Databases Skills

Use this skill when writing or modifying Drizzle ORM schemas, queries, or migrations in this repo — specifically the `@internal/dashboard-agent-db` package (the dashboard agent's conversation datastore). Covers pg-core schema definition, the postgres-js driver, drizzle-kit migrations, and this repo's conventions: a dedicated Postgres schema, foreign-key-free cross-database design, pooler-safe connections, and the access-pattern query layer. Drizzle is NOT the main database — that's Prisma.

>- Manages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB Model Context Protocol (MCP) tools for automated database operations. Use when creating, configuring, or administering AlloyDB databases. Do NOT use for general PostgreSQL instances (e.g. Cloud SQL) or other GCP databases.

>- Generates Logging Query Language (LQL) queries for Google Cloud Logging from natural language. Use this skill when you need to query log data or when you are debugging issues. You can filter log data by Google Cloud service. Don't use this skill to query other databases, such as SQL or Cloud Spanner.

>- This file generates or explains Cloud SQL resources. Use this file when the user asks to create a Cloud SQL instance or database for MySQL, PostgreSQL, or SQL Server. Cloud SQL manages third-party MySQL, PostgreSQL, and SQL Server instances as resources in Cloud SQL. For example, when Cloud SQL creates an open-source MySQL instance, the resulting resource is a Cloud SQL for MySQL instance that Google Cloud manages. Cloud SQL handles backups, high availability, and secure connectivity for relational database workloads.

>- Configures GKE Backup Plans and restore workflows. Use for backup policies, disaster recovery, or GKE cluster restores. Don't use for database backups.

>- Manages GKE storage, including PVCs, PersistentVolumes, Filestore, and GCS FUSE. Use when configuring GKE storage, creating PVCs, or setting up GCS FUSE on GKE. Don't use for database administration or replication strategies outside volume provisioning context.

>- Assists in provisioning instances and databases, designing performant schemas, and querying data in Spanner. Use when designing primary keys, writing SQL queries or client library code, or diagnosing performance issues.

Keep ioredis documentation synchronized with code behavior. Use when an agent changes public APIs, options, Redis command support, examples, generated typings, connection behavior, Cluster/Sentinel behavior, Pub/Sub, pipelines, transactions, scan streams, reconnect behavior, TypeScript usage, or release-facing behavior that may require README or docs updates.

Use when adding or updating Redis command support in ioredis, including command metadata checks, generator inputs under bin/, generated RedisCommander typings, return and argument overrides, functional command tests, optional tsd coverage, and validation.

Verify ioredis code changes before handoff. Use when an agent changes or reviews runtime TypeScript, Redis command support, generated typings, tests, docs tied to behavior, build tooling, release-sensitive files, or any task that needs choosing and running the right local validation commands.

Produce PR-ready output for redis-py — a one-line commit message and a copy-pasteable Markdown PR description (Change summary + Test coverage, 5–6 paragraphs) covering all staged changes plus the commits already on the current branch. Trigger whenever the user's message mentions any of these keywords or phrases: "PR", "pr", "PR description", "PR draft", "pull request", "the PR block", "commit msg", "commit message", or otherwise asks for a commit message or PR write-up for their local changes.

Review a GitHub issue or pull request URL as a redis-py maintainer, with a staged assessment of whether the claim is real, practically important, already solvable with supported functionality, correctly scoped, better served by another design, and worth maintainer and contributor effort. Use when assessing issue validity or severity, deciding whether an issue should be prioritized or closed, determining whether a requested feature represents an unmet need rather than a discoverability or usage gap, judging whether a PR is worth bringing to mergeable quality, comparing open PRs or alternative designs, separating code quality from repository readiness, or drafting a concise maintainer assessment. When closure, additional evidence, or code changes should be requested, also produce a polite, concise, complete, copy-paste-ready maintainer comment.

Review a GitHub issue or pull request URL as a redis-py maintainer, with a staged assessment of whether the claim is real, practically important, already solvable with supported functionality, correctly scoped, better served by another design, and worth maintainer and contributor effort. Use when assessing issue validity or severity, deciding whether an issue should be prioritized or closed, determining whether a requested feature represents an unmet need rather than a discoverability or usage gap, judging whether a PR is worth bringing to mergeable quality, comparing open PRs or alternative designs, separating code quality from repository readiness, or drafting a concise maintainer assessment. When closure, additional evidence, or code changes should be requested, also produce a polite, concise, complete, copy-paste-ready maintainer comment.

Produce PR-ready output for redis-py — a one-line commit message and a copy-pasteable Markdown PR description (Change summary + Test coverage, 5–6 paragraphs) covering all staged changes plus the commits already on the current branch. Trigger whenever the user's message mentions any of these keywords or phrases: "PR", "pr", "PR description", "PR draft", "pull request", "the PR block", "commit msg", "commit message", or otherwise asks for a commit message or PR write-up for their local changes.

Generate a redis-py GitHub release-notes draft and save it as a Markdown file. Collects the merged PRs/commits since the previous release, maps each commit to its PR labels, categorizes them into the project's release sections (Breaking Changes, Deprecations, Experimental Features, New Features, Bug Fixes, Maintenance) using the skill's pr-labels-guide, infers a category for unlabeled PRs, and assembles the notes with an optional Highlights section and a contributors footer. Trigger this skill whenever the user says "release notes", "create release notes", "generate release notes", "draft release notes", "prepare release notes", "changelog", or asks for the release notes/changelog of a specific version or branch (e.g. "release notes for 8.0", "generate release notes for the 8.0 branch", "what changed since the last release"). A phrase like "release notes for 8.0" means: use branch `8.0` as the release-branch input. Also use when asked to summarize what changed since the last tag.

Generate a redis-py GitHub release-notes draft and save it as a Markdown file. Collects the merged PRs/commits since the previous release, maps each commit to its PR labels, categorizes them into the project's release sections (Breaking Changes, Deprecations, Experimental Features, New Features, Bug Fixes, Maintenance) using the skill's pr-labels-guide, infers a category for unlabeled PRs, and assembles the notes with an optional Highlights section and a contributors footer. Trigger this skill whenever the user says "release notes", "create release notes", "generate release notes", "draft release notes", "prepare release notes", "changelog", or asks for the release notes/changelog of a specific version or branch (e.g. "release notes for 8.0", "generate release notes for the 8.0 branch", "what changed since the last release"). A phrase like "release notes for 8.0" means: use branch `8.0` as the release-branch input. Also use when asked to summarize what changed since the last tag.

Add or extend Redis commands in the Jedis client API — a new core command, a family of new commands, an extension to an existing command's options, or a module command (Search/TimeSeries/JSON/Bloom). Gathers evidence (Redis server PR, HLD document), plans the full implementation matrix in plan mode, then implements with unit and integration tests following Jedis maintainer conventions.

Optimizes database queries and improves performance across PostgreSQL and MySQL systems. Use when investigating slow queries, analyzing execution plans, or optimizing database performance. Invoke for index design, query rewrites, configuration tuning, partitioning strategies, lock contention resolution.

Use when building, configuring, or debugging enterprise Java applications with Spring Boot 3.x, microservices, or reactive programming. Invoke to implement WebFlux endpoints, optimize JPA queries and database performance, configure Spring Security with OAuth2/JWT, or resolve authentication issues and async processing challenges in cloud-native Spring applications.

Use when optimizing PostgreSQL queries, configuring replication, or implementing advanced database features. Invoke for EXPLAIN analysis, JSONB operations, extension usage, VACUUM tuning, performance monitoring.

Optimizes SQL queries, designs database schemas, and troubleshoots performance issues. Use when a user asks why their query is slow, needs help writing complex joins or aggregations, mentions database performance issues, or wants to design or migrate a schema. Invoke for complex queries, window functions, CTEs, indexing strategies, query plan analysis, covering index creation, recursive queries, EXPLAIN/ANALYZE interpretation, before/after query benchmarking, or migrating queries between database dialects (PostgreSQL, MySQL, SQL Server, Oracle).

Use when building real-time communication systems with WebSockets or Socket.IO. Invoke for bidirectional messaging, horizontal scaling with Redis, presence tracking, room management.

Claim tasks, record step progress, and verify SOP gates in the colony SQLite queue. Applies when your spawn message includes a db_path field.

Use when you need state across calls — building env vars, navigating with cd, driving REPLs (python -i, mysql, psql, node), or responding to interactive prompts (sudo password, ssh host-key confirmation, mysql connection). Teaches the prompt-sentinel exec pattern (default mode), raw I/O for REPLs (raw_send=True then read_only=True), the one-in-flight-per-session rule, and the close-or-leak-against-the-cap discipline. Bash on macOS — never zsh; explicit shell=/bin/zsh is rejected. Read before calling terminal_pty_open.

Answer an explicitly vault-scoped question from an Obsidian wiki without changing it. Use when the user selects the vault as the evidence source: query the wiki, query quick, query deep, explain from the wiki, summarize the vault, find in wiki, search the wiki, or based on the wiki. Do not route ordinary general-knowledge questions here.

> Ask questions against an open investigation log — what witnesses said, where accounts conflict, what gaps exist, what the strongest evidence is on each issue. Use when the attorney needs to query the investigation record without re-reading every entry.

Use when creating, modifying, debugging, deploying, or testing Redis Insight Workbench visualization plugins, plugin manifests, package.json visualizations, activationMethod functions, redisinsight-plugin-sdk usage, Parcel/Vite plugin builds, iframe rendering, Redis command parsing, Docker RedisInsight deployment, /api/plugins verification, or Playwright plugin validation.

>- Scans a codebase for security vulnerabilities using CodeQL's interprocedural data flow and taint tracking analysis. Triggers on "run codeql", "codeql scan", "codeql analysis", "build codeql database", or "find vulnerabilities with codeql". Supports "run all" (security-and-quality + security-experimental suites) and "important only" (high-precision security findings) scan modes. Also handles creating data extension models and processing CodeQL SARIF output.

Help developers build third-party tools that import, inspect, migrate, or analyze DeepChat data. Use when Codex needs to work with DeepChat provider configuration, model configuration, MCP/app settings, sessions, messages, legacy chat data, `agent.db`, `chat.db`, SQLCipher encrypted SQLite, Electron safeStorage wrapped passwords, Tauri importers, or native macOS/Windows/Linux data access.

Use when adding a new Redis command — or a new overload/variant of one — to the Lettuce client. Covers the full flow: writing the command specification, designing the Lettuce API, adding argument types and the API template, running the interface generators, adding the builder/async/reactive/Kotlin implementations, and adding unit + integration tests. Trigger on requests like "add support for the <X> command", "implement <REDIS COMMAND> in Lettuce", "wire up a new command", or adding a new argument/overload to an existing command.

Use this skill for any Kusto query or telemetry investigation specifically related to Fluid Framework or its partners. Triggers include: writing or running a Kusto query against the Office Fluid database, investigating Fluid Framework telemetry or error rates, querying Office_Fluid_FluidRuntime_* tables, looking up a Fluid session by Session_Id or docId, investigating a Fluid-related error in Loop or Whiteboard telemetry, monitoring an FF bump or partner ring deployment, checking Fluid render reliability or Scriptor errors, or when the user mentions Fluid-specific tables (Office_Fluid_FluidRuntime_*, OwhLoads, HostTracker, Scriptor) or Fluid-specific error types (dataCorruptionError, dataProcessingError, DeltaConnectionFailureToConnect, ICE, ACE). Do NOT trigger for general Kusto questions that are not related to Fluid Framework.

Postgres performance optimization and best practices from Supabase. Use this skill when writing, reviewing, or optimizing Postgres queries, schema designs, or database configurations.

Choose and apply automatic layout strategies for node-link diagrams and connected-node visuals. Use when the user asks how to auto-arrange nodes, reduce line crossings, route edges, avoid overlaps, stabilize layout, or choose graph-layout algorithms for network diagrams, dependency graphs, database schema diagrams, ERDs, state machines, decision trees, flow diagrams, box-and-line editors, or other line-connected nodes.

Design, critique, read, write, render, and implement UML and UML-like software diagrams. Use when the user mentions UML, sequence diagrams, class diagrams, activity diagrams, state machines, use case diagrams, component diagrams, deployment diagrams, object diagrams, package diagrams, profile diagrams, timing diagrams, communication diagrams, interaction overview diagrams, composite structure diagrams, ERDs, database schema diagrams, C4, BPMN, swimlanes, flowcharts, network diagrams, application architecture diagrams, software architecture diagrams, diagram-as-code, model-as-code, XMI, UMLDI, PlantUML, Mermaid, Graphviz DOT, D2, Structurizr, DBML, diagrams.net/draw.io, Kroki, or interactive diagram editors and explorers.

Submit compact AlphaFold Protein Structure Database API requests for prediction, UniProt summary, sequence summary, and annotation lookups. Use when a user wants AlphaFold metadata or concise structure summaries

Optimize Entity Framework Core queries by fixing N+1 problems, choosing correct tracking modes, using compiled queries, and avoiding common performance traps. Use when EF Core queries are slow, generating excessive SQL, or causing high database load.

Query GitHub issues with jq filtering and reusable selectors.

Query GitHub discussions with jq filtering and reusable selectors.

统一管理多智能体角色的团队协作框架，支持智能体动态组合、灵活协作和扩展新角色。智能体本质上是"角色定义"，可以根据任务需求灵活组建团队，实现从会议决策到系统构建的完整能力。智能体角色明确分工：有干活的、有指挥的、有挑毛病的，能实时看到沟通过程，共享数据库记忆，确保上下文一致。

Interactive system flow tracing across CODE, API, AUTH, DATA, NETWORK layers with SQLite persistence and Mermaid export. Use for security audits, compliance documentation, flow tracing, feature ideation, brainstorming, debugging, architecture reviews, or incident post-mortems. Triggers on audit, trace flow, document flow, security review, debug flow, brainstorm, architecture review, post-mortem, incident review.

Use when investigating slow queries, analyzing execution plans, or optimizing database performance. Invoke for index design, query rewrites, configuration tuning, partitioning strategies, lock contention resolution.

Explore SQLite and PostgreSQL databases: list tables, inspect schemas (columns/types/constraints), preview data, generate Mermaid ER diagrams, and run safe read-only queries. Triggered by requests to explore a database, view table structures, describe tables, generate diagrams, or query data, and by keywords like database exploration, schema, ER diagram, or SQL query.

Translate natural language to SQL, optimize query performance, and interpret EXPLAIN plans for SQLite and PostgreSQL. Triggered when users ask to convert questions into SQL, improve slow queries, tune indexes, analyze execution plans, or mention keywords like NL2SQL, query tuning, or full table scan.

This skill should be used when the user asks to "automate SQL injection testing," "enumerate database structure," "extract database credentials using sqlmap," "dump tables and columns from a vulnerable database," or "perform automated database penetration testing." It provides comprehensive guidance for using SQLMap to detect and exploit SQL injection vulnerabilities.

>- Adds support for a new Redis command to the gem from a specification. Triggers on requests like "add support for the TS.BGET command" or via /add-new-command <COMMAND|path>. Resolves the spec from command_specs/, falling back to official redis.io docs. See .claude/skills/add-new-command/examples/command-specification-template.md.

Query the memory system for relevant learnings from past sessions

Choose the right search tool for each query type

Use this skill when the user mentions "pinme", or needs to upload files, store to IPFS, create/publish/deploy websites or full-stack services (including frontend pages, backend APIs, database storage, email sending, etc.), or any feature requiring backend database/server support.