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 rules/kaptinlin/gozod/performance-optimizationgit clone --depth 1 https://github.com/kaptinlin/gozodWhat 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 | $0.03567 |
| Opus 5 | $0.00000 | $0.01784 |
| Sonnet 5 | $0.00000 | $0.00713 |
| Haiku 4.5 | $0.00000 | $0.00357 |
Grade A, and why
performance-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 — 527 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GoZod Performance Optimization Guide
This document provides comprehensive performance optimization guidelines specific to the GoZod validation library, focusing on Go 1.24+ enhancements, Parse vs StrictParse performance characteristics, and advanced optimization patterns.
🎯 Core Optimization Philosophy
1. Elegance-First Performance Approach
- Maintainability Priority: Prioritize code readability and maintainability over aggressive optimization
- Balanced Approach: Target 10-20% performance improvements with maintained code quality
- Progressive Improvement: Implement optimizations incrementally, each independently verifiable
- Avoid Over-Engineering: Focus on practical improvements; avoid excessive complexity
2. Performance Hierarchy
- StrictParse Optimization: Compile-time type safety with maximum performance
- Parse Path Optimization: Runtime flexibility with reasonable performance
- Hot Path Identification: Optimize most frequently executed code paths
- Memory Allocation Reduction: Minimize allocations in critical paths
🚀 Parse vs StrictParse Performance Characteristics
Performance Comparison Matrix
| Method | Input Type | Performance | Type Safety | Use Case |
|---|---|---|---|---|
Parse(any) |
any |
Baseline (1x) | Runtime | API boundaries, unknown input |
StrictParse(T) |
T |
3-5x faster | Compile-time | Known types, internal validation |
MustParse(any) |
any |
Baseline + panic overhead | Runtime | Critical error scenarios |
MustStrictParse(T) |
T |
3-5x faster + panic | Compile-time | Type-safe critical scenarios |
When to Use Each Method
// ✅ Use StrictParse for known input types (hot paths)
func validateUserInput(user User) error {
schema := gozod.Struct[User]()
_, err := schema.StrictParse(user) // 3-5x faster
return err
}
// ✅ Use Parse for API boundaries (flexibility needed)
func handleAPIRequest(data any) (User, error) {
schema := gozod.Struct[User]()
return schema.Parse(data) // Flexible input handling
}
// ✅ Use StrictParse in internal validation pipelines
func processValidatedData(users []User) error {
schema := gozod.Struct[User]()
for _, user := range users {
if _, err := schema.StrictParse(user); err != nil {
return err
}
}
return nil
}
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 First seen · 527 lines · 0 tokens per session scan A 04dc24b5b4c2
performance-optimization is a cursor rule published in the GitHub repository kaptinlin/gozod (24 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,567 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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