全部能力

Explanations of common asynchronous patterns used in tursodb. Involves IOResult, state machines, re-entrancy pitfalls, CompletionGroup. Always use these…

SQLite file format, B-trees, pages, cells, overflow, freelist that is used in tursodb

Overview of Experimental MVCC feature - snapshot isolation, versioning, limitations

使用 inlets 和 outlets 为 Airflow 任务添加数据血缘注解。适用于用户希望为任务添加血缘元数据、指定输入/输出数据集，或…

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解释 claude-mem 如何捕获观察结果、何时触发记忆注入，以及数据存储在哪里。当用户询问“claude-mem 是如何工作的？”或“什么……”时使用。

Bridge between OpenWork UI and OpenCode runtime

AI coding agent skill for building with EmDash, the full-stack TypeScript CMS built on Astro and Cloudflare

为重大架构变更创建新的架构决策记录（ADR）文档，使用一致的模板和基于仓库的命名规范…

Help create git commits and PRs with properly formatted messages and release notes following CockroachDB conventions. Use when committing changes or creating…

Core DQL syntax rules, common pitfalls, and query patterns. Load this skill when you need to write, build, or fix a DQL query — it prevents syntax errors and guides correct usage. Covers fetch commands, data models, field namespaces, time alignment, entity patterns, metric discovery, and smartscape topology navigation. Trigger: "write a DQL query", "build me a query", "DQL syntax", "how do I query logs/spans/metrics in Dynatrace", "create a timeseries", "fix my DQL", "fetch logs", "smartscapeNodes", "query optimization". Do NOT use for explaining an existing query or answering Dynatrace product questions — those do not require query-construction guidance.

Distributed traces, spans, service dependencies, and request flow analysis. Use when investigating span-level details, failures, performance bottlenecks, or trace correlation. Trigger: "trace analysis", "slow requests", "failed spans", "service dependencies", "distributed trace", "span details", "HTTP status codes in traces", "database query spans", "messaging spans", "gRPC calls", "Lambda cold starts", "trace ID lookup", "exception analysis", "correlate logs and traces", "request attributes". Do NOT use for explaining existing queries, product documentation or configuration questions, service-level RED metrics (use dt-obs-services), log searching (use dt-obs-logs), or problem analysis (use dt-obs-problems).

Host and process metrics including CPU, memory, disk, network, containers, and process-level telemetry. Use when analyzing infrastructure health, resource utilization, process consumption, or host discovery. Also use when building timeseries queries for host metrics that feed into analytical workflows like anomaly detection, forecasting, or seasonality analysis. Trigger: "show hosts", "CPU usage", "memory utilization", "disk space", "high CPU", "host with most free disk", "top hosts by CPU", "top processes by memory", "Linux hosts in AWS", "what databases are running", "infrastructure costs by cost center", "hosts running EOL Java", "container monitoring", "listening ports", "process resource consumption", "CPU forecast", "memory anomaly", "host seasonality". Do NOT use for explaining existing queries, product documentation questions, Kubernetes pod/workload queries (use dt-obs-kubernetes), AWS cloud resource inventory (use dt-obs-aws), or service-level metrics (use dt-obs-services).

AWS cloud resource monitoring including EC2, RDS, Lambda, ECS/EKS, VPC networking, load balancers, S3, DynamoDB, SQS/SNS, and cost optimization. Use when analyzing AWS infrastructure, resource inventory, security compliance, capacity planning, or cost savings. Trigger: "show EC2 instances", "find RDS databases", "VPC resources", "AWS cost optimization", "Lambda functions", "ECS services", "security groups", "unattached EBS volumes", "AWS load balancer topology", "publicly accessible databases", "AWS dashboards". Do NOT use for explaining existing queries, product documentation questions, generic host CPU/memory metrics (use dt-obs-hosts), application-level tracing (use dt-obs-tracing), or log analysis (use dt-obs-logs).

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In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries directly on local files, remote databases, and cloud storage without a server. Use when the user wants to write SQL queries against Parquet/CSV/ JSON files, use ClickHouse table functions (mysql(), s3(), postgresql(), iceberg(), deltaLake() etc.), build stateful analytical pipelines with Session, use parametrized queries, window functions, or other advanced ClickHouse SQL features. Also use when the user explicitly mentions chdb.query(), ClickHouse SQL syntax, or wants cross-source SQL joins. Do NOT use for pandas-style DataFrame operations — use chdb-datastore instead.

Datadog CLI for searching logs, querying metrics, tracing requests, and managing dashboards. Use this when debugging production issues or working with Datadog…

Improve database query performance through indexing, query optimization, and execution plan analysis. Reduce response times and database load.

Design database schemas with normalization, relationships, and constraints. Use when creating new database schemas, designing tables, or planning data models for PostgreSQL and MySQL.

Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset…