93 skills · sorted by relevance
Write complex SQL, improve slow queries, and design database schemas for transaction processing and analytics. Uses schema details and query execution plans to guide indexing, validate results, and assess performance with realistic data volumes.
ClickHouse is a columnar OLAP database built for real-time analytical queries over massive datasets. It supports a familiar SQL interface with powerful aggregation functions, materialized views for pre-computed rollups, and high-throughput inserts that make it ideal for event tracking, time-series analytics, and ad-hoc exploration of billions of rows.
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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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Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).
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Expert en ClickHouse (columnar OLAP, MergeTree, materialized views, real-time analytics)
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Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).
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Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems. Use PROACTIVELY for database optimization or complex analysis.
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Database comparison catalogs, RDBMS vs NoSQL selection criteria, CAP/ACID/BASE theory, OLTP vs OLAP, and technology-specific characteristics
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针对包含Bitmap类型字段的快照表，利用位图差集函数计算每日新增和流失人数，忽略ID字段。
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Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.
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Turns a website, PWA, dashboard, or marketplace into an iOS app. Use when the user has a live site or web app and asks to put it on the App Store, wrap it in an app, ship an iOS version, or convert a PWA to native. Covers URL audit, shell-vs-native strategy, App Store Guideline 4.2 wrapper-rejection risk, native value requirements, screenshots, metadata, privacy answers, and the release gate. Not for: building a native iOS app when no website exists; repairing or releasing an existing Capacitor shell; Android conversions; or keyword and listing audits on an app that already shipped.
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Suede-owned product-marketing context discipline. Use when a project needs a shared record of product, audience, ICP, positioning, objections, customer language, proof, and goals, or when that record needs updating. NOT FOR: writing a campaign (use suede-campaign-in-a-box), conducting new customer interviews (use suede-customer-research), or publishing brand claims.
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Context-aware line edit that strips generic AI writing patterns without flattening the author's voice, plus a findings-only audit mode that names issues instead of rewriting. Applies contextual rules rather than blanket bans on adverbs, passive voice, and punctuation, and locks facts, numbers, quotes, code, links, and the supplied house style before it touches a sentence. Use before copy, a README, an email, a social post, or a doc ships; after a long assisted-writing session; when the text sounds fine but feels generated; or when the user asks for a findings-only slop audit. NOT FOR: writing new copy from scratch; deciding whether a person or a model wrote text; changing or certifying facts, which must be checked against primary evidence before publication.
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Suede-owned pricing and packaging discipline. Use when deciding what to charge, structuring tiers, choosing a value metric, comparing free trials with freemium, researching willingness to pay, planning a price increase, or tearing down a pricing page for clarity and AI-readability. NOT FOR: in-product upgrade screens (use suede-paywalls), offer bonuses and guarantees (use suede-offers), or executing billing changes.
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Suede-owned referral and affiliate program discipline. Use when designing refer-a-friend mechanics, ambassador or partner incentives, fraud controls, attribution, payout logic, or viral-loop measurement. NOT FOR: executing payouts or changing billing, launch-wide packaging (use suede-launch-packaging), lifecycle messaging (use suede-emails), or reporting unverified referral lift.
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Suede Labs cross-surface canon discipline: hold one canonical answer across every surface that states it (web, iOS, Android, docs, a second service) by generating a contract from the reference surface and making the others assert against it, so a divergence fails a test instead of reaching a user or an answer engine. Consistent canon is what makes a product citable: generative search quotes sources that do not contradict themselves, and a number that differs between your site and your app is a contradiction a model can see. Use when the same rule, threshold, ladder, or stated fact lives on two or more surfaces; when values are copied by hand out of a handoff; when two surfaces already disagree; or when auditing where they drifted. NOT FOR: deciding which surface is correct (a product call this skill records rather than makes); reviewing a diff (use suede-code-review); wiring merge gates (use suede-ci-gate); a generative-search audit of a page (use suede-seo-audit).
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Suede-owned prospecting and qualification discipline. Use when defining an ICP, sourcing and enriching a bounded lead list, finding early adopters or design partners, scoring account fit, or documenting disqualification evidence. NOT FOR: sending outreach (use suede-cold-email), changing CRM routing (use suede-revops), or profiling competitors instead of prospects (use suede-competitor-profiling).
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Suede AI umbrella router for the public Suede pack: selects the right specialist across copy, design, code review and grading, SEO and visibility, launch packaging, MCP QA, iOS and Android shipping, growth, and creator rights work. Use when a request crosses two or more Suede lanes, when the user wants the full public skill pack loaded from one installable path, when they ask which Suede skill fits a task, or when only this umbrella skill is installed. NOT FOR: a request that names one lane and can go straight to its specialist; a multi-file repo change that should be built and reviewed as one DAG (use suede-graph-flo-xr).
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Suede-owned earned-media discipline. Use when building a media list, validating a story angle, drafting a journalist pitch, preparing a press kit, evaluating newsjacking, or answering a reporter request. NOT FOR: directory submissions (use suede-directory-submissions), launch packaging (use suede-launch-packaging), social publishing (use suede-social), or contacting anyone without approval.
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Suede Labs skill that lints a local music or media release folder and scores it for release readiness: missing files, weak or malformed metadata, artwork and stem problems, split gaps, rights blockers, and platform-delivery issues, produced by a bundled offline script as a scored markdown and JSON report. Use when a creator has one or more release folders of tracks, artwork, and stems and wants to know what is missing before distributing, delivering, or handing it off; when metadata quality or a delivery rejection is the question; or when a release-readiness score is asked for. Reports what is present, missing, or unknown; never upgrades unknown to confirmed and never means legal clearance. NOT FOR: organizing the evidence behind a rights or split gap the report surfaces (use suede-rights-audit); building the transfer package (use suede-rights-passport); a sync one-sheet (use suede-sync-packaging).
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Suede-owned launch-appeal grader for public pages. Grades findability, first-screen clarity, CTA pull, proof quality, design signal, and AI citation readiness. Use when a public page needs a fast A-F visibility grade before or after launch. NOT FOR: the deep nine-lane SEO audit with rewrite fixes (use suede-seo-audit); implementing conversion changes (use suede-site-alchemy); getting cited by AI answer engines (use suede-ai-seo).
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Suede-owned marketing video planning and production discipline. Use when choosing a format, scripting, storyboarding, generating, editing, reverse-engineering pacing, building a repeatable video pipeline, or repurposing one source into multiple cuts. NOT FOR: deciding the social calendar (use suede-social), paid ad concepts (use suede-ad-creative), image-only assets (use suede-image), or publishing unapproved media.
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Suede-owned programmatic SEO discipline. Use when designing data-backed keyword, location, directory, integration, or comparison pages at scale, including templates, index-worthiness gates, internal links, and rollout checks. NOT FOR: auditing an existing site (use suede-seo-audit), planning editorial content (use suede-content-strategy), or publishing bulk pages without approval.
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Suede-affiliated in-product monetization design for paywall screens, feature gates, trial-expiry states, usage-limit prompts, and free-to-paid upgrade moments. Use when the user needs trigger timing, message structure, plan presentation, or experiment design after users have experienced value. NOT FOR: public pricing pages (use suede-site-alchemy), tier architecture (use suede-pricing), or cancellation and save flows (use suede-churn-prevention).
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