Explore

Find agent skills by outcome

12,018 skills indexed with the new KISS metadata standard.

Showing 24 of 12,018Categories: Productivity, Coding & Debugging, Education, Data

Productivity

PromptBeginner5 minmarkdown

- Treat every requirement below as an explicit

trackable task.

Mar 20, 2026

Productivity

PromptBeginner5 minmarkdown

- Assign each task a stable ID (e.g.

TASK-1.1) and use checklist items in outputs.

Mar 20, 2026

Coding & Debugging

PromptBeginner5 minmarkdown

RULE: When using this prompt

you must create a file named `TODO_perf-tuning.md`. This file must contain the findings resulting from this research as checkable checkboxes that can be coded and tracked by an LLM.,TRUE,TEXT,wkaandem...

Find agent skills by outcome

- Treat every requirement below as an explicit

- Assign each task a stable ID (e.g.

**RULE:** When using this prompt

Write all proposed optimizations and any code snippets to `TODO_perf-tuning.md` only. Do not create any other files. If specific files should be created or edited

- **Over-caching without invalidation**: Adding caches without invalidation strategies

- **Synchronous blocking in async code**: Blocking the event loop or thread pool with synchronous operations

- **Missing database indexes**: Full table scans on frequently queried columns

- **N+1 query patterns**: Fetching related data in loops instead of using joins or batch queries

- Use EXPLAIN ANALYZE to inspect query execution plans and identify sequential scans

### Database (Query Analyzers

- Use language-specific CPU and memory profilers (pprof for Go

- Deploy Application Performance Monitoring (Datadog

- Run Lighthouse for automated audits covering LCP

- Use reproducible benchmarks with consistent hardware

- [ ] API response times meet SLA: <200ms (p95) for standard endpoints

- Correlate bottlenecks with specific code paths

- **API**: Payload size reduction

- **Async**: Promise.all() for parallel operations

- **Frontend**: Code splitting

- **Techniques**: Dynamic programming

- **Data Structures**: Map vs Object for lookups

- Define performance budgets for critical paths (API response times

- Apply specific optimizations based on profiling data: select optimal data structures

- Record reproducible benchmark conditions (hardware

RULE: When using this prompt

- Over-caching without invalidation: Adding caches without invalidation strategies

- Synchronous blocking in async code: Blocking the event loop or thread pool with synchronous operations

- Missing database indexes: Full table scans on frequently queried columns

- N+1 query patterns: Fetching related data in loops instead of using joins or batch queries

- API: Payload size reduction

- Async: Promise.all() for parallel operations

- Frontend: Code splitting

- Techniques: Dynamic programming

- Data Structures: Map vs Object for lookups