Loading...
Loading...
Found 274 Skills
WeCom schedule management skill. It meets users' various management needs for WeCom schedules. Use this skill when users need to: (1) Query the schedule list within a specified time range or obtain detailed schedule information (title, time, location, participants, etc.), (2) Create new schedules and set reminders, participants, etc., (3) Modify information such as title, time, location of existing schedules or cancel schedules, (4) Add or remove schedule participants, (5) Query the busy/free status of multiple members and analyze common free time slots to arrange meetings.
Query resource usage metrics for Railway services. Use when user asks about resource usage, CPU, memory, network, disk, or service performance like "how much memory is my service using" or "is my service slow".
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
Design partition schemes, select partition keys, create GSI, and write SQL for PolarDB-X 2.0 Enterprise Edition AUTO mode databases, handling PolarDB-X vs MySQL differences (partitioned tables, GSI, CCI, Sequence, table groups, TTL, pagination, etc.). Use when designing partition schemes, selecting partition keys, converting single tables to partitioned tables, creating GSI/CCI indexes, writing or migrating SQL for PolarDB-X, or diagnosing slow queries on PolarDB-X. Triggers: "PolarDB-X SQL", "PolarDB-X create table", "partitioned table", "partition design", "partition scheme", "partition key", "GSI", "CCI", "Sequence", "MySQL migrate to PolarDB-X", "PolarDB-X compatibility", "single table to partitioned table", "convert to partitioned table", "large table", "distributed table", "AUTO mode", "pagination query", "Keyset pagination", "Range partition", "auto add partition", "PolarDB-X slow query", "full-shard scan"
Set up and optimize context management for any project. Use this skill when the user says "set up context management", "optimize my CLAUDE.md", "context setup", "configure compact instructions", "set up rules", or when starting a new project and wanting best practices for long sessions, memory, compaction, and subagent delegation. Also trigger when the user mentions problems with context loss, compaction losing info, or sessions getting slow.
Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.
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 app feels slow, memory grows, battery drains, or diagnosing ANY performance issue. Covers memory leaks, profiling, Instruments workflows, retain cycles, performance optimization.
Use when debugging Foundation Models issues — context exceeded, guardrail violations, slow generation, availability problems, unsupported language, or unexpected output. Systematic diagnostics with production crisis defense.
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.
Diagnose ClickHouse issues by analyzing system.part_log (part creation, merges, mutations, downloads, removals, moves). Use for too many parts / micro-batch inserts, merge backlog or slow merges, mutation storms (ALTER DELETE/UPDATE), unusual replication DownloadPart churn, unexpected RemovePart spikes, or ZooKeeper/Keeper znode growth correlated with part activity.
Remove AI-generated code slop from branches. Use after AI-assisted coding sessions to clean up defensive bloat, unnecessary comments, type casts, and style inconsistencies. Focuses on identifying and removing AI artifacts that degrade code quality.