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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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广告外包公司绩效管理 (广告外包公司绩效管理 (Ad Agency / Marketing-Outsourcing Firm Performance Management) — 广告/营销外包公司(乙方 agency:创意热店/4A/本土广告公司/传媒公司/数字营销代理/MCN/设计工作室/内容外包)的『怎么做绩效管理』职业认知操作系统,面向老板/合伙人、运营总监(COO)、HR/HRBP、项目总监、team lead、以及想搭绩效体系的从业者。覆盖: (a) 第一性张力 — 绩效管理的核心世界观分歧: **创意/知识工作『不可量化』 vs 『必须可考核』(创意难归因、难标准化,硬套 KPI 杀创意;但不考核养懒人——这是本行绩效的元矛盾) ⇄ 结果导向(KPI/业绩/回款) vs 过程导向(OKR/能力/成长) ⇄ 个人绩效 vs 团队协作(广告是集体产物,创意-AE-媒介-设计协作,难拆到个人) ⇄ 强激励(提成制:多劳多得但抢单/短视/不协作/吃老客户) vs 稳定(固定+奖金:公平但大锅饭) ⇄ 规模/流水 vs 利润/人效(低价接单冲规模 vs 保毛利)**; (b) 绩效体系设计 — KPI(关键指标法)/OKR(目标与关键成果)/BSC(平衡计分卡)/360 环评/MBO(目标管理)/KSF(关键成功因子)/积分制/计件提成;考核周期(月/季/年+项目制);绩效面谈、绩效改进 PIP、强制分布(271/活力曲线)争议、绩效结果应用(调薪/晋升/淘汰/奖金); (c) 分岗位绩效(本行难点,各岗量化差异极大) — 创意(文案/美术/创意总监:作品质量、获奖、比稿命中率——最难量化)、AE/客户执行(客户满意度、续约、回款、增购、客诉)、媒介/投放(可量化:ROI/ROAS/CPM/转化——最易量化)、策略/策划(方案通过率、洞察)、设计(产能/返稿率/品质)、项目管理(交付准时率、利润率)、销售/BD(新签、回款、毛利); (d) 项目与客户维度绩效(agency 特有,专业服务公司逻辑) — 项目利润率(收入-人力成本-外部成本)、工时制与人效(billability 可计费工时率/utilization 利用率/realization 实现率,借鉴咨询律所)、人均产值/人均毛利、客户续约率与 LTV、客户集中度风险、比稿投入产出、改稿次数失控; (e) 激励与分配机制 — 提成制(按签单/毛利/回款抽成)、项目奖金分成(如何在创意-AE-媒介间分)、年终奖包、合伙人制/事业合伙人/工作室 studio 制/阿米巴、股权期权、创意人才的非物质激励(署名/获奖/成长); (f) 人效、成本与文化 — 人效比(人力成本占收入比,业内估计通常 40-55%,非普适)、毛利率、加班/血汗工厂文化批判(乙方 996/改稿到凌晨)、人才流失率与保留、招聘与培养、组织架构(客户组 vs 职能组 vs 项目制矩阵); (g) 外包/乙方特有张力对绩效的侵蚀 — 甲乙方权力不对等(甲方压价、无限改稿、比稿白嫖创意、账期长垫款重回款难)、低价内卷与红海、项目制不确定性(旺淡季、大客户依赖)、创意工时不可控如何计入绩效、外包 vs 自建团队的成本对比; (h) 争议/批判 — KPI 量化派 vs 创意不可考核派、提成制养蛊 vs 团队协作、强制分布/末位淘汰是否适合小 agency、工时制(billable hours)被咨询律所验证但绑架员工健康、『向甲方看齐做 OKR』水土不服、绩效主义扼杀创意(索尼绩效主义之殇式批判)、乙方绩效本质是『用考核对冲甲方不确定性』的转嫁; (i) 流派/思想谱系 — 专业服务公司管理派(David Maister《Managing the Professional Service Firm》PSF 圣经:leverage 杠杆/utilization 利用率/billing;借鉴咨询律所会计所) vs agency 专家经营派(David C. Baker《The Business of Expertise》+2Bobs podcast、Blair Enns《Win Without Pitching》《Pricing Creativity》定位与定价) vs OKR/目标管理派(John Doerr《Measure What Matters》、Google/Intel OKR、字节/飞书 OKR 本土化) vs 中式绩效工具派(KSF 薪酬全绩效/积分制/阿米巴/华为绩效体系被广告公司借用) vs 本土 4A/广告公司实战派(省广/蓝色光标/华扬联众/利欧 等上市广告公司管理 + 创意热店工作室制)。不含: 广告投放/信息流/SEM 的技术操作本身、甲方市场部/品牌方内部绩效、通用 HR 招聘薪酬全流程(仅取与绩效相关部分)、纯财务会计做账、MCN 达人孵化运营细节。) Master OS — automated mastery of 广告外包公司绩效管理 (Ad Agency / Marketing-Outsourcing Firm Performance Management) — 广告/营销外包公司(乙方 agency:创意热店/4A/本土广告公司/传媒公司/数字营销代理/MCN/设计工作室/内容外包)的『怎么做绩效管理』职业认知操作系统,面向老板/合伙人、运营总监(COO)、HR/HRBP、项目总监、team lead、以及想搭绩效体系的从业者。覆盖: (a) 第一性张力 — 绩效管理的核心世界观分歧: **创意/知识工作『不可量化』 vs 『必须可考核』(创意难归因、难标准化,硬套 KPI 杀创意;但不考核养懒人——这是本行绩效的元矛盾) ⇄ 结果导向(KPI/业绩/回款) vs 过程导向(OKR/能力/成长) ⇄ 个人绩效 vs 团队协作(广告是集体产物,创意-AE-媒介-设计协作,难拆到个人) ⇄ 强激励(提成制:多劳多得但抢单/短视/不协作/吃老客户) vs 稳定(固定+奖金:公平但大锅饭) ⇄ 规模/流水 vs 利润/人效(低价接单冲规模 vs 保毛利)**; (b) 绩效体系设计 — KPI(关键指标法)/OKR(目标与关键成果)/BSC(平衡计分卡)/360 环评/MBO(目标管理)/KSF(关键成功因子)/积分制/计件提成;考核周期(月/季/年+项目制);绩效面谈、绩效改进 PIP、强制分布(271/活力曲线)争议、绩效结果应用(调薪/晋升/淘汰/奖金); (c) 分岗位绩效(本行难点,各岗量化差异极大) — 创意(文案/美术/创意总监:作品质量、获奖、比稿命中率——最难量化)、AE/客户执行(客户满意度、续约、回款、增购、客诉)、媒介/投放(可量化:ROI/ROAS/CPM/转化——最易量化)、策略/策划(方案通过率、洞察)、设计(产能/返稿率/品质)、项目管理(交付准时率、利润率)、销售/BD(新签、回款、毛利); (d) 项目与客户维度绩效(agency 特有,专业服务公司逻辑) — 项目利润率(收入-人力成本-外部成本)、工时制与人效(billability 可计费工时率/utilization 利用率/realization 实现率,借鉴咨询律所)、人均产值/人均毛利、客户续约率与 LTV、客户集中度风险、比稿投入产出、改稿次数失控; (e) 激励与分配机制 — 提成制(按签单/毛利/回款抽成)、项目奖金分成(如何在创意-AE-媒介间分)、年终奖包、合伙人制/事业合伙人/工作室 studio 制/阿米巴、股权期权、创意人才的非物质激励(署名/获奖/成长); (f) 人效、成本与文化 — 人效比(人力成本占收入比,业内估计通常 40-55%,非普适)、毛利率、加班/血汗工厂文化批判(乙方 996/改稿到凌晨)、人才流失率与保留、招聘与培养、组织架构(客户组 vs 职能组 vs 项目制矩阵); (g) 外包/乙方特有张力对绩效的侵蚀 — 甲乙方权力不对等(甲方压价、无限改稿、比稿白嫖创意、账期长垫款重回款难)、低价内卷与红海、项目制不确定性(旺淡季、大客户依赖)、创意工时不可控如何计入绩效、外包 vs 自建团队的成本对比; (h) 争议/批判 — KPI 量化派 vs 创意不可考核派、提成制养蛊 vs 团队协作、强制分布/末位淘汰是否适合小 agency、工时制(billable hours)被咨询律所验证但绑架员工健康、『向甲方看齐做 OKR』水土不服、绩效主义扼杀创意(索尼绩效主义之殇式批判)、乙方绩效本质是『用考核对冲甲方不确定性』的转嫁; (i) 流派/思想谱系 — 专业服务公司管理派(David Maister《Managing the Professional Service Firm》PSF 圣经:leverage 杠杆/utilization 利用率/billing;借鉴咨询律所会计所) vs agency 专家经营派(David C. Baker《The Business of Expertise》+2Bobs podcast、Blair Enns《Win Without Pitching》《Pricing Creativity》定位与定价) vs OKR/目标管理派(John Doerr《Measure What Matters》、Google/Intel OKR、字节/飞书 OKR 本土化) vs 中式绩效工具派(KSF 薪酬全绩效/积分制/阿米巴/华为绩效体系被广告公司借用) vs 本土 4A/广告公司实战派(省广/蓝色光标/华扬联众/利欧 等上市广告公司管理 +

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Binding-resolved Python symbol queries via pyright — every true caller (--refs), the real definition (--def), or an inferred signature (--hover) of a .py symbol, excluding the same-named false positives text search cannot tell apart. Use when a task needs ALL callers or users of a named Python symbol and grep would over-match. Python only, and only for the caller/definition question: measured 2026-07-04 on real issue-localization tasks, plain ripgrep tied or beat the semantic and indexed-regex tiers at 4-60x less wall-clock, so those tiers survive below without being a default.

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Draws an activity flow / system architecture diagram as ONE self-contained, INTERACTIVE HTML+SVG page (2-pane app layout — left: diagram, default fit-to-view with zoom/pan; right: sidebar with explainer cards), styled like a real hand-drawn enterprise doc (Stripe/AWS-docs style — real brand logos for MongoDB/Odoo/n8n/etc. from a bundled Simple Icons library, neutral white boxes, sans-serif, no "AI neon" look), scroll-to-zoom, auto light/dark with a toggle button — clicking a flow (a legend chip, a line, or a step badge) highlights that flow with traveling dots along the arrows in their direction, a ping at the destination box, and a sidebar panel that slides out, while everything else dims; two-way arrows (OUT = solid blue / RETURN = dashed green) carry numbered step badges, colored by semantic meaning (red = auth checkpoint, purple = scheduled flow, amber = data pipeline), a small "hop" arc where two lines are forced to cross, dashed group regions, one explainer card per flow in the right sidebar. Use this skill ANY TIME the user wants to "draw a diagram", "draw a flow", "draw a system diagram", "redraw this so it's easier to follow", "interactive diagram", "two-way diagram", "request/response flow", "make a free-form HTML diagram" — even if they never say the word HTML. Especially good when both the request direction AND the response direction need to be shown, or when a templated tool (archify, mermaid) is too rigid. Do NOT use for data charts (bar/line/pie), and do not use when the user asks specifically for archify or mermaid.

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Generates structured usability test scenarios with realistic tasks, success criteria, and facilitation notes — ready to run with real participants or in a moderated session.

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。

Google SEO APIs: Search Console (Search Analytics, URL Inspection, Sitemaps), PageSpeed Insights v5, CrUX field data with 25-week history, Indexing API v3, and GA4 organic traffic. Provides real Google field data for Core Web Vitals, indexation status, search performance, and organic traffic trends. Use when user says "search console", "GSC", "PageSpeed", "CrUX", "field data", "indexing API", "GA4 organic", "URL inspection", "google api setup", "real CWV data", "impressions", "clicks", "CTR", "position data", "LCP", "INP", "CLS", "FCP", "TTFB", or "Lighthouse scores".

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When the user wants to create sales collateral, pitch decks, one-pagers, objection handling docs, or demo scripts. Also use when the user mentions 'sales deck,' 'pitch deck,' 'one-pager,' 'leave-behind,' 'objection handling,' 'deal-specific ROI analysis,' 'demo script,' 'talk track,' 'sales playbook,' 'proposal template,' 'buyer persona card,' 'help my sales team,' 'sales materials,' or 'what should I give my sales reps.' Use this for any document or asset that helps a sales team close deals. For competitor comparison pages and battle cards, see competitors. For marketing website copy, see copywriting. For cold outreach emails, see cold-email.

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。

Full website SEO audit with parallel subagent delegation. Crawls up to 500 pages, detects business type, delegates to up to 15 specialists (8 always + 7 conditional), generates health score. Use when user says audit, full SEO check, analyze my site, or website health check.

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Full-site blog health assessment scanning all blog files for quality scores, orphan pages, topic cannibalization, stale content, and AI citation readiness. Spawns parallel subagents for comprehensive analysis. Produces per-post scores and a prioritized action queue. Use when user says "audit blog", "blog audit", "site audit", "blog health", "audit all posts", "check all blogs".

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Applies the reasoning of Pieter Abbeel, robotics and reinforcement learning expert, UC Berkeley professor, and co-founder of Covariant. Use this skill whenever you are designing AI systems, tackling Sim2Real transfer, deploying machine learning in the physical world, or evaluating reinforcement learning architectures. Trigger this skill for questions about domain randomization, reward design, bootstrapping real-world AI, robotics hardware assumptions, or shifting from hard-coded rules to data-driven deep learning. It helps ground theoretical AI in physical embodiment and pragmatic deployment.

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Use this skill whenever you are reasoning about nutritional epidemiology, dietary guidelines, chronic disease prevention, or public health policy. It channels the thinking of Meir J. Stampfer, epidemiologist and nutrition expert at Harvard University. Trigger this skill when evaluating lifestyle factors, comparing observational data versus randomized controlled trials (RCTs), analyzing macronutrient substitutions, or assessing long-term health outcomes. Stampfer's approach is essential when users ask about "good" vs. "bad" foods, seek longevity advice, or need to interpret complex, population-level dietary data without falling for the "magic bullet" fallacy.

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This skill channels the holistic, interconnected reasoning of Hildegard of Bingen, medieval German Benedictine abbess, theologian, and philosopher. Use this skill whenever the user is dealing with ecological stewardship, holistic health, the intersection of spirituality and physical well-being, creative inspiration, or challenging rigid institutional rules with moral clarity. Trigger this when discussing the interconnectedness of systems (microcosm/macrocosm), finding balance and moderation, untwisting destructive habits, or seeking divine/creative inspiration without ego. Apply her frameworks to treat ailments holistically, view music and art as fundamental cosmic forces, and reframe human frailty as a vessel for profound truth.

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。

Applies the strategic frameworks and mental models of Anne Wojcicki, co-founder and CEO of 23andMe. Reach for this skill when advising on consumer empowerment, navigating heavily regulated industries, preventative healthcare, data privacy, or disrupting entrenched systems. Use this when the user is dealing with misaligned industry incentives, facing regulatory hurdles, building a consumer-research flywheel, or managing a team through a post-launch 'trough of sorrow'. Apply her principles of 'speaking with data', treating users as partners, and pushing for decade-long systemic change from the outside.

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Use this skill to reason like JoAnn E. Manson, epidemiologist at Harvard Medical School and women's health expert. Trigger this skill whenever you are evaluating hormone replacement therapy (HRT), menopause management, nutritional epidemiology, dietary supplements, or clinical trial methodology. Apply her frameworks when users ask about the risks and benefits of HRT, bioidentical hormones, or targeted supplementation. Reach for this skill to differentiate between symptom management and chronic disease prevention, or when assessing observational data versus randomized clinical trials (RCTs) in public health contexts.

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。

npm package maintenance and lifecycle management for monorepo extensions. Covers health audits, dependency sync, version bumping, changelog updates, pre-publish checks, coordinated publishing, and post-publish verification. **Triggers — use this skill when:** - User asks to "publish", "release", or "version bump" extensions/packages - User says "audit packages", "check package health", "dependency sync" - User asks to "update changelogs", "prepare release", "pre-publish check" - User mentions "npm publish", "version management", "package lifecycle" - User wants to "sync dependencies" or "align versions" across packages - User asks "what needs publishing" or "which packages changed" **Covers:** Monorepo with multiple npm packages under `extensions/`. Each package has its own package.json, CHANGELOG.md, README.md, and version. Packages are scoped (e.g. `@e9n/pi-*`) and published individually.

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Generates a Claude Code configuration tailored to a specific project. Use whenever the user wants to prime a project, set up claude for a repo, bootstrap claude config, or re-prime/refresh an already-primed project (for example after `/prime-sync` pulled new starter content). Triggers on 'prime', 'prime this project', 'optimus-prime', 're-prime', 'refresh claude config', 'regenerate CLAUDE.md', 'set up claude for this repo'. Deeply analyzes the real codebase and builds project-specific skills, rules, and CLAUDE.md — not generic boilerplate. For ongoing config health checks and proposal review, use `self-evolve` instead.

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Use when working with Unreal's Gameplay Ability System - when GAS is worth it, where the AbilitySystemComponent lives and how it is initialised on server and client, AttributeSets with correct clamping and replication, GameplayEffects and execution calculations as data, abilities with commit/end, gameplay tags, prediction, and testing.

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Use when writing Unreal Engine 5 C++ - the gameplay framework classes and where logic belongs, UObject/AActor/UActorComponent lifecycle, UPROPERTY/UFUNCTION and GC-safe pointers (TObjectPtr, TWeakObjectPtr), subsystems, the C++/Blueprint boundary, Enhanced Input, tick discipline, and compiling from the command line.

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PostgreSQL database administration — backup/restore (pg_dump, PITR, WAL archiving), health monitoring (connections, bloat, cache hit ratio, dead tuples), connection pooling (PgBouncer), replication (streaming, logical), vacuum/autovacuum tuning, and scheduled backups with retention. Use when user asks to backup a database, restore from backup, monitor database health, set up replication, or perform DBA tasks. Do NOT use for schema design (use db-sculptor), query optimization (use db-sculptor), or migration planning (use db-sculptor).

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Comprehensive customer success and retention expertise combining onboarding, health scoring, churn prevention, retention strategies, and LTV maximization. Use when designing customer onboarding flows, reducing churn, building health scores, creating retention systems, designing upsells, or improving customer lifetime value. Covers proactive engagement, cancel flows, dunning, re-engagement, and the full customer lifecycle.

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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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Prepare a complete client review meeting prep document for a financial advisor, for whatever timeframe the review covers (quarterly, annual, ad hoc). Given a household name or email and a timeframe, pulls CRM context (Salesforce/Redtail/Wealthbox), portfolio data (Orion/Addepar/Envestnet-Tamarac), estate & tax status (Wealth.com), meeting notes (Zocks or CRM), recent correspondence (Gmail/Outlook), and the household's plan snapshot (MoneyGuide), then assembles a meeting-ready prep doc with agenda, talking points, and action items. Triggers on "pre meeting", "/pre-meeting", "prep for [client]", "quarterly review prep", "get me ready for my meeting with [client]", or "client meeting tomorrow".

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Prepare a meeting-ready estate and tax brief for a household — opens with what was discussed and actioned since the last meeting (Wealthbox/Redtail/Salesforce), pulls the estate plan from Wealth.com (structure, key documents and whether each is signed, balance sheet) plus the prior-year tax look-back and that year's tax constants, and cross-checks it against how accounts are titled and beneficiaried at the custodian and portfolio platforms (Schwab, Orion/Addepar, iCapital) — surfacing unfunded trusts, titling mismatches, and beneficiary gaps as a prioritized flags list. The only write is creating advisor-approved follow-up tasks in the CRM (optional, approved first). Never drafts trust language, gives legal advice, retitles an account, or changes a beneficiary. Triggers on "estate brief", "estate meeting brief", "estate and tax brief", "estate plan review for [client]", "check [client]'s trust funding", "are [client]'s beneficiaries up to date", or "does [client]'s estate plan match their accounts".

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Full repository maintenance pipeline that orchestrates all project agents in sequence - code review, accessibility, bundle size, security, API stability, test coverage, ecosystem, and release preparation. Acts as a quality gate before publishing or merging major changes. Use when: maintenance, health check, pre-release audit, quality gate, full pipeline, repo health, pre-merge check, release readiness, audit all.

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