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 RaNDoM6913/claude-code-superkit --skill postgresql-optimizationgit clone --depth 1 https://github.com/RaNDoM6913/claude-code-superkitWrote 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/random6913/claude-code-superkit/postgresql-optimization)<a href="https://agentmods.dev/skills/random6913/claude-code-superkit/postgresql-optimization"><img src="https://agentmods.dev/badge/skills/random6913/claude-code-superkit/postgresql-optimization/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/random6913/claude-code-superkit/postgresql-optimization"><img src="https://agentmods.dev/badge/skills/random6913/claude-code-superkit/postgresql-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00046 | $0.01666 |
| Opus 5.5 | $0.00018 | $0.00666 |
| Sonnet 5.5 | $0.00009 | $0.00333 |
| Haiku 4.5 | $0.00005 | $0.00167 |
Grade A, and why
postgresql-optimization 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 yesterday.
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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PostgreSQL Optimization
Workflow for diagnosing and fixing PostgreSQL performance — query tuning, indexes, EXPLAIN analysis, server configuration, and maintenance.
Use this skill when
- Optimizing slow PostgreSQL queries
- Designing or reviewing indexing strategies
- Analyzing performance and identifying bottlenecks
- Tuning server configuration
- Managing production maintenance (VACUUM, ANALYZE, bloat)
- Setting up monitoring and alerting
Do not use this skill when
- You need schema design guidance (select a relevant DB skill from the runtime's available catalog; this installation does not supply a skill named
postgresql) - The bottleneck is at the application layer, not the database
- You need a DB-agnostic optimization guide
Workflow
- Identify the performance problem: slow query, missing index, config, or maintenance
- Measure current state with
EXPLAIN ANALYZEbefore any change - Apply targeted fix at the right layer (index, query, config, maintenance)
- Verify improvement with a second
EXPLAIN ANALYZE - Monitor over time to confirm the fix holds under load
Phase 1: Performance Assessment
- Check PostgreSQL version and available features
- Review config:
shared_buffers,work_mem,effective_cache_size,autovacuum - Identify slow queries via
pg_stat_statementsor slow query log - Analyze CPU, memory, disk I/O
- Map bottleneck category: scan type, join strategy, I/O, locking, vacuum lag
Phase 2: Query Analysis (EXPLAIN)
- Read node types:
Seq Scan,Index Scan,Index Only Scan,Hash Join,Nested Loop,Merge Join - Compare
estimated vs actualrow counts — large mismatch = stale statistics (runANALYZE) - Find expensive operations: high cost nodes, high actual rows, large loops
- Identify opportunities: missing index, suboptimal join order, unnecessary sorts
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT) SELECT ...;
Phase 3: Indexing Strategy
| Pattern | Index type |
|---|---|
| Equality + range on 1 column | B-tree |
| Multi-column equality | Composite B-tree, most selective first |
| JSONB containment | GIN |
| Full-text search | GIN on tsvector |
| Range overlap, scheduling | GiST on range type |
Hot subset (WHERE status='active') |
Partial index |
| Avoid heap reads for extra columns | Covering index with INCLUDE |
| Time-series, naturally ordered | BRIN |
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.
- yesterday Changed · +1 lines 05c8d07909f4
- 1mo ago First seen · 159 lines · 46 tokens per session scan A c1a5bd699fc0
postgresql-optimization is a skill published in the GitHub repository RaNDoM6913/claude-code-superkit (3 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 1,666 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
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