Other skills

Unity profiling patterns including ProfilerMarkers, FrameTimingManager, memory profiling, and performance budgets.

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Unity NavMesh pathfinding patterns, safety checks, and performance optimization.

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Unity physics query correctness patterns. Catches common mistakes with Raycast, SphereCast, OverlapSphere, NonAlloc allocation, LayerMask construction, trigger interaction, hit ordering, and query type selection. PATTERN format: WHEN/WRONG/RIGHT/GOTCHA. Based on Unity 6.3 LTS.

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Unity New Input System correctness patterns. Catches common mistakes with action reading (triggered vs IsPressed vs WasPressedThisFrame), action map switching, rebinding persistence, InputValue lifetime, PassThrough vs Value, local multiplayer device assignment, and control scheme auto-switching. PATTERN format: WHEN/WRONG/RIGHT/GOTCHA. Based on Unity 6.3 LTS.

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Object pooling patterns for Unity to reduce GC allocations and improve performance.

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Unity physics patterns including character controllers, collision handling, and tunneling prevention.

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Mobile-specific Unity optimization patterns for memory, battery, thermal, and performance.

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FlowCanvas visual scripting patterns for abilities, custom nodes, and graph execution.

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Unity Addressables patterns for asset loading, memory management, reference counting, and remote content delivery.

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MemoryPack high-performance binary serialization for Unity including custom formatters, versioning, and performance patterns.

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Validates and scores SKILL.md files against the agentskills.io specification; use when linting skills for cross-agent compatibility, description quality, or capability graph structure.

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Use when the user needs to find, compare, and extract reusable content structures from low-follower high-performing Xiaohongshu posts, or diagnose why a post did or did not become a breakout using click rate, retention, and interaction signals. Trigger phrases: "找对标", "低粉爆款", "拆爆款", "内容骨架", "点击率", "停留时长", "互动率", "competitor pattern", "viral post structure".

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Use when the user has an AI-generated Xiaohongshu draft and wants it to sound more personal, concrete, platform-appropriate, and stronger in hook, retention, interaction value, or pre-publish safety. Trigger phrases: "去 AI 味", "不像我", "合规检查", "发布前审", "能不能发", "改成能发", "违禁词", "限流风险", "改得像真人", "开头不抓人", "没停留", "humanize draft", "platform check". Do not use for initial positioning or topic generation.

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StarRocks materialized views — synchronous MV (rollup/aggregate acceleration), asynchronous MV (CREATE MATERIALIZED VIEW REFRESH ASYNC/MANUAL/SCHEDULED, partition-aware refresh, query rewrite, nested MVs, MV on Iceberg/Hive external tables), SHOW MATERIALIZED VIEWS, ALTER/REFRESH/DROP MV, transparent query rewrite via EXPLAIN, use_mv hint

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StarRocks high-concurrency optimization — pipeline_dop tuning for BI workloads, resource groups for workload isolation (short_query type), query queue configuration, FE connection pool, tablet scan parallelism, point query optimization (Primary Key table direct lookup), query cache for repeated dashboard queries, BE thread pool tuning

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StarRocks schema evolution — ALTER TABLE ADD/DROP/MODIFY/REORDER COLUMN, column type widening rules, adding columns to Aggregate/Unique/Primary Key tables, schema change types (direct vs linked vs sorted), SHOW ALTER TABLE COLUMN progress, rollback safety, backward-compatible patterns, dbt model evolution, zero-downtime migration checklist

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StarRocks EXPLAIN plan reading — EXPLAIN/EXPLAIN VERBOSE/EXPLAIN COSTS output structure, operator nodes (OlapScanNode/HashJoinNode/AggregationNode/ExchangeNode/SortNode/ProjectNode), reading fragment instances, identifying bottlenecks (shuffle exchange cost, large scan rows, memory spill), runtime filter nodes, partition pruning in scan, query profile (SHOW PROFILELIST/EXPLAIN ANALYZE)

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Airflow + StarRocks pipeline orchestration — MySqlHook for DDL/DML, Broker Load trigger + poll pattern, Stream Load via HTTP operator, Routine Load job lifecycle (pause/resume/stop), sensor for load completion, ANALYZE TABLE post-load, partition-aware DAG templating, Airflow variables for StarRocks connection config, XCom label passing

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Production dbt + Trino platform — dbt-trino adapter profiles.yml (ldap/kerberos/oauth/jwt/certificate auth), all materializations (table/view/incremental/materialized_view/ephemeral), incremental strategies (append/merge/delete+insert), Iceberg table_properties in config blocks (format/partitioning/sorted_by/location), on_schema_change, snapshots with TIMESTAMP(6), slim CI with state:modified+ and --defer, dbt project structure (staging/intermediate/mart), ANALYZE post-hook, session_properties in profiles, multi-thread parallelism

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StarRocks compaction tuning — base compaction vs cumulative compaction, compaction score analysis, write amplification reduction, BE config parameters (compaction_threads/min_cumulative_compaction_num_singleton_deltas/max_compaction_candidate_num), SHOW PROC '/compactions', manual compaction trigger, Primary Key table compaction, rowset management

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StarRocks AI incident RCA — autonomous root cause analysis agent for FE/BE failures, memory spike diagnosis (BE heap/process memory), Routine Load pause RCA (ErrorLogUrls + error patterns), Kafka consumer lag spike analysis, compaction backlog detection, tablet replication failures, OOM kill patterns, query timeout clusters, FE GC pressure detection, structured incident report generation

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dbt + StarRocks models — dbt-starrocks adapter setup (profiles.yml), all materializations (table/view/incremental/ephemeral), incremental strategies (append/insert_overwrite/unique_key merge), StarRocks-specific model config (engine/keys/partition_by/distributed_by/properties), Duplicate/Aggregate/Primary Key table DDL from dbt, partition_by with date_trunc, on_schema_change behavior

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StarRocks file ingestion — FILES() table function (SELECT/INSERT from S3/HDFS Parquet/ORC/CSV without CREATE TABLE), Iceberg external catalog (HMS/Glue/REST), CREATE EXTERNAL CATALOG, cross-catalog INSERT INTO SELECT, schema auto-detection, partition filter pushdown on external tables, SHOW CREATE CATALOG, external table DDL patterns

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StarRocks aggregation optimization — one-phase vs two-phase aggregation, pre-aggregation pushdown to scan, Aggregate Key table automatic merge, GROUP BY on distribution key (local aggregation), COUNT DISTINCT optimization (multi-phase), HLL/BITMAP approximate aggregation, aggregation spill tuning, EXPLAIN aggregation nodes

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