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.
git clone --depth 1 https://github.com/hamzaamjad/cursor-rulesWrote 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/rules/hamzaamjad/cursor-rules/301-constraint-optimization)<a href="https://agentmods.dev/rules/hamzaamjad/cursor-rules/301-constraint-optimization"><img src="https://agentmods.dev/badge/rules/hamzaamjad/cursor-rules/301-constraint-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/rules/hamzaamjad/cursor-rules/301-constraint-optimization"><img src="https://agentmods.dev/badge/rules/hamzaamjad/cursor-rules/301-constraint-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 | $0.00000 | $0.00970 |
| Opus 5.5 | $0.00000 | $0.00388 |
| Sonnet 5.5 | $0.00000 | $0.00194 |
| Haiku 4.5 | $0.00000 | $0.00097 |
Grade A, and why
301-constraint-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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Constraint Optimization Patterns
Purpose
Guide optimization of system constraints based on the discovery that multiple AI agents independently converged on ~60% as the optimal constraint level across diverse domains. This rule provides heuristics for finding optimal constraints in new systems.
The 60% Principle
Multiple independent discoveries revealed that optimal performance often emerges around 60% constraint level:
- Memory Allocation: 60% allocation for data processing systems
- Test Coverage: 60% entropy/chaos in testing strategies
- Technical Debt: Retaining 60% of "debt" maximizes velocity
- Biological Rhythms: 60% of baseline (e.g., sleep) can optimize certain outputs
This suggests a universal principle: moderate constraints catalyze optimal performance.
Optimization Strategies
1. Starting Heuristic
When optimizing any system constraint without prior knowledge:
- Begin testing at 60% as the initial hypothesis
- Test range: 40%, 50%, 60%, 70%, 80%
- Look for inverse U-curve performance patterns
2. Golden Ratio Consideration
The 60% principle (0.6) is remarkably close to the golden ratio (0.618):
- Consider testing at exactly 61.8% for systems with aesthetic or natural components
- Check Fibonacci ratios: 38.2%, 50%, 61.8%, 78.6%
3. Domain-Specific Variations
While 60% is common, expect slight variations:
- Physical systems: May optimize at 55-65%
- Cognitive tasks: Often exactly at 60%
- Biological systems: Can range 50-70% depending on circadian factors
- Creative work: Strong convergence at 60%
4. Testing Methodology
Use "superposition testing" when possible:
- Test multiple constraint levels simultaneously
- Run parallel experiments to reduce time
- Look for resonance patterns across related systems
5. Fractal Properties
The principle shows self-similarity:
- If 60% is optimal, test 36% (60% of 60%)
- Look for harmonic relationships at multiples/fractions
Implementation Guidance
For Resource Allocation
# Example: Memory allocation
total_memory = system.available_memory()
optimal_allocation = total_memory * 0.6 # Start here
test_range = [0.4, 0.5, 0.6, 0.7, 0.8] # Expand if needed
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.
- 1mo ago First seen · 116 lines · 0 tokens per session scan A 1c5697049aba
301-constraint-optimization is a cursor rule published in the GitHub repository hamzaamjad/cursor-rules (2 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 970 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 2026-08-31.
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