Databases Skills

Set up or check claude-mem cloud sync with cmem.ai Pro. Use when the user says "set up cloud sync", "sync my memories", "cmem pro", "cloud backup", "sync status", or wants their memory database backed up or synced to their cmem.ai account.

Search claude-mem's persistent cross-session memory database. Use when user asks "did we already solve this?", "how did we do X last time?", or needs work from previous sessions.

Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.

Profile a ClickHouse query using the sampling query profiler and system.trace_log. Use when the user wants to find CPU hotspots, analyze where time is spent in a query, or investigate performance bottlenecks.

Generate PR descriptions for ClickHouse/ClickHouse that match maintainer expectations. Use when creating or updating PR descriptions.

Bisect a ClickHouse regression using pre-built master binaries from CI. Use when the user wants to find the commit that introduced a bug.

Audit open "flaky test" GitHub issues and close those whose tests are no longer failing on master. Cross-references CI history from play.clickhouse.com with git log to attribute fixes.

Edit an auto-generated ClickHouse release changelog into the form that gets committed to CHANGELOG.md. Use when the user has the output of `utils/changelog/changelog.py` and wants it cleaned up and re-categorized for a release.

Show a report of open ClickHouse PRs whose only non-green CI check is "CH Inc sync" (or that are fully green) — i.e. effectively ready to merge. Groups by your authored PRs, PRs assigned to you (authored by others), and PRs by tracked authors (default groeneai). Shows the CH Inc sync state and whether each PR was ever approved; excludes already-merged PRs. Use when asked for "good PRs", merge-ready PRs, or PRs blocked only on the sync job.

Check whether ClickHouse's supported versions (last 3 majors + latest LTS) have recent stable patch releases, diagnose why the scheduled AutoReleases pipeline failed, and identify which releases must be created manually. Use when asked "are the patch releases up to date", "why did autorelease fail", "which releases are missing", "did a release get skipped", during the bi-weekly release-health check, or when investigating create_release.yml / auto_releases.yml failures. Reproduces the full investigation: supported versions from SECURITY.md, per-version staleness, classification of the last N days of AutoReleases/CreateRelease failures (version-bump-PR guard vs missing release-maker runner vs other), the Slack cross-check that reveals the blocking PR, and gated remediation (close a stale robot bump PR, dispatch CreateRelease for a missing version).

Extract the inner ELF from a ClickHouse self-extracting `clickhouse` binary, including when its architecture differs from the host (e.g. to load an aarch64 CI core dump on an x86 workstation). Use when gdb/lldb needs real symbols from a downloaded CI/release binary, or when self-extraction by running the binary is not possible because of an architecture mismatch.

Investigate a ClickHouse CI failure end-to-end from a PR or S3 report URL. Fetches the failed tests and their output, classifies each as flaky vs a real regression using play.clickhouse.com master history, and for every failure searches for both an existing tracking GitHub issue and an existing fix (open/merged PR) — reporting, per failure, whether an issue still needs to be created and whether a fix exists with its status (WIP, merged, already in this branch or not). Downloads and reads the harness artifacts only for failures that history does not explain, and reports a root-cause hypothesis. Read-only first pass — never commits, pushes, or edits.

Evaluate ClickHouse performance test results from existing CI/dashboard data or local perf.py runs. Use to check PR performance changes, run local performance tests, compare with history, assess flakiness, inspect coverage/PR relevance, and summarize whether a result is actionable.

Analyze CI performance comparison reports for a ClickHouse PR. Lists all regressions and improvements, cross-references with master history to distinguish real changes from flaky tests.

Build read models and projections from event streams. Use when implementing CQRS read sides, building materialized views, or optimizing query performance in event-sourced systems.

Use this skill when designing or reviewing a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features

Master SQL query optimization, indexing strategies, and EXPLAIN analysis to dramatically improve database performance and eliminate slow queries. Use when debugging slow queries, designing database schemas, or optimizing application performance.

Execute database migrations across ORMs and platforms with zero-downtime strategies, data transformation, and rollback procedures. Use when migrating databases, changing schemas, performing data transformations, or implementing zero-downtime deployment strategies.

Build production-ready Node.js backend services with Express/Fastify, implementing middleware patterns, error handling, authentication, database integration, and API design best practices. Use when creating Node.js servers, REST APIs, GraphQL backends, or microservices architectures.

Discovers all projects in a .NET solution, classifies each for Oracle-to-PostgreSQL migration eligibility, and produces a persistent master migration plan. Use when starting a multi-project Oracle-to-PostgreSQL migration, creating a migration inventory, or assessing which .NET projects contain Oracle dependencies.

Creates structured bug reports for defects found during Oracle-to-PostgreSQL migration. Use when documenting behavioral differences between Oracle and PostgreSQL as actionable bug reports with severity, root cause, and remediation steps.

Creates integration test cases for .NET data access artifacts during Oracle-to-PostgreSQL database migrations. Generates DB-agnostic xUnit tests with deterministic seed data that validate behavior consistency across both database systems. Use when creating integration tests for a migrated project, generating test coverage for data access layers, or writing Oracle-to-PostgreSQL migration validation tests.

Generate D2 database diagrams from Entity Framework Core models. USE FOR: EF Core database diagram, Entity Framework Core ERD, DbContext diagram, C# entity relationship diagram, PostgreSQL schema visualization, generate .d2 file from EF Core entities, Fluent API mapping diagram, migrations-based database diagram, table relationships, owned types, many-to-many join tables, indexes and constraints. DO NOT USE FOR: runtime debugging, database migration execution, schema deployment, SQL performance tuning, or draw.io diagrams.

Migrates Oracle PL/SQL stored procedures to PostgreSQL PL/pgSQL. Translates Oracle-specific syntax, preserves method signatures and type-anchored parameters, leverages orafce where appropriate, and applies explicit collation mapping (`COLLATE "C"` only when appropriate, locale collations when required). Use when converting Oracle stored procedures or functions to PostgreSQL equivalents during a database migration.

Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.

Query documented public database APIs with explicit endpoints, filters, pagination, and provenance. Use when a scientific, regulatory, financial, or other database-backed fact must be retrieved reproducibly from a named source rather than inferred from general knowledge.

Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST/BLAT, viral sequence downloads, AlphaFold structures, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.

Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological data including genes, drugs, diseases, phenotypes, and more.

MUST USE when reviewing ClickHouse schemas, queries, or configurations. Contains 28 rules that MUST be checked before providing recommendations. Always read relevant rule files and cite specific rules in responses.

| Analyze Langfuse Cloud infrastructure cost structure using Metabase cost marts. Use when asked about cloud spend, AWS versus ClickHouse cost splits, cost drivers by provider/service/usage type/account, daily cost per tracing event, infra cost dashboards, or cost regressions visible in Metabase.

Build or review Langfuse backend code. Use for tRPC routers, public REST APIs, BullMQ processors, services, middleware, Prisma or ClickHouse access, OpenTelemetry, Zod, environment configuration, or backend tests.

| Seed reproducible local Langfuse data in ClickHouse and Postgres. Use for complex traces, long sessions, v3/v4 events, bulk list data, or frontend rendering and performance tests; never use ad hoc scripts or raw inserts.

Inspect and explain conversations in the local Deep Agents Code SQLite session store. Use as a fallback when LangSmith trace tooling is unavailable, for offline or untraced sessions, or when asked to identify or summarize a local dcode thread, inspect checkpoint metadata, list recent local threads, or parse ~/.deepagents/.state/sessions.db and a thread UUID or prefix.

Query the authoritative global Herdr Review and Work Manager inventory, recommend actionable PR work, and explicitly materialize confirmed remote-only Review Manager records through the plugin's stable public CLI.

Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.

QA an analysis before sharing -- methodology, accuracy, and bias checks. Use when reviewing an analysis before a stakeholder presentation, spot-checking calculations and aggregation logic, verifying a SQL query's results look right, or assessing whether conclusions are actually supported by the data.

Write optimized SQL for your dialect with best practices. Use when translating a natural-language data need into SQL, building a multi-CTE query with joins and aggregations, optimizing a query against a large partitioned table, or getting dialect-specific syntax for Snowflake, BigQuery, Postgres, etc.

Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.

Pre-deployment verification checklist. Use when about to ship a release, deploying a change with database migrations or feature flags, verifying CI status and approvals before going to production, or documenting rollback triggers ahead of time.

Use when adding, updating, or removing a `redislabs/client-libs-test` image tag used by the test stack — covers every place a Redis version → image-tag mapping lives so local `make test` and GitHub CI stay in sync.

Use when writing a commit message or opening a PR in the go-redis repo — covers the Conventional-Commit format, the repo's scope vocabulary (pool, conn, sentinel, command, …), breaking-change syntax, and the rule that NO AI-attribution trailer is added.

Use when adding a new Redis command (or RediSearch / TimeSeries / VectorSet / module subcommand) to go-redis — covers fetching the command spec and docs, the Cmder type, Cmdable interface wiring, RESP parsing, tests, and the custom-vet rule that enforces SetVal.

Use when preparing a go-redis release — cutting a new version, bumping version.go, updating the go-redis dependency version in submodule go.mod files, or writing the RELEASE-NOTES.md entry for a new vX.Y.Z. Covers choosing the next semver, finding the last release and gathering merged PRs since then, the release-notes format and what to exclude, running scripts/release.sh to bump versions, and verifying with the scripts/tag.sh dry-run. Does NOT create tags, push, or commit — publishing stays a manual maintainer step.

Bitwarden database architecture, migrations, and dual-ORM strategy. Use when working with `.sql` files, stored procedures, EF migrations, or database schema changes. Also use when deciding whether a change needs both Dapper and EF Core implementations, or whether a breaking stored-procedure change requires `_V2` versioning.

Read-only exploration of a local Bitwarden development database — answer business questions from live data, verify seeded fixtures, and introspect schema. Use whenever the user wants to query, count, look up, verify, or explore data in a local Bitwarden database ("how many orgs/users/ciphers", "show me collections", "check what the seeder created", "look up user X", "which orgs have feature Y"), even without the word SQL. Not for authoring stored procedures, migrations, or repository code (use writing-database-queries), and not for seeding or modifying data.

Implementing Entity Framework Core repositories and migrations for PostgreSQL, MySQL, and SQLite at Bitwarden. Use when creating or modifying EF repositories, generating EF migrations, or working with non-MSSQL data access in the server repo. Also use when editing `EntityTypeConfiguration<T>` classes or debugging provider-specific LINQ translation issues.

End-to-end smoke test for the public Errors HTTP API (error groups). Seeds failed runs into ClickHouse so the error materialized views populate, then drives the real endpoints against the running webapp — list (with filters + pagination), retrieve, resolve/ignore/unresolve, the `filter[error]` runs filter, user attribution via the `trigger.dev mint-token` -> JWT exchange, and the 401/403/404 negatives. Use for "smoke test the errors API", "test the errors API e2e", "prove the errors endpoints work", or to re-verify after changes.

Use when adding, modifying, or debugging OTel span timeline events in the trace view. Covers event structure, ClickHouse storage constraints, rendering in SpanTimeline component, admin visibility, and the step-by-step process for adding new events.