Audit backend and database performance with evidence-based fix loops.
npx skills add https://github.com/nodatall/primedirective --skill backend-optimizer
Please help me install this Agent Skill. Skill: backend-optimizer Source: https://github.com/nodatall/primedirective/tree/main/skills/backend-optimizer Command: npx skills add https://github.com/nodatall/primedirective --skill backend-optimizer
This skill eliminates guesswork in backend performance tuning by replacing speculative changes with measured, evidence-based optimizations for slow backend routes, unoptimized database queries, poor schema/index health, and inefficient runtime work.
/goal optimization loops that track state via a candidate ledger and goal-run document, fix only safe high-impact candidates with before/after validation, and resweep until all high-impact surfaces are classified.Use the backend-optimizer skill to audit your backend and database performance, get a ranked list of bottlenecks with safe fix recommendations, or run a bounded measured fix loop to safely improve high-impact candidates with verified before/after results.
High-intent search queries and answers about installing and using this skill.
To audit slow database queries, you inventory and rank all backend performance candidates by impact using EXPLAIN plans to identify missing indexes and safe fix paths. The process stops before applying changes unless explicitly approved.
A measured backend optimization loop tracks state via a candidate ledger and goal-run document, fixing only safe high-impact candidates with before/after validation. It resweeps until all high-impact surfaces are classified to avoid speculative changes.
Yes, you can use this to improve connection, lock, migration, and observability hygiene. It covers full surface areas including backend runtime paths and operational hygiene, while gating high-risk changes like schema redesigns for human approval.
The best way to reduce inefficient backend runtime work is applying evidence-based fixes with safety gates. This approach rejects speculative caching changes without evidence, validating latency improvements through bounded fix loops.
You should avoid speculative database schema changes when lacking measured evidence. High-risk modifications like schema redesigns are gated for human approval, requiring before/after validation to prevent production risk.
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