「sourcing」の検索結果

Apache Pekko + Scala 3によるCQRS/Event Sourcing実装ガイド。 PersistenceEffectorを用いた集約アクター、ドメインモデルとアクターの分離、 状態遷移の型安全な表現、イベント設計、Protocol Buffersシリアライズ、 ZIOベースのユースケース層、リードモデルアップデータの実装パターンを提供する。 対象言語: Scala 3限定。CQRS/Event Sourcingアーキテクチャが前提の場合のみ使用。 トリガー条件: 「Scala」かつ「CQRS」または「Event Sourcing」または「Pekko」が リクエストに含まれる場合のみ起動。Scala以外の言語やCQRS/ES以外のアーキテクチャでは このスキルを使用してはならない。 トリガー：「PekkoでCQRS/ESを実装したい」「Scalaで集約アクターを書きたい」 「PersistenceEffectorの使い方」「Pekkoのイベントソーシング」 「ScalaでCQRSのコマンド側を実装」「Pekkoで状態遷移を管理」 といったPekko + Scala + CQRS/ES実装リクエストで起動。 非トリガー：「CQRSのトレードオフ」「イベントソーシングとは」「JavaでCQRS」 「GoでEvent Sourcing」「CQRSの概念を教えて」など、Scala/Pekko以外や概念的な質問では 起動してはならない。

Run a Busabase-backed sandbox desk for scoring and triaging course-instructor ("讲师") candidates found on a recruiting-style platform. Use whenever the user invokes $kelly-instructor-sourcing or /kelly-instructor-sourcing, wants a lightweight instructor candidate tracker, needs to score sourcing-platform leads against a three-axis rubric (background endorsement, expertise depth/breadth, teaching-service ability) before reaching out, or wants to log a WeChat add without building a live-connected outreach tool. Three subcommands - setup, criteria, review - plus the desk itself. v1 is sandbox-only — every dataset is deterministic mock data, it never connects to a real sourcing-platform or messaging account, and it never sends a message or automates a WeChat add - that stays entirely human-performed outside the app.

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Discover and evaluate potential A-share investment opportunities for China-focused private equity and investment strategies. Adapted from the original deal-sourcing skill for Chinese market structure, data sources, and business practices. Triggers on "A股项目挖掘", "投资项目发现", "deal sourcing China", "find A-share deals", "sourcing opportunities China", or "discover investment targets".

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Query 1688 product bestseller billboard data for sourcing discovery and wholesale product research. Trigger when the user mentions 1688 product rankings, 1688 bestseller lists, wholesale hot products, domestic sourcing rankings, dropshipping product selection, 1688 trending products, bulk purchase popular items, supplier product rankings, 1688 billboard, 1688 bestsellers, sourcing rankings, wholesale hot products, trending product rankings, supplier rankings, 1688 trends. Even if the user does not explicitly mention "1688 billboard", if their need involves discovering hot-selling wholesale products or sourcing opportunities on the 1688 platform, this skill should also be triggered.

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Search and analyze products on the Chinese 1688 wholesale platform (Alibaba domestic B2B market) for sourcing, supplier discovery, and product selection. Trigger when the user mentions 1688 product search, finding suppliers on 1688, wholesale product queries, factory sourcing, dropshipping supplier search, 1688 keyword product selection, wholesale price comparison, filtering by sales volume, any product research on the 1688 platform, 1688 search, 1688 product selection, find suppliers, factory lookup, wholesale pricing, supplier search, domestic sourcing, 1688 products. Even if the user does not explicitly say "1688", if their need involves searching for wholesale products, finding domestic suppliers, or sourcing from the domestic Chinese market, this skill should also be triggered.

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Build a candidate sourcing strategy and outreach pipeline. Use when the user says "where do I find candidates for this role", "build a sourcing plan", "write a cold outreach message", "we're not getting enough applicants", "our pipeline is dry", "help me find passive candidates", "write a LinkedIn message", or wants to increase the volume or quality of candidates in the funnel - even if they don't explicitly say "sourcing strategy". Also use when a recruiter needs to go beyond job postings to find talent.

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- СОРСИНГ ПОД РЕЙЗ в два трека: инвесторы из нашего VC-графа (leads.db) и покупатели-пилоты, найденные ПО СИМПТОМУ в интернете (GitHub issues, Hacker News, X, наши же репозитории, клуб СОСТАВ). Триггеры: “/raise-sourcing“, “/sourcing“, “собери список инвесторов“, “кому питчить“, “найди покупателей“, “добери лидов под рейз“, “кого звать в пилот“, “обнови стол сорсинга“, “raise sourcing“

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PE deal sourcing workflow — discover target companies, check CRM for existing relationships, and draft personalized founder outreach emails. Use when sourcing new deals, prospecting companies in a sector, or reaching out to founders. Triggers on "find companies", "source deals", "draft founder email", "check if we've seen this company", or "outreach to founder".

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Daily outbound-sourcing sweep for {{greenhouse_role}}. Searches LinkedIn for candidates matching {{sourcing_criteria}}, dedupes every match against the role's Greenhouse pipeline, and drafts a personalized outreach email per new candidate to {{recruiter_review_inbox}} — capped at {{daily_cap}} per run. Never sends outreach and never writes to Greenhouse; the recruiter reviews, sends, and owns the ATS.

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Panel structure design, firm selection criteria, right-sourcing analysis, and coverage gap assessment for in-house legal teams. Build panel frameworks from scratch or formalise existing informal arrangements. Define weighted selection criteria for firm evaluation. Run right-sourcing analysis (ABC tiering) to identify work sent to expensive providers that doesn't require that level of resource. Assess existing panels for practice area, geographic, and capability gaps. Trigger on: 'design our panel', 'how many firms do we need', 'panel structure', 'firm selection criteria', 'which firms should we use', 'right-sourcing', 'are we sending work to the right firms', 'panel gaps', 'too many firms', 'consolidate our panel', 'we need a firm for', 'preferred provider', 'panel tiers', 'do we need BigLaw for this', 'convergence', 'D&I panel targets', 'how do we choose firms'.

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Event sourcing and CQRS expert for AI memory systemsUse when "event sourcing, event store, cqrs, nats jetstream, kafka events, event projection, replay events, event schema, event-sourcing, cqrs, nats, kafka, projections, event-driven, memory-architecture, ml-memory" mentioned.

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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。

Audit a supply chain compliance system for ethical sourcing and labor rights. Triggers: building or reviewing supply chain compliance platforms, ESG reporting systems, or textile/garment sourcing tools.

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。

CQRS pattern, event stores, projections, eventual consistency. Activate on: CQRS, event sourcing, event store, read model, projection, aggregate, domain event, command handler. NOT for: message broker setup (use event-driven-architecture-expert), database optimization (use data-warehouse-optimizer).

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Especialista em event sourcing, CQRS e padrões de arquitetura orientada por eventos. Domina design de event store, construção de projeções, orquestração de sagas e padrões de consistência eventual. Use PROATIVAMENTE para sistemas baseados em event sourcing, requisitos de trilha de auditoria ou modelagem de domínio complexa com consultas temporais.

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Converts a job description into a structured candidate-sourcing workflow. Use when the user provides a role and wants candidate criteria, search terms, source recommendations, and a screening process. Must avoid sourcing on sensitive personal attributes.

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Event Sourcing knowledge base. Provides patterns, antipatterns, and PHP-specific guidelines for Event Sourcing architecture audits.

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。

CQRS pattern, event stores, projections, eventual consistency. Activate on: CQRS, event sourcing, event store, read model, projection, aggregate, domain event, command handler. NOT for: message broker setup (use event-driven-architecture-expert), database optimization (use data-warehouse-optimizer).

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Buys well — specifying need, running competitive sourcing, negotiating, and category strategy before a contract exists. Use this to run an RFP or vendor selection, negotiate a purchase, consolidate spend across a category, decide between single and multiple suppliers, or bring uncontrolled spending under management.

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Expert in event sourcing, CQRS, and event-driven architecture patterns. Masters event store design, projection building, saga orchestration, and eventual consistency patterns. Use PROACTIVELY for event-sourced systems, audit trails, or temporal queries.

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Keep citations honest in business documents — every load-bearing claim sourced, links that actually contain the claim, the as-of dates that keep numbers honest, and the internal-vs-external sourcing rules for decks and memos. Use when asked check the sourcing in this deck, add citations to this doc, our slide says 'studies show' — which studies, or set citation norms for the team. Produces the claim-by-claim audit, the fixes (source found, claim softened, or cut), the citation format for the venue, and the team norm card.

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BOM (Bill of Materials) management for electronics projects — the workflow skill that coordinates DigiKey, Mouser, LCSC, element14, JLCPCB, PCBWay, and KiCad skills around a unified BOM lifecycle. Create, update, and maintain BOMs with part numbers, costs, quantities stored as KiCad symbol properties. ALWAYS trigger this skill for any task involving component sourcing, pricing, ordering, distributor searches, BOM export, or fabrication preparation — even if the user names a specific distributor or fab house (e.g. "search DigiKey for...", "generate JLCPCB BOM", "order from Mouser"). This skill analyzes the schematic for sourcing gaps, recommends which distributor/fab skills to call for each gap, and writes results back as symbol properties — the agent (or user) performs the actual searches via the called skills. Also trigger on phrases like "what parts do I need", "order components", "how much will this cost", "export for JLCPCB", "find parts for this board", "compare pricing", or "check stock".

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。

BOM (Bill of Materials) management for electronics projects — the workflow skill that coordinates DigiKey, Mouser, LCSC, element14, JLCPCB, PCBWay, and KiCad skills around a unified BOM lifecycle. Create, update, and maintain BOMs with part numbers, costs, quantities stored as KiCad symbol properties. ALWAYS trigger this skill for any task involving component sourcing, pricing, ordering, distributor searches, BOM export, or fabrication preparation — even if the user names a specific distributor or fab house (e.g. "search DigiKey for...", "generate JLCPCB BOM", "order from Mouser"). This skill analyzes the schematic for sourcing gaps, recommends which distributor/fab skills to call for each gap, and writes results back as symbol properties — the agent (or user) performs the actual searches via the called skills. Also trigger on phrases like "what parts do I need", "order components", "how much will this cost", "export for JLCPCB", "find parts for this board", "compare pricing", or "check stock".

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。

United Arab Emirates Governance, Risk & Compliance advisor — a jurisdiction-first compliance router. In the UAE, WHERE an organization sits determines its law: mainland (Federal PDPL, Decree-Law 45/2021 — executive regulations still pending), DIFC (DP Law No. 5 of 2020 as amended 2025, with a private right of action), ADGM (DP Regulations 2021), CBUAE-licensed financial institutions (consumer-data residency, outsourcing approvals), healthcare (ICT Health Law data localization), and government/CNI (UAE IA Regulation, Cyber Security Council; Dubai ISR, ADHICS). Use for any UAE / Dubai / Abu Dhabi / Emirates compliance question: UAE data protection, DIFC or ADGM privacy, free-zone vs mainland obligations, health-data residency, CBUAE cyber and outsourcing rules, market entry ("expanding to the UAE"), breach notification, gap assessments, and mapping UAE requirements to ISO 27001 / NIST CSF / SOC 2. Trigger for any UAE privacy, cybersecurity, or regulatory question even if no framework is named.

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。