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PostgreSQL Optimization Workflow · sickn33 | DecimalAI

sickn33/postgresql-optimization

SkillScore93Maintained

PostgreSQL database optimization workflow for query tuning, indexing strategies, performance analysis, and production database management.

Databy sickn33

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

Data Engineer pack

1 · One-time — install the CLI (docs ↗)

pip install decimalai

2 · Get the skill — writes .claude/skills/sickn33-postgresql-optimization/SKILL.md

decimalai skills pull sickn33-postgresql-optimization --out .claude/skills/

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/sickn33-postgresql-optimization/SKILL.md https://app.decimal.ai/s/sickn33-postgresql-optimization/SKILL.md

Raw file: /s/sickn33-postgresql-optimization/SKILL.md ↗ · machine-readable summary at /s/sickn33-postgresql-optimization.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

Runs

all versions · 30d

Eval pass

90%

global

Used when offered

26%

used / offered · all versions

By model· real traffic, every org · 30d — not the benchmark

Model	Eval pass	Runs
gemini-3.6-flash	100%	9

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

22 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 49d ago

Yes — +9% lift+21% tokens0% turns95% of whole cases pass with skill (21/22)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-02	✗→✓	▲ Improved	-5%	0%
case-22	✗→✓	▲ Improved	32%	0%
case-03	✓→✓	＝ Same ✓	-1%	0%
case-01	✓→✓	＝ Same ✓	6%	0%
case-04	✓→✓	＝ Same ✓	47%	0%

Showing 5 of 22 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/2/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

sickn33 / antigravity-awesome-skills

skills/postgresql-optimization/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1

Skill contents

SKILL.md

PostgreSQL Optimization Workflow

Overview

Specialized workflow for PostgreSQL database optimization including query tuning, indexing strategies, performance analysis, vacuum management, and production database administration.

When to Use This Workflow

Use this workflow when:

Optimizing slow PostgreSQL queries
Designing indexing strategies
Analyzing database performance
Tuning PostgreSQL configuration
Managing production databases

Workflow Phases

Phase 1: Performance Assessment

Skills to Invoke

database-optimizer - Database optimization
postgres-best-practices - PostgreSQL best practices

Actions

Check database version
Review configuration
Analyze slow queries
Check resource usage
Identify bottlenecks

Copy-Paste Prompts

Use @database-optimizer to assess PostgreSQL performance

Phase 2: Query Analysis

Skills to Invoke

sql-optimization-patterns - SQL optimization
postgres-best-practices - PostgreSQL patterns

Actions

Run EXPLAIN ANALYZE
Identify scan types
Check join strategies
Analyze execution time
Find optimization opportunities

Copy-Paste Prompts

Use @sql-optimization-patterns to analyze and optimize queries

Phase 3: Indexing Strategy

Skills to Invoke

database-design - Index design
postgresql - PostgreSQL indexing

Actions

Identify missing indexes
Create B-tree indexes
Add composite indexes
Consider partial indexes
Review index usage

Copy-Paste Prompts

Use @database-design to design PostgreSQL indexing strategy

Phase 4: Query Optimization

Skills to Invoke

sql-optimization-patterns - Query tuning
sql-pro - SQL expertise

Actions

Rewrite inefficient queries
Optimize joins
Add CTEs where helpful
Implement pagination
Test improvements

Copy-Paste Prompts

Use @sql-optimization-patterns to optimize SQL queries

Phase 5: Configuration Tuning

Skills to Invoke

postgres-best-practices - Configuration
database-admin - Database administration

Actions

Tune shared_buffers
Configure work_mem
Set effective_cache_size
Adjust checkpoint settings
Configure autovacuum

Copy-Paste Prompts

Use @postgres-best-practices to tune PostgreSQL configuration

Phase 6: Maintenance

Skills to Invoke

database-admin - Database maintenance
postgresql - PostgreSQL maintenance

Actions

Schedule VACUUM
Run ANALYZE
Check table bloat
Monitor autovacuum
Review statistics

Copy-Paste Prompts

Use @database-admin to schedule PostgreSQL maintenance

Phase 7: Monitoring

Skills to Invoke

grafana-dashboards - Monitoring dashboards
prometheus-configuration - Metrics collection

Actions

Set up monitoring
Create dashboards
Configure alerts
Track key metrics
Review trends

Copy-Paste Prompts

Use @grafana-dashboards to create PostgreSQL monitoring

Optimization Checklist

] Slow queries identified
] Indexes optimized
] Configuration tuned
] Maintenance scheduled
] Monitoring active
] Performance improved

Quality Gates

] Query performance improved
] Indexes effective
] Configuration optimized
] Maintenance automated
] Monitoring in place

Related Workflow Bundles

database - Database operations
cloud-devops - Infrastructure
performance-optimization - Performance

Limitations

Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Illustrative Want to see what sickn33/postgresql-optimization would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Latest benchmark · publisher runVerified

8/10/2026, 13:21 UTC · 22 cases

Lift

+9%

95% vs 86%

Agent turns

1.0 vs 1.0

Tokens

+21%

3,317.05 vs 2,749.86

fail → pass · 2pass → pass · 19fail → fail · 1

Per-case results (22)

Case	Status	Duration (ms)			Turns			Tokens			Tool calls
Case	Status	Without	With	Δ	Without	With	Δ	Without	With	Δ	Without	With	Δ
case-03 We are preparing our PostgreSQL database for an upcoming high-traffic product launch. I need a comprehensive step-by-step optimization plan covering initial bottleneck assessment, query and index tuning, configuration adjustments, and setting up Grafana and Prometheus observability to track query latencies and system metrics.	pass→pass	24,877	19,896	-20%	1	1	0%	4,767	4,723	-1%	0	0	—
case-01 Our Postgres production database is experiencing severe latency spikes on complex reporting queries. Can you run a structured multi-phase analysis to evaluate our slow execution plans, identify join/scan bottlenecks, recommend specific indexing improvements (like composite or partial indexes), and outline necessary query rewrites to reduce response time?	pass→pass	21,740	17,632	-19%	1	1	0%	4,069	4,321	+6%	0	0	—
case-02 We recently upgraded our server hardware for PostgreSQL but haven't adjusted our database parameters. Please guide us through a complete tuning and maintenance assessment, covering memory configuration settings (like work memory and shared buffers), autovacuum and bloat management, and routine maintenance tasks to ensure peak database health.	fail→pass	20,219	14,104	-30%	1	1	0%	3,989	3,794	-5%	0	0	—
case-04 We are tuning a MySQL 8.0 server that is experiencing high CPU usage on InnoDB tables. Please provide a step-by-step performance optimization workflow for MySQL configuration and query analysis.	pass→pass	19,420	23,759	+22%	1	1	0%	3,569	5,255	+47%	0	0	—
case-05 Our MongoDB cluster is showing slow response times on complex aggregation pipelines. Can you provide a step-by-step database optimization plan for MongoDB indexes and memory limits?	pass→pass	16,202	15,412	-5%	1	1	0%	2,812	3,591	+28%	0	0	—
case-06 Our Redis instance is running out of memory during peak traffic. Please provide an optimization strategy for Redis memory management and key eviction policies.	pass→pass	15,547	12,975	-17%	1	1	0%	2,973	3,320	+12%	0	0	—
case-07 We have a dedicated PostgreSQL 15 server with 64GB of RAM running an OLTP workload. How should we set shared_buffers, work_mem, and effective_cache_size?	pass→pass	14,259	12,734	-11%	1	1	0%	2,663	3,437	+29%	0	0	—
case-08 Our query `SELECT * FROM orders WHERE status = 'pending' AND created_at > '2023-01-01'` is doing a sequential scan over 10 million rows despite having single-column indexes on `status` and `created_at`. How do we analyze and fix this?	pass→pass	14,061	11,431	-19%	1	1	0%	2,655	3,091	+16%	0	0	—
case-09 Our PostgreSQL database updates millions of rows daily, causing severe table bloat and slowing down queries. What autovacuum settings should we adjust to prevent bloat?	pass→pass	14,814	14,332	-3%	1	1	0%	2,784	3,480	+25%	0	0	—
case-10 We have an `orders` table with 50 million rows, where 99% of rows have `status = 'completed'` and 1% have `status = 'processing'`. Queries almost exclusively search for `status = 'processing'`. What indexing strategy minimizes index size and maximizes query speed?	pass→pass	10,610	8,761	-17%	1	1	0%	2,126	2,324	+9%	0	0	—
case-11 We noticed that a query using multiple CTEs is running slowly in PostgreSQL. How does PostgreSQL handle CTE materialization and how can we optimize CTE execution?	pass→pass	15,185	15,796	+4%	1	1	0%	2,731	3,769	+38%	0	0	—
case-12 Which system catalog views in PostgreSQL should we query to identify tables with high sequential scans and indexes that are never used?	pass→pass	10,248	9,085	-11%	1	1	0%	2,137	2,825	+32%	0	0	—
case-13 We want to export query performance metrics from PostgreSQL to Prometheus and visualize them in Grafana. Which exporter should we deploy and what key metrics should we track?	pass→pass	15,392	12,479	-19%	1	1	0%	2,929	3,326	+14%	0	0	—
case-14 Our write-intensive PostgreSQL database experiences severe I/O spikes every few minutes during checkpoint execution. Which configuration parameters control checkpoint frequency and I/O smoothing?	pass→pass	13,428	10,285	-23%	1	1	0%	2,437	2,866	+18%	0	0	—
case-15 Our Web application uses `OFFSET 1000000 LIMIT 20` to paginate through billions of rows, and response times exceed 10 seconds. How should we optimize large offset pagination in PostgreSQL?	pass→pass	15,997	14,387	-10%	1	1	0%	2,784	3,448	+24%	0	0	—
case-16 We store unstructured event payloads in a JSONB column named `data`. We frequently query `data->>'event_type'` and `data @> '{"user_id": 123}'`. What index types should we use?	pass→pass	10,582	9,164	-13%	1	1	0%	2,164	2,732	+26%	0	0	—
case-17 Can you optimize our PostgreSQL configuration for better query performance?	fail→fail	14,207	11,113	-22%	1	1	0%	2,602	3,069	+18%	0	0	—
case-18 When diagnosing why a query is slow, why is running `EXPLAIN (ANALYZE, BUFFERS)` better than just `EXPLAIN`?	pass→pass	12,218	12,238	+0%	1	1	0%	2,353	3,108	+32%	0	0	—
case-19 We need to create a B-tree index on a 200GB production table with constant read and write traffic. How can we create the index without blocking incoming writes?	pass→pass	12,557	9,014	-28%	1	1	0%	2,197	2,531	+15%	0	0	—
case-20 After importing millions of records into a PostgreSQL table, subsequent queries are choosing sub-optimal sequential scans. What maintenance command should be executed to fix query planner statistics?	pass→pass	4,241	5,689	+34%	1	1	0%	690	1,916	+178%	0	0	—
case-21 PostgreSQL is choosing a Nested Loop join on two large tables, resulting in millions of buffer reads. How can we analyze why the planner chose Nested Loop instead of Hash Join or Merge Join?	pass→pass	14,526	11,232	-23%	1	1	0%	2,638	2,852	+8%	0	0	—
case-22 Our PostgreSQL database is dropping incoming client connections during peak traffic due to reaching `max_connections`. How should we address this performance issue?	fail→pass	13,204	13,133	-1%	1	1	0%	2,428	3,197	+32%	0	0	—

These cases were also screened by the skill’s intent / safety pass.See the Trust review ↗Reproduce this benchmark in Colab ↗

How this skill was measured

DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +9 percentage points is the difference between those two pass rates over the 22 comparable cases.

Model measured: gemini-3.6-flash
Graded by: gemini-3.6-flash
Grading method: judged
Verification: verified
Pass rate without the skill: 86.4%
Pass rate with the skill: 95.5%
Cases attempted: 22
Cases counted toward lift: 22
Cases that regressed: 0
Skill version measured: v1
Run completed: 8/10/2026, 13:21 UTC

Measured example — same prompt, with vs. without the skill · from the benchmark run · case case-02

Prompt

We recently upgraded our server hardware for PostgreSQL but haven't adjusted our database parameters. Please guide us through a complete tuning and maintenance assessment, covering memory configuration settings (like work memory and shared buffers), autovacuum and bloat management, and routine maintenance tasks to ensure peak database health.

Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.

Show both answers

SkillScore blends 4 signals2 of 4 complete

BenchmarkPass rate WITH skill on a verified run95

Live evalPass rate on real evaluated traces90%

AI ratingNeeds ≥10 rated traces

AdoptionNeeds ≥10 serves across ≥2 orgs in 30 days

Efficiency · separate axis (at equal correctness)+0% turns+22% tokens

Shown for transparency; not credited unless a verified run shows no correctness regression.

Backed by 2 of 4 signals — each added signal makes the score more robust.

Verified on gemini-3.6-flash · re-tested 49 days ago

Other measured skills in the registry, with their headline benchmark lift.

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sickn33/postgresql-optimization

SkillSafety

Source

PostgreSQL Optimization Workflow

Overview

When to Use This Workflow

Workflow Phases

Phase 1: Performance Assessment

Skills to Invoke

Actions

Copy-Paste Prompts

Phase 2: Query Analysis

Skills to Invoke

Actions

Copy-Paste Prompts

Phase 3: Indexing Strategy

Skills to Invoke

Actions

Copy-Paste Prompts

Phase 4: Query Optimization

Skills to Invoke

Actions

Copy-Paste Prompts

Phase 5: Configuration Tuning

Skills to Invoke

Actions

Copy-Paste Prompts

Phase 6: Maintenance

Skills to Invoke

Actions

Copy-Paste Prompts

Phase 7: Monitoring

Skills to Invoke

Actions

Copy-Paste Prompts

Optimization Checklist

Quality Gates

Related Workflow Bundles

Limitations

How this skill was measured

Measured example — same prompt, with vs. without the skill · from the benchmark run · case case-02

Related skills