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概要と使いどころ
Reviews HealthKit code for authorization patterns, query usage, background delivery, and data type handling. Use when reviewing code with import HealthKit, HKHealthStore, HKSampleQuery, HKObserverQuery, or HKQuantityType.
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Use this skill when answering business analytics, RevOps, GTM metric, pipeline, revenue, funnel, customer, or warehouse questions with Deepline. Triggers on phrases like 'query Snowflake', 'analyze pipeline', 'total ACV', 'break down by quarter', 'use the semantic layer', 'run a semantic query', or any use of snowflake_get_semantic_layer / snowflake_run_semantic_query. Skip prospecting, enrichment, contact finding, outbound, or personalization workflows; use deepline-gtm for those.
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实现 API 调用 / 缓存 / 加载 / 错误处理 / 乐观更新时使用。覆盖 TanStack Query / SWR / RTK Query / Apollo Client / urql / Pinia Colada / Pinia Query。融合 stale-while-revalidate + 乐观更新 + Mutation + 无限加载。
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Anyquery is a SQL query engine that lets you run SQL against 40+ apps, files, and databases including GitHub, Notion, Chrome, and Apple Notes. Built on SQLite with MCP server support for connecting AI agents to structured data across services.
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Regenerate `.sqlx/` cached metadata when adding or modifying `sqlx::query!`, `sqlx::query_as!`, or `sqlx::query_scalar!` macros.
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。
Query the project knowledge base. Wraps the research-librarian agent. The librarian reads the shelf-index, identifies the 2-4 most relevant library files for the question, deep-reads only those, and returns structured evidence with citations. Stateless — reads the index fresh on every query.
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。
State management decision tree for React SPAs: useState/useReducer, Context API, Zustand, Jotai, Redux Toolkit, TanStack Query, SWR. Detect what's installed and apply matching patterns. Use this skill to: - Pick the right state tool for the data shape (local / shared UI / server / form). - Use Context correctly (and know when to switch to Zustand/Jotai). - Implement a Zustand or Jotai store. - Set up TanStack Query for server state with caching, pagination, mutations. - Avoid common mistakes (overusing Redux, prop drilling, forgetting query invalidation). Do NOT use this skill for: - General hook conventions (see react-conventions). - Form state specifically (see react-forms). - Routing state (see react-routing). - Testing stores/queries (see react-testing).
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。
Django ORM patterns: model definitions, field types, model managers, custom QuerySet methods, select_related/prefetch_related for N+1 prevention, transactions, F/Q expressions, Meta indexes and constraints. For django-architect (model definitions) and django-migrations-specialist (field finalization). Activated automatically by django-plugin/stack.md. Use this skill to: - Write clean, efficient Django model definitions with proper field choices and __str__. - Use custom managers and QuerySets to encapsulate query logic. - Prevent N+1 queries with select_related and prefetch_related. - Use transactions for atomic operations. - Define Meta indexes and constraints (for django-migrations-specialist to finalize). Do NOT use this skill for: - Creating actual migrations (makemigrations) — that is django-migrations-specialist. - Web view / API patterns — see django-plugin:django-conventions. - Python idioms — see python-foundation:python-conventions.
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。
Fix node query issues in Grafema tests when nodes have numeric IDs instead of human-readable IDs, or when `type` field is undefined. Use when: (1) queryNodes returns nodes with numeric IDs like "52710336597754872375318185843222727675" instead of semantic IDs like "net:request#__network__", (2) node.type is undefined but node.nodeType has a value, (3) metadata is a JSON string instead of parsed object, (4) tests fail with "Cannot read property 'length' of undefined" after queryNodes. Covers RFDBServerBackend vs RFDBClient distinction and async generator handling.
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。
Recover from stale RFDB nodes that survive `commit_batch` cleanup despite having their `file` field listed in `changed_files`. Use when: (1) re-running an enricher/orchestrator step but old data with the same file field stays in the graph, (2) `commit_batch(changed_files=[X], ...)` reports success but a query later still returns nodes with `file = X`, (3) you re-pointed structural nodes (e.g., DIRECTORY/FILE) from a synthetic file path to real file paths and the old synthetic-path nodes won't go away, (4) graph-stream / find_by_type returns a mix of "stale" and "new" nodes for the same logical entity. Root cause: `handle_commit_batch` calls `engine.find_by_attr({file: X})` to enumerate nodes-to-delete, but that query path returns only a PARTIAL set after compaction (L1 segments and the file→nodes index can desync). Workaround: enumerate stale nodes via the TS rfdb client `queryNodes({file: X})` (different code path that finds all of them) and call `deleteNode(id)` per node, then re-commit the intended state. Related: rfdb-manifest-l1-carryforward (same L1 family).
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。
Connect to and query ClickHouse (a local server or a ClickHouse Cloud service) from the terminal. For ClickHouse Cloud analytics, use the configured direct ClickHouse Query API endpoint with per-user CH_API_KEY and CH_API_SECRET credentials; do not use clickhousectl cloud service query. For local or host/port servers, use clickhousectl local client. Use when the user wants to run SQL against ClickHouse, explore schemas and tables, inspect Cloud services, or authenticate. For building a local dev environment or deploying to Cloud, defer to the official ClickHouse skills (see Scope).
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。
Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, FK replacement code generation, OCC retry patterns, ORM migration (Django/Hibernate/Rails), DDL operations, query plan explainability, SQL compatibility validation, and bulk data loading. Triggers on phrases like: DSQL, Aurora DSQL, distributed SQL database, serverless PostgreSQL-compatible database, migrate to DSQL, DSQL query plan, DSQL EXPLAIN ANALYZE, DSQL ENUM, DSQL foreign key, DSQL OCC retry, DSQL multi-region, DSQL JSONB, DSQL GIN index, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader.
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。
Datadog provider for obz. Supports metrics (Datadog Query Language), logs (Datadog Log Query), and traces. Requires api-key and app-key configured under auth in config.yaml. Does not support metric labels, label-values, or series commands. This skill should be used when the user mentions "Datadog", "DD", "obz -p dd", Datadog Query Language, or needs to configure Datadog provider instances with regional endpoints.
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。
Write Prisma Next queries for Postgres, SQLite, or Mongo — pick a lane (Postgres/SQLite `db.orm.<Model>` + `db.sql.<table>`; Mongo `db.orm.<root>` + `db.query.from(...)` pipeline builder), filter / project / sort / paginate, eager-load with `.include(...)`, Postgres/SQLite `db.transaction(...)`, Postgres/SQLite ORM `.aggregate(...)`, Mongo aggregations via query builder, namespace-aware accessors (`db.orm.<ns>.<Model>`, `db.sql.<ns>.<table>`). Triggers: query, where, match, select, project, orderBy, take, skip, include, lookup, first, all, count, aggregate, group, create, update, delete, upsert, returning, transaction, db.close, script teardown, variant, polymorphism, drizzle-style, kysely-style. Notes: `.all()` is a Thenable (just `await` it), iterators are single-use (`RUNTIME.ITERATOR_CONSUMED`), Postgres `count` is `number` while sum/avg/min/max are `number | null`, ranges use chained `.where()` or `and(...)` (no `.between(...)`).
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。
Read a Prisma Next structured error envelope and route to the right recovery — code, domain, severity, why, fix, meta. Use for error, exception, my emit failed, my query won't typecheck, my query crashed, my migration won't apply, MIGRATION.HASH_MISMATCH, BUDGET.ROWS_EXCEEDED, BUDGET.TIME_EXCEEDED, RUNTIME.ABORTED, PLAN.HASH_MISMATCH, CONTRACT.MARKER_MISSING, PN-RUN-3001, PN-RUN-3002, PN-RUN-3030, PN-MIG-2001, PN-CLI-4011, PN-SCHEMA-0001, drift, capability missing, planner conflict, prisma studio, EXPLAIN, query log, db.end, db.close, script won't exit, hangs, close connection, pool.end, client is closed.
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。
SQL discipline for querying Pulp Perfetto traces (.pftrace) with trace_processor — idempotent CREATE OR REPLACE PERFETTO views, GLOB not LIKE, dur = -1 incomplete-slice handling, EXTRACT_ARG for span args, joining on stable utid/upid, SPAN_JOIN PARTITIONED, and the draft→validate→execute loop. Ships Pulp's CPU/UI trace stdlib plus closed GPU startup, health, and probe views. TRIGGER when writing or debugging SQL over a .pftrace, when `pulp trace query` returns wrong/empty rows, or when the trace-analysis skill needs a query primitive.
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。
Query Google NotebookLM notebooks for source-grounded, citation-backed answers from user-uploaded documents. Manages notebook library, handles Google authentication, and supports smart discovery. Works standalone via /blog notebooklm or internally from blog-write and blog-researcher for source-grounded research context. Falls back gracefully when not configured. Use when user says "notebooklm", "notebook", "query notebook", "ask notebook", "notebook research", "source grounded research", "document query", "notebook library".
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。
Answer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization instead).
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。
Generates Google Cloud Monitoring Server-Driven UI (SDUI) Widget and XyChart Protocol Buffer textprotos from resolved PromQL or ListTimeSeries queries. Use when: - Generating valid google.monitoring.dashboard.v1.Widget textprotos, containing PrometheusQuery or TimeSeriesFilter datasets, for use with the Cloud Monitoring Dashboards API, gcloud CLI, or declarative dashboard definitions. - Synthesizing Server-Driven UI (SDUI) widget titles, axis labels, and plot types for Prometheus or ListTimeSeries queries. Don't use for: - Metric discovery or PromQL query generation. For those tasks, use the cloud-monitoring-metric-selection or cloud-monitoring-promql-query skills.
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。
Use OpenClaw's exec tool to inspect orgs, sources, and run bounded read-only SQL through the OneQuery CLI directly.
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。
Run read-only SQL against BigQuery public datasets with local result capture, cost safeguards, and reproducibility outputs.
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。
Architect data fetching with TanStack Query v5, SWR, optimistic updates, prefetching, and cache invalidation strategies. Activate on: React Query, SWR, optimistic updates, cache invalidation, prefetching, stale-while-revalidate, infinite queries. NOT for: REST API design (use api-architect), database queries (use database skills), GraphQL schema (use graphql-expert).
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。
Detect data exfiltration through DNS tunneling by analyzing query entropy, subdomain length, query volume, TXT record abuse, and response payload sizes using passive DNS monitoring.
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。
GraphDB探索エージェント - RyuGraphデータベースを自然言語またはCypherで探索し、関連するコードや仕様書を返却。/query-graph [クエリ] で呼び出し。