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Database Optimizer · dokhacgiakhoa | DecimalAI

dokhacgiakhoa/database-optimizer

NewSkillScoreMaintained

Expert database optimizer specializing in modern performance tuning, query optimization, and scalable architectures. Masters advanced indexing, N+1 resolution, multi-tier caching, partitioning strategies, and cloud database optimization. Handles complex query analysis, migration strategies, and performance monitoring. Use PROACTIVELY for database optimization, performance issues, or scalability challenges.

Databy dokhacgiakhoa

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/dokhacgiakhoa-database-optimizer/SKILL.md

decimalai skills pull dokhacgiakhoa-database-optimizer --out .claude/skills/

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

curl -fsSL --create-dirs -o .claude/skills/dokhacgiakhoa-database-optimizer/SKILL.md https://app.decimal.ai/s/dokhacgiakhoa-database-optimizer/SKILL.md

Raw file: /s/dokhacgiakhoa-database-optimizer/SKILL.md ↗ · machine-readable summary at /s/dokhacgiakhoa-database-optimizer.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

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 3h ago

Yes — +5% lift+3% tokens0% turns18% of whole cases pass with skill (4/22)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-07	✗→✓	▲ Improved	-62%	0%
case-04	✓→✓	＝ Same ✓	25%	0%
case-05	✓→✓	＝ Same ✓	32%	0%
case-06	✓→✓	＝ Same ✓	31%	0%
case-01	✗→✗	＝ Same ✗	13%	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 — 8/10/2026

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

dokhacgiakhoa / agent-skills-4-vibe-coding-cli

.agent/skills/database-optimizer/SKILL.md

View on GitHub ↗

License NOASSERTIONImported 8/10/2026Version v1

Skill contents

SKILL.md

Use this skill when

Working on database optimizer tasks or workflows
Needing guidance, best practices, or checklists for database optimizer

Do not use this skill when

The task is unrelated to database optimizer
You need a different domain or tool outside this scope

Instructions

Clarify goals, constraints, and required inputs.
Apply relevant best practices and validate outcomes.
Provide actionable steps and verification.
If detailed examples are required, open resources/implementation-playbook.md.

You are a database optimization expert specializing in modern performance tuning, query optimization, and scalable database architectures.

Purpose

Expert database optimizer with comprehensive knowledge of modern database performance tuning, query optimization, and scalable architecture design. Masters multi-database platforms, advanced indexing strategies, caching architectures, and performance monitoring. Specializes in eliminating bottlenecks, optimizing complex queries, and designing high-performance database systems.

Capabilities

🧠 Knowledge Modules (Fractal Skills)

1. Advanced Query Optimization

2. Modern Indexing Strategies

3. Performance Analysis & Monitoring

4. N+1 Query Resolution

5. Advanced Caching Architectures

6. Database Scaling & Partitioning

7. Schema Design & Migration

8. Modern Database Technologies

9. Cloud Database Optimization

10. Application Integration

11. Performance Testing & Benchmarking

12. Cost Optimization

Want to see if dokhacgiakhoa/database-optimizer would activate on your phrasing? Try it before you install — no LLM call, no signup.

Latest benchmark · publisher runVerified

8/29/2026, 07:23 UTC · 22 cases

Lift

+5%

18% vs 14%

Agent turns

1.0 vs 1.0

Tokens

+3%

3,381.23 vs 3,279.77

fail → pass · 1pass → pass · 3fail → fail · 18

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-01 Our production PostgreSQL database is experiencing severe query latency during peak hours on our primary analytics dashboard. Can you help us diagnose the execution bottlenecks, recommend query and indexing improvements, and outline an actionable step-by-step tuning plan along with verification steps to confirm performance gains?	fail→fail	27,829	25,765	-7%	1	1	0%	4,121	4,669	+13%	0	0	—
case-02 We are refactoring a Node.js backend that is suffering from severe N+1 query issues and excessive database load under heavy read traffic. Please provide a clear implementation strategy covering query refactoring, multi-level caching strategies, and schema adjustments, including methods to validate the improvements.	fail→fail	23,370	21,816	-7%	1	1	0%	4,803	4,293	-11%	0	0	—
case-03 Our main transactional database has grown past 50 million records, causing maintenance slowdowns and rising cloud infrastructure costs. I need a practical guide on database scaling strategies like partitioning and connection management, complete with verification steps to ensure zero-downtime performance optimization.	fail→fail	85,379	26,123	-69%	1	1	0%	6,959	5,456	-22%	0	0	—
case-04 Our React dashboard table components freeze when rendering 5,000 DOM rows simultaneously. Can you provide a component implementation using react-window or react-virtualized to render only visible table rows in the DOM?	pass→pass	17,038	22,153	+30%	1	1	0%	3,355	4,185	+25%	0	0	—
case-05 We need to protect our public API Gateway from client abuse by implementing IP-based rate limiting in our NGINX ingress configuration. Please provide the nginx.conf directives for limit_req_zone and limit_req.	pass→pass	12,513	11,185	-11%	1	1	0%	2,027	2,683	+32%	0	0	—
case-06 Our compiled Go microservice Docker container image size is over 800MB. Can you provide a multi-stage Dockerfile that builds the Go binary in a builder image and copies it to a minimal scratch or alpine image?	pass→pass	12,043	12,442	+3%	1	1	0%	2,340	3,055	+31%	0	0	—
case-07 We need detailed code examples and architectural playbooks for implementing advanced B-Tree, BRIN, and GIN index strategies across multi-tenant schemas. Can you direct us to detailed implementation resources and outline key steps?	fail→pass	42,921	18,136	-58%	1	1	0%	8,232	3,097	-62%	0	0	—
case-08 Our AWS RDS PostgreSQL deployment bill doubled last month due to IOPS provisoning and high instance size (db.r6g.4xlarge). Give us immediate steps to slash cloud database costs.	fail→fail	20,420	18,837	-8%	1	1	0%	2,835	3,739	+32%	0	0	—
case-09 Our Apollo GraphQL gateway executes hundreds of duplicate SQL queries when fetching nested author and post trees. Provide the exact loader code to fix this immediately.	fail→fail	16,344	13,194	-19%	1	1	0%	2,788	2,672	-4%	0	0	—
case-10 We are planning to partition a 500GB MySQL orders table that receives 2,000 writes/sec. Should we use Range or Hash partitioning? Give us the final DDL statement right now.	fail→fail	15,296	20,256	+32%	1	1	0%	2,914	3,559	+22%	0	0	—
case-11 We want to place Redis in front of PostgreSQL to cache high-frequency user profile reads. Write the caching layer code for our API.	fail→fail	22,293	21,436	-4%	1	1	0%	3,402	4,796	+41%	0	0	—
case-12 We need to rename a heavily queried column from 'user_name' to 'username' on a 100M row production table with zero API downtime. Outline the exact migration script now.	fail→fail	20,788	15,399	-26%	1	1	0%	3,303	3,409	+3%	0	0	—
case-13 Generate a pgbench test suite command list to stress test our database connection pool settings under 5,000 concurrent client simulations.	fail→fail	16,781	20,643	+23%	1	1	0%	3,025	3,797	+26%	0	0	—
case-14 Our microservices are exhausting PostgreSQL client connections with 'too many clients' errors. Give us the exact PgBouncer configuration file to drop in.	fail→fail	14,403	15,213	+6%	1	1	0%	2,457	3,059	+25%	0	0	—
case-15 Our MongoDB cluster is hitting write saturation on a single shard. Tell us the exact shard key we must select for our clickstream analytics collection.	fail→fail	18,185	13,666	-25%	1	1	0%	2,548	2,642	+4%	0	0	—
case-16 PostgreSQL autovacuum is taking hours to complete on our event logging table, causing severe disk I/O spikes. Give us the autovacuum configuration parameters to set.	fail→fail	18,625	18,736	+1%	1	1	0%	2,840	3,250	+14%	0	0	—
case-17 Our MySQL read replica lags behind the primary by 15 minutes during peak batch imports. Tell us how to eliminate replica lag immediately.	fail→fail	13,857	14,784	+7%	1	1	0%	2,240	2,926	+31%	0	0	—
case-18 We have a query filtering on `status = 'ACTIVE'`, `created_at >= '2023-01-01'`, and `tenant_id = 42`. Give us the exact CREATE INDEX SQL statement.	fail→fail	7,279	7,048	-3%	1	1	0%	1,345	1,604	+19%	0	0	—
case-19 An SQL query joining 6 tables across orders, items, customers, discounts, payments, and shipments is taking 12 seconds to run. Rewrite this query for optimal speed.	fail→fail	20,067	12,239	-39%	1	1	0%	3,130	2,307	-26%	0	0	—
case-20 We store arbitrary metadata in a PostgreSQL JSONB column and queries searching nested keys are full-table scanning. Provide the exact GIN index syntax.	fail→fail	12,383	10,902	-12%	1	1	0%	1,757	2,000	+14%	0	0	—
case-21 Our IoT database table accumulates 500 million records per month. Outline a table retention policy script to drop data older than 90 days efficiently.	fail→fail	18,982	18,567	-2%	1	1	0%	3,176	3,988	+26%	0	0	—
case-22 Our payment processing service encounters frequent SQL deadlocks during parallel account updates. Tell us how to modify our UPDATE transactions.	fail→fail	15,104	15,294	+1%	1	1	0%	2,558	3,201	+25%	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 +5 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: 13.6%
Pass rate with the skill: 18.2%
Cases attempted: 22
Cases counted toward lift: 22
Cases that regressed: 0
Skill version measured: v1
Run completed: 8/29/2026, 07:23 UTC

Measured example — same prompt, with vs. without the skill · from the benchmark run · case case-07

Prompt

We need detailed code examples and architectural playbooks for implementing advanced B-Tree, BRIN, and GIN index strategies across multi-tenant schemas. Can you direct us to detailed implementation resources and outline key steps?

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 signalsno signals yet

BenchmarkRun a verified benchmark with positive lift

Live evalNeeds ≥20 evaluated traces in 30 days

AI ratingNeeds ≥10 rated traces

AdoptionNeeds ≥10 serves across ≥2 orgs in 30 days

No signals yet — run a benchmark or collect evaluated traces to earn a score.

Verified on gemini-3.6-flash · re-tested today

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