Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add nimadorostkar/Claude-Skills-collection --skill database-performancegit clone --depth 1 https://github.com/nimadorostkar/Claude-Skills-collectionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/database-performance)<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/database-performance"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/database-performance/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/database-performance"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/database-performance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.01363 |
| Opus 5 | $0.00019 | $0.00681 |
| Sonnet 5 | $0.00008 | $0.00273 |
| Haiku 4.5 | $0.00004 | $0.00136 |
Grade A, and why
database-performance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database Performance
Purpose
Make the database fast by finding what it is actually spending time on. Database performance work fails when it starts from intuition; it succeeds when it starts from EXPLAIN ANALYZE and the slow-query log.
When to Use
- An endpoint is slow and the database is suspected.
- CPU or I/O on the database server is saturated.
- Lock waits, timeouts, or deadlocks in production.
- Planning for a tenfold increase in data volume.
Capabilities
- Query profiling and execution-plan analysis.
- Index strategy: what to add, and what to remove.
- Lock contention and deadlock diagnosis.
- Connection pool sizing.
- Denormalization, materialization, and partitioning as scaling tools.
Inputs
- The slow query log, or the top queries by total execution time.
- Actual execution plans, with row counts.
- Table sizes, index sizes, and hardware.
Outputs
- The identified bottleneck, with evidence.
- A change — index, query rewrite, or schema — and its measured effect.
- Monitoring so the regression is caught next time.
Workflow
- Rank by total time, not by mean — The query taking 20ms and running 50,000 times per minute is the problem. The 3-second report that runs hourly is not.
- Read the plan —
EXPLAIN (ANALYZE, BUFFERS). Look for: a sequential scan on a large table, an estimate that differs from the actual by more than 10x, and a nested loop over many rows. - Fix the biggest thing — Usually a missing index, an N+1 from the application, or a query that fetches far more rows than it uses.
- Check the locks — If queries are fast in isolation but slow in production, the problem is contention, not the plan. Look at lock waits and long-running transactions.
- Size the pool correctly — More connections is not more throughput. Beyond the point where the database is saturated, additional connections increase latency for everyone.
- Re-measure under production-like load — A query that is fast on a warm cache with 10,000 rows tells you nothing about 10 million.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 111 lines · 38 tokens per session scan A 99221ab2c4f8
database-performance is a skill published in the GitHub repository nimadorostkar/Claude-Skills-collection (26 stars, last pushed 23d ago), licensed MIT. It adds 38 tokens to every session and 1,363 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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