3K skills · sorted by relevance
Manage shared iii configuration with JSON Schema, a definition of allowed value structures. Covers validated settings, automatic change notifications, YAML edits, temporary overrides, and default initialization that preserves existing values.
Build and update Convex backend code, including schemas, queries, authentication, storage, and scheduled jobs. Apply input and output validation, indexed data access, and deployment checks to verify the implementation.
Build custom calculation and parsing tools that an n8n AI Agent can call. This skill covers JavaScript or Python inputs, structured arguments, string results, tool descriptions, and fixes for invocation and output errors.
Inspect JSON files or API responses with jq and produce a compact map of keys and value types. Use the result to understand nested data before writing queries or processing the full dataset.
Implement structured telemetry, such as counters and events, from DOCA applications to external consumers. Guides publishing API selection, schema and source setup, build and test workflows, and debugging lifecycle errors or reports that never reach the receiver.
Workflow skill for KiCAD schematic design via MCP tools. Triggers on: "design a circuit", "add a component", "wire up", "connect pins", "build schematic", "place resistor", "place cap", "place IC", "schematic", "add symbol", "net label", "power rail".
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Governs Event Schema Sets, the Fabric registries of event types and message payload schemas. Covers create, rename, update and delete of a definition, and listing, property reads or base64 decoding. Every Event Schema Set task belongs here. Ingestion pipelines are eventstream-cli.
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Document and type a Sentry API endpoint. Write or fix @extend_schema decorators, specify response TypedDicts, type request parameters, correct type drift between the declared schema and the runtime response, and validate the generated spec. Use when asked to "document an endpoint", "add OpenAPI docs", "add/fix @extend_schema", "type an endpoint response", "fix the response type", "fix type drift", "reuse a response type", "split an overloaded endpoint", "specify the response schema", "add a TypedDict response", "migrate a legacy api-docs path", "fix a parameter type", or "make an endpoint public" / "promote an endpoint" (promotion is one section here).
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Generate Drizzle ORM schemas for Cloudflare D1 databases with correct D1-specific patterns. Produces schema files, migration commands, type exports, and DATABASE_SCHEMA.md documentation. Handles D1 quirks: foreign keys always enforced, no native BOOLEAN/DATETIME types, 100 bound parameter limit, JSON stored as TEXT. Use when creating a new database, adding tables, or scaffolding a D1 data layer.
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Plans and scripts data migrations between SQL databases: schema mapping, type conversion, batched and resumable loading, validation and cutover. Use when the user mentions "data migration," "database migration," "migrate from MySQL to PostgreSQL," "schema migration," "ETL pipeline," "data transfer," "database consolidation," "legacy migration," or "move data between databases." Covers schema analysis, mapping, transformation, batch processing, validation, and cutover planning. For query optimization during migration, see sql-optimizer.
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Expert at evolving database schemas safely. Covers migration strategies, zero-downtime deployments, rollback planning, and data transformation. Knows how to change production schemas without breaking running applications. Use when "database migration, schema change, add column, alter table, drizzle migrate, prisma migrate, zero downtime migration, rollback migration, database, migrations, schema, drizzle, prisma, postgresql, zero-downtime" mentioned.
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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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Expert guide for agent-native structured outputs, JSON Schema strict mode enforcement (OpenAI response_format, Gemini schema mode, Claude tool_use), MCP resource schemas, and zero-hallucination type contracts / Panduan ahli kontrak output terstruktur native agen dan penegakan JSON schema ketat.
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🇺🇸 SEO & GEO Dual-Engine Playbook 2026 — Rank on Google AND get cited by AI search (ChatGPT, Perplexity, Claude, Gemini). Battle-tested from AFFiNE (60k GitHub stars in 24 months) and 150+ AI startup consultations. E-E-A-T writing voice system, keyword funnel strategy, JSON-LD schema templates, comparison page SOP, IndexNow setup, Generative Engine Optimization content patterns. **Optimized for dev tools / OSS / B2B SaaS. 2C products (education, apps, games): see 2c-adaptation guide in references/.** 🇨🇳 SEO & GEO 双引擎增长手册 —— 同时拿下 Google 排名和 AI 搜索引用（ChatGPT、Perplexity、Claude、文心一言）的完整方法论。AFFiNE 60k stars 与 150+ AI 创业公司咨询实战验证。E-E-A-T 写作声音系统、关键词漏斗、JSON-LD 结构化数据、竞品对比页 SOP、IndexNow 推送、生成式引擎优化内容模式。**默认适用于开发者工具 / 开源项目 / B2B SaaS，2C 产品（教育/应用/游戏）请参考 references/2c-adaptation.md。** 🇯🇵 SEO & GEO デュアルエンジングロースプレイブック — Google検索とAI検索（ChatGPT、Perplexity、Claude）の両方で評価される手法。AFFiNE 60k starsと150社以上のAIスタートアップコンサルティングで検証。E-E-A-Tライティング、キーワードファネル、Schema.org構造化データ、比較ページSOP。**デフォルトは開発者ツール／OSS／B2B SaaS向け。2C製品（教育・アプリ・ゲーム）はreferences/2c-adaptation.mdを参照。** 🇰🇷 SEO & GEO 듀얼 엔진 성장 플레이북 — 구글 검색과 AI 검색(ChatGPT, Perplexity, Claude) 양쪽 모두에서 인용되는 방법론. AFFiNE 60k 스타와 150+ AI 스타트업 컨설팅으로 검증. E-E-A-T 글쓰기, 키워드 퍼널, Schema.org 구조화 데이터, 비교 페이지 SOP. **기본값은 개발자 도구/OSS/B2B SaaS용. 2C 제품(교육·앱·게임)은 references/2c-adaptation.md 참조.** Triggers: "SEO" | "GEO" | "Generative Engine Optimization" | "AI search optimization" | "ChatGPT SEO" | "Perplexity SEO" | "Claude SEO" | "search optimization" | "content SEO" | "technical SEO" | "schema markup" | "JSON-LD" | "structured data" | "IndexNow" | "E-E-A-T" | "keyword strategy" | "keyword funnel" | "comparison page" | "programmatic SEO" | "SaaS SEO" | "搜索优化" | "AI 搜索" | "内容 SEO" | "GEO 优化" | "生成式引擎优化"
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Manages Apache Avro schema evolution with compatibility checking via Confluent Schema Registry API. Validates forward, backward, and full compatibility across schema versions automatically.
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Conversational profile builder. Produces validated JSON config files for any rootnode Skill that consumes profile-shaped configuration (handoff trigger check, mode router, critic gate, future Skills). Reads a target JSON Schema (draft-07), conducts a progressive-depth interview using plain-language questions, validates answers, and writes the resulting JSON to a destination path. Schema-agnostic — works with any profile schema conforming to draft-07. Use when a user needs to create, clone, or revise a profile and you do not want them hand-editing JSON. Trigger on: "build me a profile," "create a profile," "I need a profile for X," "set up my profile," "edit my profile," "clone the X profile," "walk me through making a profile." Do NOT use when the user already has a working profile and only wants to invoke the consuming Skill, when the schema cannot be located, or for non-profile JSON config work. Always validate before writing.
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Detect, validate, and generate Schema.org structured data with Ahrefs traffic prioritization. JSON-LD format preferred. Use when user says "schema", "structured data", "rich results", "JSON-LD", "markup", or "schema priority".
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Use when a BA or admin needs to document the Salesforce data model: creating field inventories, object relationship maps, ER diagrams, or analyzing field usage across objects. Triggers: 'data dictionary', 'document our data model', 'object relationship map', 'field inventory', 'ER diagram for Salesforce'. NOT for designing the model or choosing lookup vs master-detail — use data/data-model-design-patterns. NOT for generating the Mermaid or PlantUML diagram — use architect/salesforce-erd-and-diagramming. More trigger keywords: field inventory CSV, describe API, getGlobalDescribe, DescribeFieldResult, sObject describe, schema snapshot, schema drift, blank field descriptions, undocumented fields, field ownership, data dictionary review cadence.
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Render a crouton collection schema (the field-definition JSON) into a human-readable field table + relationships sketch (HTML + PNG + a terse Markdown table), so a human can sign off on the DATA MODEL before any code is generated. Use when an agent drafts or changes a collection schema (before `crouton config` / `generate_collection`), or when asked to "review a schema", "show the data model", "check these fields".
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Verified lookup pack for crouton's schema→CRUD generation domain — the fieldsFile JSON format, the complete field-type catalog with aliases, the naming contract, the exact artifact list one generate run produces, the seed/default-layout pipeline, dialect defaults, and rollback semantics. Use when writing or reviewing a collection schema, when asking "what field types exist / what does `crouton config` actually generate / where did this file name come from / why did I get a pg schema", when a generated file's name or path doesn't match the docs, or when removing/regenerating a collection. Trigger phrases — "what field types are supported", "schema JSON format", "what files does crouton generate", "naming convention for collections", "rollback a collection", "dialect default". The generation WORKFLOW (steps + sign-off gates) lives in the `crouton` skill — this is the reference it doesn't carry.
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Use Exasol JDBC-based virtual schemas for federated read-only access to external databases such as PostgreSQL, Oracle, MySQL, SQL Server, and DB2. Covers database-source adapter selection, `CREATE VIRTUAL SCHEMA`, `ALTER VIRTUAL SCHEMA`, `EXPLAIN VIRTUAL`, metadata refresh, and query-side troubleshooting.
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Schema Therapy according to Jeffrey Young: Schemas, modes, inner child concept, and coping styles — psychoeducationally presented.
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Grundlagen der Schematherapie nach Jeffrey Young: Schemata, Modi, Inneres-Kind-Konzept und Bewältigungsstile — psychoedukativ vermittelt.
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claude-org-runtime のリリース (release-* タスク / vX.Y.Z タグ発行) を、 同 PyPI 発行を受ける claude-org-ja 側の expectation 同期と ペアで設計・委譲・完了させるためのワークフロー。 DEFAULT_NOTIFY 値・classifier vocabulary・role_configs_schema (ja 側ミラー: org_extension_schema)・attention.example.json テンプレ等が 変わるリリースで発動する。 CI cascade を予測・予防し、ja-side の red を回避することを目的とする。
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