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Agent Skill · 研究检索

postgres-tuning(Postgres tuning 搜索)

用于辅助数据库表结构、查询语句、迁移脚本和数据维护任务。它适合让 Agent 分析 schema、编写 SQL、排查查询问题、整理索引或生成迁移建议。使用时需要明确数据库类型、连接环境和目标表,区分只读分析与写入变更;涉及删除、更新、迁移和批量导入时,应优先 dry-run、备份或事务保护,避免误操作。

postgres-tuning 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 43,020。

总安装 1,022周安装 43GitHub Stars 10下载 358核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:postgres-tuning(Postgres tuning 搜索)
来源仓库:https://github.com/oakoss/agent-skills
仓库路径:skills/postgres-tuning
安装命令:
npx skills add https://github.com/oakoss/agent-skills --skill postgres-tuning
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/oakoss/agent-skills --skill postgres-tuning

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于辅助数据库表结构、查询语句、迁移脚本和数据维护任务。适合分析 schema、编写 SQL、排查查询性能或生成索引优化建议。使用时需明确数据库连接环境和目标表,区分只读分析与写入变更。涉及删除、更新、迁移或批量导入时,应优先 dry-run、备份或启用事务保护。

建议结合数据库负载和使用模式使用,确保调优建议具备实际可操作性和稳定性。

来源仓库内路径:skills/postgres-tuning

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/oakoss/agent-skills --skill postgres-tuning;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

postgres-tuning 怎么安装?

在终端执行 npx skills add https://github.com/oakoss/agent-skills --skill postgres-tuning。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

postgres-tuning 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

postgres-tuning 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

postgres-tuning 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

postgres-tuning 用的人多吗?

公开数据是累计安装 1,022 次,近一周安装 43 次,下载 358 次,GitHub 10 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

postgres-tuning 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
postgres-tuning

Postgres tuning 搜索 · 本页

1,02210只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

PostgreSQL Tuning

Overview

Optimizes PostgreSQL 17/18+ performance across I/O, query execution, indexing, and maintenance. Covers the native AIO subsystem introduced in PostgreSQL 18 for throughput gains on modern storage, forensic query plan analysis with EXPLAIN BUFFERS (auto-included in PG18), B-tree skip scans for composite indexes, native UUIDv7 generation, and autovacuum tuning for high-churn tables.

When to use: Diagnosing slow queries, configuring async I/O, tuning shared_buffers and work_mem, optimizing indexes for write-heavy workloads, managing table bloat, pgvector HNSW tuning.

When NOT to use: Schema design (use a data modeling tool), application-level caching strategy, database selection decisions, ORM query generation.

Key monitoring views:

  • pg_stat_statements — identifies slow query patterns by cumulative execution time
  • pg_stat_io — granular I/O analysis by backend type, object, and context (PG16+)
  • pg_stat_checkpointer — checkpoint frequency and timing (PG17+; previously in pg_stat_bgwriter)
  • pg_stat_user_tables — dead tuple counts for bloat detection and autovacuum monitoring
  • pg_statio_user_tables — buffer cache hit ratios per table
  • pg_aios — in-progress AIO operations (PG18+)

Quick Reference

PatternConfiguration / QueryKey Points
Async I/Oio_method = worker or io_uringPG18 default is worker; io_uring Linux-only (kernel 5.1+, requires liburing build flag)
I/O concurrencyio_max_concurrency and io_workersio_workers defaults to 3; io_max_concurrency defaults to -1 (auto-calculated)
Forensic EXPLAINEXPLAIN (ANALYZE, BUFFERS, SETTINGS)PG18 auto-includes BUFFERS with ANALYZE; target Shared Hit > 95%
UUIDv7 primary keysDEFAULT uuidv7()PG18 built-in; time-ordered, monotonic within a session; RFC 9562 compliant
B-tree skip scanComposite index on (a, b)PG18 skips leading column; works best with low-cardinality prefix and equality on trailing columns
Aggressive autovacuumautovacuum_vacuum_scale_factor = 0.01Triggers at 1% row change instead of default 20%
Shared buffersStart at 25% of RAMDo not exceed 40% without benchmarking
work_mem tuningSET work_mem = '64MB' per sessionPrevents sort spills to disk; allocated per operator, not per query
BRIN indexCREATE INDEX USING brin(...)100x smaller than B-tree for physically ordered time-series data
HNSW vector indexUSING hnsw (col vector_cosine_ops)Tune m (default 16) and ef_construction (default 64) for recall vs speed
GIN indexCREATE INDEX USING gin(...)JSONB containment, full-text search, array operators; slower writes
Checkpoint tuningcheckpoint_timeout = 30minSpread writes over 90% of timeout window to avoid I/O storms
WAL compressionwal_compression = zstdAvailable since PG15; reduces WAL I/O 50-70% for write-heavy workloads
Bloat detectionpg_stat_user_tables.n_dead_tupReindex concurrently if bloat > 30%
I/O monitoringSELECT * FROM pg_stat_ioWatch evictions (cache too small) and extends (fast growth)
Checkpoint monitoringpg_stat_checkpointerPG17+ moved checkpoint stats out of pg_stat_bgwriter

Key Version Changes

PostgreSQL 18:

  • Native async I/O via io_method parameter (reads only; writes remain synchronous)
  • Built-in uuidv7() function with monotonic ordering within a session (RFC 9562)
  • uuidv4() alias for gen_random_uuid() and uuid_extract_timestamp() for UUIDv7
  • B-tree skip scan for composite indexes (equality on trailing columns, low-cardinality prefix)
  • EXPLAIN ANALYZE auto-includes buffer statistics without specifying BUFFERS
  • pg_stat_io gains byte-level columns (read_bytes, write_bytes, extend_bytes); op_bytes removed
  • effective_io_concurrency default changed from 1 to 16
  • AIO monitoring via pg_aios system view for in-progress I/O operations

PostgreSQL 17:

  • Checkpoint statistics moved from pg_stat_bgwriter to pg_stat_checkpointer
  • Column renames: checkpoints_timed to num_timed, checkpoints_req to num_requested
  • buffers_backend and buffers_backend_fsync removed from pg_stat_bgwriter (now in pg_stat_io)

PostgreSQL 15:

  • wal_compression expanded from boolean to support pglz, lz4, and zstd algorithms

Common Mistakes

MistakeCorrect Pattern
Using uuid_generate_v7() or gen_random_uuid() for ordered keysPG18 provides built-in uuidv7() for time-ordered UUIDs; pre-PG18 use pg_uuidv7 extension
Using max_async_ios as a configuration parameterThe correct PG18 parameter is io_max_concurrency (max concurrent I/O ops per process)
Querying pg_stat_bgwriter for checkpoint statistics on PG17+Checkpoint stats moved to pg_stat_checkpointer in PG17; columns renamed (num_timed, num_requested)
Using SELECT * in high-frequency queriesSelect only needed columns to reduce I/O and improve cache hit ratios
Ignoring sequential scans on tables over 10k rowsAdd targeted indexes on columns used in WHERE, ORDER BY, and JOIN clauses
Setting shared_buffers above 40% of RAM without testingStart at 25% and benchmark; excessive allocation causes OS page cache contention
Leaving autovacuum at default settings for high-churn tablesTune autovacuum_vacuum_scale_factor to 0.01 for tables with frequent UPDATE/DELETE
Over-indexing columns rarely used in queriesEvery extra index slows UPDATE/INSERT and prevents HOT (Heap Only Tuple) updates
Expecting B-tree skip scan to work with range predicatesPG18 skip scan only works with equality operators on trailing columns
Ignoring "External Merge Disk" in query plansIncrease work_mem for specific sessions; it indicates sort spills to disk
Setting io_method = io_uring without verifying build flagsPostgreSQL must be built with --with-liburing and requires Linux kernel 5.1+
Assuming PG18 AIO accelerates writesAIO in PG18 only covers reads (seq scans, bitmap heap scans, VACUUM); writes remain synchronous

Tuning Workflow

  1. Identify slow queries from pg_stat_statements (sort by total_exec_time)
  2. Analyze execution plans with EXPLAIN (ANALYZE, BUFFERS, SETTINGS)
  3. Check buffer hit ratios via pg_statio_user_tables (target > 99%)
  4. Monitor I/O patterns via pg_stat_io (watch evictions and disk reads)
  5. Optimize with targeted indexes, work_mem adjustments, or query rewrites
  6. Verify improvements by re-running EXPLAIN and comparing costs
  7. Maintain with aggressive autovacuum settings for high-churn tables

Delegation

  • Discover slow queries and I/O bottlenecks: Use Explore agent to analyze pg_stat_statements, pg_stat_io, and slow query logs
  • Execute query plan analysis and index optimization: Use Task agent to run EXPLAIN ANALYZE, create indexes, and verify performance improvements
  • Design database scaling and partitioning strategy: Use Plan agent to architect sharding, partitioning, and replication topology

References

平台分布

Codex

35.25%
按下载量换算126

Claude

29.99%
按下载量换算107

Cursor

16.78%
按下载量换算60

Gemini CLI

9%
按下载量换算32

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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