plugins/faos-dev/skills/postgres-best-practices/SKILL.md
<!-- AUTO-GENERATED by export-plugins.py — DO NOT EDIT --> --- name: postgres-best-practices description: Postgres performance optimization and best practices from Supabase covering indexing, connection management, RLS, schema design, and query tuning. Use when writing, reviewing, or optimizing Postgres queries, schema designs, or database configurations. tags: [postgres, database, sql, optimization] --- # Supabase Postgres Best Practices Comprehensive performance optimization guide for Postgr
npx skillsauth add frank-luongt/faos-skills-marketplace plugins/faos-dev/skills/postgres-best-practicesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Comprehensive performance optimization guide for Postgres, maintained by Supabase. Contains rules across 8 categories, prioritized by impact to guide automated query optimization and schema design.
Reference these guidelines when:
| Priority | Category | Impact | Prefix |
|----------|----------|--------|--------|
| 1 | Query Performance | CRITICAL | query- |
| 2 | Connection Management | CRITICAL | conn- |
| 3 | Security & RLS | CRITICAL | security- |
| 4 | Schema Design | HIGH | schema- |
| 5 | Concurrency & Locking | MEDIUM-HIGH | lock- |
| 6 | Data Access Patterns | MEDIUM | data- |
| 7 | Monitoring & Diagnostics | LOW-MEDIUM | monitor- |
| 8 | Advanced Features | LOW | advanced- |
Read individual rule files for detailed explanations and SQL examples:
rules/query-missing-indexes.md
rules/schema-partial-indexes.md
rules/_sections.md
Each rule file contains:
For the complete guide with all rules expanded, see: references/postgres-rules.md
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: grpo-rl-training description: GRPO reinforcement learning training with TRL. Use when applying Group Relative Policy Optimization for reasoning and task-specific model training. --- # GRPO/RL Training with TRL Expert-level guidance for implementing Group Relative Policy Optimization (GRPO) using the Transformer Reinforcement Learning (TRL) library. This skill provides battle-tested patterns, critical insights, and production-r
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<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: graphql-architect description: Master modern GraphQL with federation, performance optimization, --- ## Use this skill when - Working on graphql architect tasks or workflows - Needing guidance, best practices, or checklists for graphql architect ## Do not use this skill when - The task is unrelated to graphql architect - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: grafana-dashboards description: Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces. --- # Grafana Dashboards Create and manage production-ready Grafana dashboards for comprehensive system observability. ## Do not use this skill when - The task is unrelated
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: gptq description: GPTQ post-training quantization for generative models. Use when quantizing large models to 4-bit with calibration-based weight compression. --- # GPTQ (Generative Pre-trained Transformer Quantization) Post-training quantization method that compresses LLMs to 4-bit with minimal accuracy loss using group-wise quantization. ## When to use GPTQ **Use GPTQ when:** - Need to fit large models (70B+) on limited GPU