テスト、セキュリティ検査で確認するスキル。差分もレビューし、公開・デプロイ前の合否と修正点をまとめます。

• Spring BootのPR前に確認したいとき
• リファクタリングや依存更新後の検証
• テストカバレッジの目標を確認したいとき

Spring Bootの機能追加や不具合修正を、先にテストを書く手順で進めます。単体・Web層・データ保存のテストから結合テスト、カバレッジの確認まで扱います。

• テストからSpring BootのAPIを追加
• 不具合の再発防止テストを書きたいとき
• データの保存と取得を検証したいとき

Claude Codeのファイル編集時に、Planktonで自動整形とコード検査を行う設定を支援するスキル。残った違反の自動修正や再検査、検査ルールの保護も扱います。

• 編集のたびに整形・コード検査したいとき
• 残った検査違反の自動修正を設定
• 検査設定のルール緩和を防ぎたいとき