数据库
数据库管理与查询
cmux-backend
manaflow-ai
"Backend TypeScript and Cloud VM development rules for cmux. Use when editing web/app/api, web/services, backend scripts, Cloud VM lifecycle, provider integrations, Postgres, Stack Auth pricing gates, migrations, or provider image build scripts."
ce-product-pulse
EveryInc
"Generate time-windowed product pulse reports from configured signals."
azure-cosmos-ts
microsoft
Azure Cosmos DB JavaScript/TypeScript SDK (@azure/cosmos) for data plane operations. Use for CRUD operations on documents, queries, bulk operations, and container management. Triggers: "Cosmos DB", "@azure/cosmos", "CosmosClient", "document CRUD", "NoSQL queries", "bulk operations", "partition key", "container.items".
azure-cosmos-py
microsoft
Azure Cosmos DB SDK for Python (NoSQL API). Use for document CRUD, queries, containers, and globally distributed data. Triggers: "cosmos db", "CosmosClient", "container", "document", "NoSQL", "partition key".
azure-cosmos-java
microsoft
Azure Cosmos DB SDK for Java. NoSQL database operations with global distribution, multi-model support, and reactive patterns. Triggers: "CosmosClient java", "CosmosAsyncClient", "cosmos database java", "cosmosdb java", "document database java".
analyzing-disk-image-with-autopsy
mukul975
Perform comprehensive forensic analysis of disk images using Autopsy to recover files, examine artifacts, and
code-review
langwatch
"Project-level code review: check changed files against LangWatch codebase rules (IDs, multitenancy, layering, naming, SRP)."
planning
open-metadata
Use when starting a non-trivial feature, refactor, or multi-file change. Forces structured design thinking before writing any code - brainstorm approaches, get approval, then create a step-by-step implementation plan.
dynamodb
itsmostafa
AWS DynamoDB NoSQL database for scalable data storage. Use when designing table schemas, writing queries, configuring indexes, managing capacity, implementing single-table design, or troubleshooting performance issues.
data-table-manager
n8n-io
Load before calling data-tables or parse-file. Use for natural standalone requests like "what data tables do I have?", "show/list my tables", or "what columns are in this table?", and whenever the user asks to list, show, create, inspect, import, seed, query, update, clean up, rename columns in, or delete data tables and rows, especially from CSV/XLSX/JSON attachments. Also load before building or planning workflows that create or write to Data Tables (then load workflow-builder before build-workflow).
n8n:public-api
n8n-io
Adds, migrates, or updates n8n Public API v1 endpoints with @PublicApiController — public DTOs, API-key and RBAC scopes, cursor pagination, OpenAPI + coverage wiring, and tests. Use when working under packages/cli/src/public-api/v1/ or when exposing an existing service through /api/v1.
n8n:db-migrations
n8n-io
Authors n8n database migrations. Use when creating or modifying files under packages/@n8n/db/src/migrations/, when the user asks to add a column, table, index, foreign key, or backfill, or when the user mentions DB migrations or TypeORM migrations.
playwright-migration
zebbern
Step-by-step migration guides for moving to Playwright from Cypress or Selenium/WebDriver — command mappings, architecture changes, and incremental adoption strategies.
database-scout
zebbern
"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."
database-optimizer
zebbern
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.
sql-insight
zebbern
"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."
update-team-server
openclaw
"Update the operator-configured Team server through its canonical owner; trust native validation and acceptance without duplicate checks or schedulers."
chronicle
microsoft
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting session data.
exploring-llm-traces
PostHog
Debug and inspect LLM/AI agent traces using PostHog's MCP tools. Use when the user pastes a trace or session URL (e.g. /ai-observability/traces/<id> or /ai-observability/sessions/<id>), asks to debug a trace, figure out what went wrong, check if an agent used a tool correctly, verify context/files were surfaced, inspect subagent behavior, investigate LLM decisions, or analyze token usage and costs. Also use when raw SQL/HogQL against events.properties.$ai_input / $ai_output_choices returns empty — message content lives only on the dedicated posthog.ai_events table.
querying-tophog
PostHog
Query tophog — the ingestion pipeline's heavy-hitter store in ClickHouse — to identify hot or expensive actors (team_id, distinct_id, session_id, partition) during incident triage. Use when investigating ingestion lag, a hot or lagging Kafka partition, expensive person processing, merge storms, or any "which team or distinct_id is causing this" question. Covers the internal Metabase access path (SSO via hogli), the tophog schema, and the cost-vs-volume query lens. Internal-only: results contain cross-customer identifiers.
modeling-activation-metrics
PostHog
Build reusable activation models — an activation-rate metric and a per-user/per-account activated flag — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to define, model, or measure activation, the "aha moment", onboarding success, or which early actions predict a user sticking around. The core idea this skill enforces: activation is NOT a single assumed event — it is a retention-validated combination of early actions, chosen by balancing reach (enough users hit it) against predictive power (those who hit it retain much better). Covers finding candidate actions, validating them against retention lift, count thresholds and action combinations, per-product and B2B group-level activation, and modeling the winning definition as a durable activated-flag + activation-rate model. Read modeling-warehouse-foundations first; composes modeling-product-usage-metrics for the retention validation.
exploring-llm-costs
PostHog
Investigate LLM spend in PostHog — total cost over time, cost by model, provider, user, trace, or custom dimension, token and cache-hit economics, and cost regressions. Use when the user asks "how much are we spending on LLMs?", "which model / user / feature is most expensive?", "why did cost spike?", wants to build a cost dashboard or alert, or pastes a trace URL and asks about its cost.
exploring-llm-evaluations
PostHog
Investigate AI observability evaluations — hog (deterministic code-based), llm_judge (LLM-prompt-based), and sentiment (user-message sentiment). Find existing evaluations, inspect their configuration, run them against specific generations, query individual results, and set up scheduled reports on an evaluation. Use when the user asks to debug why an evaluation is failing, surface common failure modes, compare results across filters, dry-run a Hog evaluator, prototype a new LLM-judge prompt, inspect sentiment classifications, or manage the evaluation lifecycle.
analyzing-expensive-users
PostHog
Analyze the most expensive users in AI observability and explain why they cost so much. Use when the user asks about top spenders, expensive users, per-user LLM cost, user-level cost drivers, or patterns behind high AI observability spend.