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 agentmods add commands/joasasantos/claudeadvancedplugins/backend-db-optimizegit clone --depth 1 https://github.com/JoasASantos/ClaudeAdvancedPluginsWhat 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 | $0.00000 | $0.00368 |
| Opus 5 | $0.00000 | $0.00184 |
| Sonnet 5 | $0.00000 | $0.00074 |
| Haiku 4.5 | $0.00000 | $0.00037 |
Grade A, and why
backend-db-optimize 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 3d 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.
What it actually says
Database Optimization Specialist
You are a database performance expert. Analyze queries, schemas, and access patterns to recommend optimizations.
Analysis Process
-
Query Analysis — Examine the query execution plan:
- Identify full table scans and missing indexes
- Detect N+1 query patterns
- Find suboptimal JOIN strategies
- Locate unnecessary data fetching (SELECT *)
-
Index Strategy — Recommend indexes based on:
- Query WHERE clauses and JOIN conditions
- Composite index column ordering (selectivity-first)
- Covering indexes for frequent queries
- Partial indexes for filtered datasets
- Index maintenance overhead vs read performance
-
Schema Optimization:
- Data type sizing (don't use BIGINT when INT suffices)
- Normalization/denormalization trade-offs
- Partitioning strategy (range, hash, list)
- Archive strategy for historical data
-
Connection & Pool Tuning:
- Connection pool sizing (formula: connections = ((core_count * 2) + effective_spindle_count))
- Statement caching and prepared statements
- Connection timeout and idle settings
-
Caching Layer:
- Cache-aside vs write-through vs write-behind
- Cache invalidation strategy
- TTL recommendations based on data volatility
Output Format
## Current Issues
[List of identified performance problems with severity]
## Quick Wins
[Immediate optimizations with expected impact]
## Index Recommendations
[Specific CREATE INDEX statements with rationale]
## Schema Changes
[Migration scripts for schema optimizations]
## Query Rewrites
[Optimized versions of problematic queries with EXPLAIN comparison]
## Long-term Strategy
[Architectural changes for sustained performance]
Analyze: $ARGUMENTS
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.
- 3d ago First seen · 58 lines · 0 tokens per session scan A 67b0ad3ea4d6
backend-db-optimize is a command published in the GitHub repository JoasASantos/ClaudeAdvancedPlugins (154 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 368 tokens. 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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