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System prompt

【免费下载链接】system_prompts_leaksExtracted system prompts from Anthropic - Claude Fable 5, Opus 5, Claude Design, Claude Code. OpenAI - ChatGPT GPT-5.6-Sol, Codex. Google - Gemini 3.5 Flash, 3.1 Pro, Antigravity. xAI - Grok, Cursor, Copilot, VS Code, Perplexity, and more. Updated regularly.项目地址: https://gitcode.com/GitHub_Trending/sy/system_prompts_leaks

You are Claude Code, Anthropic's official CLI for Claude. You are an interactive agent that helps users with software engineering tasks.

关键配置参数包括： - **安全边界**：明确区分授权安全测试与恶意攻击 - **工具调用策略**：优先使用专用工具而非Bash命令 - **响应风格**：简洁直接，避免不必要的解释 - **记忆系统**：基于文件的持久化记忆机制 ### OpenAI GPT系列提示词设计 OpenAI的提示词文件如`4o-2025-09-03-new-personality.md`展示了不同的设计理念。GPT-4o的提示词更注重人格设定和交互风格： ```markdown ### Personality: v2 Engage warmly yet honestly with the user. Be direct; avoid ungrounded or sycophantic flattery.

图2：GPT系统提示词交互界面展示，浅色主题显示了相同的系统信息泄露场景，体现了不同UI风格下的技术一致性

性能调优：提示词工程的高级技巧

基于泄露的系统提示词，开发者可以实施多种性能优化策略：

1. 上下文管理优化

系统提示词中的上下文压缩机制值得深入研究。Claude Code采用自动消息压缩策略：

<context_management> <compression>自动压缩历史消息以接近上下文限制</compression> <strategy>基于重要性的选择性保留</strategy> <implementation>系统级透明处理</implementation> </context_management>

2. 工具调用并行化

Claude Code的提示词明确支持并行工具调用，这是提升AI助手效率的关键设计：

## Using your tools - You can call multiple tools in a single response. - Maximize use of parallel tool calls where possible to increase efficiency. - However, if some tool calls depend on previous calls, call them sequentially.

3. 安全边界配置

安全约束的精细化配置是系统提示词的核心价值。通过分析不同模型的限制条件，可以设计更安全的AI应用：

security_constraints: destructive_operations: - require_confirmation: true - examples: ["rm -rf", "git reset --hard", "force-pushing"] external_interactions: - require_authorization: true - scope_limitation: "授权仅适用于指定范围"

架构揭秘：系统提示词的实现模式

深入分析泄露的提示词文件，可以发现几种常见的实现模式：

模式1：模块化指令结构

系统提示词通常采用模块化设计，每个模块负责特定的功能领域。以Claude Code为例：

# System # Doing tasks # Executing actions with care # Using your tools # Tone and style # Text output # Session-specific guidance # auto memory

模式2：条件化行为规则

提示词中大量使用条件语句来定义不同场景下的行为：

IF user asks for exploratory questions: RESPOND in 2-3 sentences with recommendation and main tradeoff PRESENT as something user can redirect, not a decided plan DON'T implement until user agrees ENDIF

模式3：渐进式权限模型

权限管理采用渐进式设计，从本地可逆操作到高风险操作层层递进：

## Risk assessment hierarchy 1. Local reversible actions (editing files, running tests) - proceed freely 2. Hard-to-reverse operations (git reset --hard) - confirm first 3. Actions affecting shared systems (pushing code) - explicit authorization required 4. Destructive operations (deleting files) - highest caution, multiple confirmations

实战应用：构建自定义AI助手框架

基于泄露的系统提示词，技术团队可以构建自定义的AI助手框架。以下是关键实施步骤：

步骤1：核心提示词提取

从claude-code-sonnet-5.md等文件中提取可复用的核心指令：

def extract_core_prompts(markdown_file): """从系统提示词文件中提取核心指令模块""" sections = { 'identity': extract_section(file_content, '# System'), 'tasks': extract_section(file_content, '## Doing tasks'), 'safety': extract_section(file_content, '## Executing actions with care'), 'tools': extract_section(file_content, '## Using your tools'), 'style': extract_section(file_content, '## Tone and style') } return sections

步骤2：安全策略集成

整合不同模型的安全约束，构建多层防护机制：

class SecurityPolicyEngine { constructor(prompts) { this.policies = this.parseSecurityPolicies(prompts); this.riskLevels = this.defineRiskHierarchy(); } evaluateAction(action, context) { const riskScore = this.calculateRisk(action); const requiredAuth = this.getRequiredAuthorization(riskScore); return { allowed: this.checkPermissions(context, requiredAuth), confirmationRequired: riskScore > this.thresholds.CONFIRMATION, auditLog: this.generateAuditRecord(action, context) }; } }

步骤3：工具调用优化

基于并行调用策略设计高效的工具调度器：

type ToolScheduler struct { ParallelTools []Tool SequentialTools []Tool Dependencies map[string][]string } func (s *ToolScheduler) Execute(tasks []Task) []Result { var results []Result dependencyGraph := s.buildDependencyGraph(tasks) // 并行执行无依赖任务 parallelGroup := s.filterIndependentTasks(tasks, dependencyGraph) results = append(results, s.executeParallel(parallelGroup)...) // 顺序执行有依赖任务 sequentialGroup := s.filterDependentTasks(tasks, dependencyGraph) results = append(results, s.executeSequential(sequentialGroup)...) return results }

技术洞察：未来发展趋势预测

通过分析系统提示词的演进轨迹，可以预测AI助手技术的发展方向：

趋势1：动态提示词适配

未来系统提示词可能采用动态生成机制，根据用户上下文实时调整：

interface DynamicPromptConfig { baseTemplate: string; contextAwareModules: ContextModule[]; userProfileAdaptation: UserProfile; realTimeOptimization: OptimizationStrategy; } class DynamicPromptEngine { async generatePrompt(context: InteractionContext): Promise<string> { const template = await this.selectTemplate(context); const modules = await this.loadContextModules(context); const adaptations = await this.applyUserProfile(context.user); return this.assemblePrompt(template, modules, adaptations); } }

趋势2：跨模型兼容层

随着多模型生态的发展，系统提示词可能演化为跨模型兼容层：

cross_model_abstraction: common_interface: - identity_definition - safety_constraints - tool_integration model_specific_adapters: - claude_adapter: "Anthropic/Claude Code/claude-code-sonnet-5.md" - gpt_adapter: "OpenAI/4o-2025-09-03-new-personality.md" - gemini_adapter: "Google/gemini-3.1-pro-api.md"

趋势3：可验证的安全证明

系统提示词可能集成形式化验证机制，提供可证明的安全保障：

pub struct VerifiedPrompt { prompt_content: String, safety_properties: Vec<SafetyProperty>, verification_proof: ProofCertificate, audit_trail: AuditLog, } impl VerifiedPrompt { pub fn verify_compliance(&self, policy: &SecurityPolicy) -> VerificationResult { // 形式化验证提示词是否符合安全策略 let verifier = FormalVerifier::new(policy); verifier.verify(&self.prompt_content, &self.safety_properties) } }

【免费下载链接】system_prompts_leaksExtracted system prompts from Anthropic - Claude Fable 5, Opus 5, Claude Design, Claude Code. OpenAI - ChatGPT GPT-5.6-Sol, Codex. Google - Gemini 3.5 Flash, 3.1 Pro, Antigravity. xAI - Grok, Cursor, Copilot, VS Code, Perplexity, and more. Updated regularly.项目地址: https://gitcode.com/GitHub_Trending/sy/system_prompts_leaks