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/wangqiqi/cursor-ai-rules/evolution-automationgit clone --depth 1 https://github.com/wangqiqi/cursor-ai-rulesWhat 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 | $0.02159 |
| Opus 5 | $0.00000 | $0.01079 |
| Sonnet 5 | $0.00000 | $0.00432 |
| Haiku 4.5 | $0.00000 | $0.00216 |
Grade A, and why
evolution-automation 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.
How it starts
The opening of the file, as written. The whole thing — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🤖 自动化演进系统 (Automated Evolution System)
版本: v4.3.0 | 最后更新: {{GENERATION_TIME}} | 作者: wangqiqi (https://github.com/wangqiqi)
⚠️ 执行原则
MUST 遵循以下自动化演进准则：
- MUST 基于数据驱动而非主观判断
- NEVER 在未经测试的情况下应用自动优化
- ALWAYS 监控自动化演进的影响
- DO NOT 让自动化系统修改核心宪法规则
- MUST 保持人工最终决策权
- ALWAYS 记录自动化决策的依据
🎯 核心机制 (Core Mechanisms)
自动化演进系统通过持续感知项目状态和用户行为，实现规则的智能优化和自动调整。新增规则演进建议系统，基于数据驱动提供具体的演进方向和实施建议。
📊 自动化感知接口 (Automated Perception Interface)
自动化演进系统基于直接的数据源感知，实现规则的自动优化。
数据源 (Data Sources)
- 项目状态: 直接调用
../../core/env-perception.sh脚本进行项目变化感知 - 用户行为: 基于协作历史和使用模式分析
- 系统指标: 集成
@evolution-governance的监控数据
感知触发 (Perception Triggers)
系统通过以下方式获取感知数据：
- 定时调用
../../core/env-perception.sh脚本进行项目状态扫描 - 分析用户协作模式和反馈数据
- 集成
@evolution-governance的实时监控指标
🤖 自动化优化算法 (Automated Optimization Algorithms)
核心算法 (Core Algorithms)
强化学习优化 (Reinforcement Learning)
# 基于历史数据训练规则参数优化模型
class RuleOptimizer:
def __init__(self, perception_data, governance_metrics):
self.perception = perception_data
self.metrics = governance_metrics
def optimize_rules(self):
# 分析感知数据变化趋势
trends = self.analyze_trends()
# 计算最优参数组合
optimal_params = self.calculate_optimal(trends)
# 生成优化建议
suggestions = self.generate_suggestions(optimal_params)
return suggestions
规则演进建议引擎 (Rule Evolution Suggestion Engine) ⭐ 新增
基于感知数据和使用模式，智能生成具体的规则演进建议：
演进分析维度 (Evolution Analysis Dimensions)
interface EvolutionAnalyzer {
// 项目成熟度分析
analyzeProjectMaturity(perception: PerceptionData): MaturityLevel
// 用户协作模式分析
analyzeCollaborationPatterns(interactions: InteractionHistory[]): CollaborationPattern
// 技术栈演进趋势
analyzeTechStackEvolution(techData: TechStackData): EvolutionTrend
// 规则使用效率分析
analyzeRuleEfficiency(usage: RuleUsageData): EfficiencyMetrics
// 生成演进建议
generateEvolutionSuggestions(analysis: AnalysisResult): EvolutionSuggestion[]
}
// 演进建议生成示例
const analyzer = new EvolutionAnalyzer()
// 1. 综合分析
const maturity = analyzer.analyzeProjectMaturity(perceptionData)
// 返回: {level: 'growing', confidence: 0.85}
const patterns = analyzer.analyzeCollaborationPatterns(interactionHistory)
// 返回: {style: 'iterative', team_size: 'small', frequency: 'daily'}
const techTrends = analyzer.analyzeTechStackEvolution(techStackData)
// 返回: {direction: 'modernization', priority: 'high', timeline: '3_months'}
const efficiency = analyzer.analyzeRuleEfficiency(ruleUsageData)
// 返回: {optimal_rules: ['eslint', 'system_info'], underutilized: ['templates']}
// 2. 生成综合建议
const suggestions = analyzer.generateEvolutionSuggestions({
maturity, patterns, techTrends, efficiency
})
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 · 248 lines · 0 tokens per session scan A 4a78cf55d8a9
evolution-automation is a cursor rule published in the GitHub repository wangqiqi/cursor-ai-rules (15 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,159 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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