Results for “query-tuning”

Guides scaling Qdrant query throughput (QPS) through performance tuning, horizontal scaling with read replicas, and disk I/O optimization.

Optimizes database queries and improves performance across PostgreSQL and MySQL systems by analyzing execution plans, designing index strategies, and tuning configurations.

Self-tuning question sensitivity + developer psychographic for gstack (v1: observational). (gstack)

Adds autotuning to CuTile kernels using the exhaustive_search API with a tune-once/cache/direct-launch pattern, covering occupancy-only and complex tile-size search spaces.

Diagnoses and resolves slow Qdrant indexing and data ingestion by optimizing batching, sharding, HNSW parameters, and payload indexing strategies.

Optimizes Qdrant query performance for large limits across multiple shards by using Poisson-distributed subsampling to reduce inter-shard data transfer.

Guides scaling decisions for Qdrant vector databases based on data volume, query throughput, latency, or query volume.

通用多轮迭代改进：对任意研究制品反复调用 /review → 解析反馈 → 修复 → 更新 wiki，直到达标

Use to design support queues and the skill taxonomy routing rules depend on — not as an org chart mirror, but as staffable combinations of skill, priority and channel. Trigger for "how should we structure our queues", "too many queues", "queue design", "routing taxonomy", "catch-all queue growing", overflow rules, or redesigning queue structure before changing routing rules.

Optimize PostgreSQL queries, configure replication, and implement advanced database features with EXPLAIN analysis, JSONB operations, extension usage, and VACUUM tuning.

jq - JSON Querying and Transformation

Relational modelling lets you defer query design: normalize the entities, and

MySQL and MariaDB schema, query, indexing, transaction, replication, and connection-pool patterns for production backends.

Use this when the system needs queue-backed processing design, message handling rules, delivery semantics, dead-letter strategy, or throughput planning. Trigger on requests about queues, brokers, message processing, and consumer coordination.

Self-tuning question sensitivity + developer psychographic for gstack (v1: observational). Review which AskUserQuestion prompts fire across gstack skills, set per-question preferences (never-ask / always-ask / ask-only-for-one-way), inspect the dual-track profile (what you declared vs what your behavior suggests), and enable/disable question tuning. Conversational interface — no CLI syntax required. Use when asked to "tune questions", "stop asking me that", "too many questions", "show my profile", "what questions have I been asked", "show my vibe", "developer profile", or "turn off question tuning". (gstack) Proactively suggest when the user says the same gstack question has come up before, or when they explicitly override a recommendation for the Nth time.

Provides MySQL and MariaDB schema, query, indexing, transaction, replication, and connection-pool patterns for production backends.

Designer's eye QA: finds visual inconsistency, spacing issues, hierarchy problems, AI slop patterns, and slow interactions — then fixes them. (gstack)

Build ontology logs (ologs) from problem descriptions using categorical foundations. Use when designing problem taxonomies, classifying tasks for routing, building knowledge libraries, establishing formal analogies between domains via functor search, or translating between natural language and database schemas. NOT for OWL/RDF ontology work, query tuning, or graph modeling without functional-arrow discipline.