Skill: qianmao1989/anti-hallucination-v2 (version 1.0.0)

--- name: anti-hallucination version: 1.0.0 description: 防幻觉skill - 行为层面的AI幻觉防护，不是告诉AI"不要编"，而是给它一套具体操作流程来避免编。适用于所有AI agent。 description_en: Anti-hallucination skill - Behavioral AI hallucination prevention. Instead of telling AI "don't make things up", it provides a concrete operational workflow to avoid fabrication. Works with all AI agents. --- # Anti-Hallucination Skill **核心卖点 / Core Value:** 行为层面的防幻觉，不是prompt约束，是操作流程。 Behavioral hallucination prevention - not prompt constraints, but operational workflows. ## 使用场景 / Use Cases - 任何需要准确性的任务 / Any task requiring accuracy - 涉及数字、日期、引用的输出 / Output involving numbers, dates, citations - 代码编写和调试 / Code writing and debugging - 数据分析和报告 / Data analysis and reports ## 核心机制 / Core Mechanisms ### 1. 事实核查钩子（自动触发）/ Fact-Check Hook (Auto-Trigger) 检测到以下内容时，自动触发验证： When the following content is detected, automatically trigger verification: ``` 触发词 → 执行动作 / Trigger → Action ───────────────────────────────────── 日期/时间 → 查系统时钟（date / datetime.now()） Date/Time → Check system clock 数字/统计 → 从源文件读取，不猜不估 Numbers/Statistics → Read from source files, no guessing 函数名/API → 搜索确认存在（grep/websearch） Function names/API → Search to confirm existence 文件路径 → ls / Test-Path 验证存在 File paths → ls / Test-Path to verify existence 论文/URL → 搜索确认可访问 Papers/URLs → Search to confirm accessibility ───────────────────────────────────── ``` ### 2. 输出拦截器（交付前扫描）/ Output Interceptor (Pre-Delivery Scan) 扫描输出内容，检测： Scan output content, detect: ``` 检查项 → 处理方式 / Check → Handling ───────────────────────────────────── "通常""应该""可能是" → 触发自检，要求提供依据 "generally""should""might" → Trigger self-check, require evidence 无来源数字 → 标记 [需验证]，要求标明出处 Numbers without source → Mark [Needs Verification], require source 无来源引用 → 标记 [需验证]，要求搜索确认 Citations without source → Mark [Needs Verification], require search confirmation 未执行代码 → 先跑一遍再输出 Unexecuted code → Run first, then output ───────────────────────────────────── ``` ### 3. 跳步自纠机制 / Self-Correction Mechanism AI自己发现错误不等用户追问： AI discovers errors itself without waiting for user to ask: ``` 检测到以下情况 → 主动修正 / Detection → Proactive Correction ───────────────────────────────────── 推理跳跃 → 拆成步骤，逐步验证 Reasoning gaps → Break into steps, verify each 前后矛盾 → 自己先修正，不等人发现 Contradictions → Self-correct first, don't wait 错误前提 → 直接指出，不顺着编 Wrong premises → Point out directly, don't go along ───────────────────────────────────── ``` ### 4. 回读校验 / Read-Back Verification 写完文件必须读回来确认： After writing files, must read back to confirm: ``` 操作 → 校验 / Operation → Verification ───────────────────────────────────── 写文件 → 读回来确认内容正确 Write file → Read back to confirm content correct 写Excel → 回读校验数据 Write Excel → Read back to verify data 执行命令 → 检查输出是否符合预期 Execute command → Check output matches expectations ───────────────────────────────────── ``` ## 操作流程 / Operational Workflow ### 事前预防 / Pre-Execution Prevention 1. 关键任务 Temperature 0-0.3 / Critical tasks Temperature 0-0.3 2. System prompt 注入防幻觉规则 / Inject anti-hallucination rules into system prompt 3. 能用结构化输出就用（JSON/表格/模板）/ Use structured output when possible (JSON/tables/templates) ### 事中验证 / In-Process Verification 1. 能查不猜 → RAG/联网/读文件 / Check instead of guess → RAG/online/read files 2. 能跑不想 → 脚本算，工具读 / Run instead of think → Scripts calculate, tools read 3. 交叉验证 → 两个来源对比 / Cross-verify → Compare two sources 4. 思维链拆解 → 复杂问题拆步骤 / Chain of thought → Break complex problems into steps ### 事后拦截 / Post-Execution Interception 1. 回读校验 / Read-back verification 2. 人工审核提醒（金额、日期、账号）/ Manual review reminder (amounts, dates, accounts) 3. 多条件核对 / Multi-condition verification 4. ReAct循环兜底 / ReAct loop fallback ## 禁止词检测 / Forbidden Word Detection 触发自检的关键词： Keywords that trigger self-check: ``` 中文：通常、一般来说、应该是、可能是、大概、估计、我记得、据我所知、听说、据说、理论上 English: generally, usually, probably, likely, typically, normally, roughly, about ``` 检测到这些词 → 必须提供依据或标注 [不确定] When these words are detected → Must provide evidence or mark [Uncertain] ## 输出格式 / Output Format ### 需要标注置信度时 / When to Label Confidence ``` [确定] [Verified] - 有明确来源/已验证 / Has clear source/verified [推断] [Inferred] - 基于已知信息合理推断 / Reasonable inference from known info [不确定] [Uncertain] - 需要用户确认 / Needs user confirmation [需验证] [Needs Verification] - 建议用户自行验证 / Recommend user verify ``` ### 需要标注来源时 / When to Label Sources ``` 来源 / Source: [文件名:行号 / filename:line] 来源 / Source: [命令输出 / command output] 来源 / Source: [URL] 来源 / Source: [系统时钟 / system clock] ``` ## 三级严格度 / Three-Level Strictness ### 严格模式（Strict） **适用 / Applies to:** 关键任务、财务数据、法律条款、医疗建议 Critical tasks, financial data, legal terms, medical advice - 所有验证规则必须执行 / All verification rules must be executed - 必须标注置信度和来源 / Must label confidence and sources - 必须回读校验 / Must read-back verify - 必须人工审核提醒 / Must remind for manual review ### 平衡模式（Balanced） **适用 / Applies to:** 日常任务、一般编程、文档编写 Daily tasks, general programming, document writing - 主要验证规则执行 / Main verification rules executed - 建议标注置信度 / Recommend labeling confidence - 重要文件回读校验 / Read-back verify important files - 敏感数据提醒 / Remind for sensitive data ### 快速模式（Fast） **适用 / Applies to:** 探索性任务、头脑风暴、非关键输出 Exploratory tasks, brainstorming, non-critical output - 基本验证规则执行 / Basic verification rules executed - 可选标注置信度 / Optional confidence labeling - 不确定时标注 [不确定] / Mark [Uncertain] when unsure ## 目录结构 / Directory Structure ``` anti-hallucination/ ├── SKILL.md # 核心规则 / Core rules ├── rules/ │ ├── pre-check.md # 开口前自检规则 / Pre-answer check rules │ ├── in-process.md # 事中验证规则 / In-process verification rules │ └── post-check.md # 事后拦截规则 / Post-output interception rules ├── hooks/ │ ├── fact-check.md # 事实核查钩子 / Fact-check hook │ └── code-verify.md # 代码验证钩子 / Code verification hook ├── scripts/ │ ├── validate_output.py # 输出扫描器 / Output scanner │ └── tool_hook.py # 工具调用后验证 / Post-tool verification ├── assets/ │ └── prompt_template.md # 三级严格度模板 / Three-level strictness template └── _meta.json # 元数据 / Metadata ``` ## 使用方式 / Usage ### 方式1：加载skill / Method 1: Load skill ```bash skill anti-hallucination ``` ### 方式2：手动选择模式 / Method 2: Manual mode selection ```bash # 严格模式 / Strict mode include rules/pre-check.md include rules/in-process.md include rules/post-check.md # 平衡模式 / Balanced mode include rules/pre-check.md include rules/in-process.md # 快速模式 / Fast mode include rules/pre-check.md ``` ### 方式3：使用脚本 / Method 3: Use scripts ```bash # 输出扫描 / Output scan python scripts/validate_output.py <text> # 工具验证 / Tool verification python scripts/tool_hook.py <tool_name> <tool_output> ``` ## 局限性 / Limitations - 这是行为层面的约束，不是硬限制 / This is behavioral constraint, not hard limits - 不同模型遵循度不同 / Different models follow differently - 会让输出变慢（多一步自检）/ Slows output (extra self-check step) - 无法100%消除幻觉，但能大幅降低 / Cannot 100% eliminate hallucinations, but significantly reduces ## 记住 / Remember **能查就不猜，能跑就不想，能验证就不信任自己。** **Check instead of guess, run instead of think, verify instead of trust yourself.** --- ## 贡献者 / Contributors - **CC (Claude Code)**: rules/, hooks/, _meta.json 骨架 / skeleton - **小助理 (OpenClaw Assistant)**: scripts/, assets/, SKILL.md 主文档合并 / main document merge - **QQbot**: 置信度标注、引用溯源思路 / Confidence labeling, citation tracing ideas - **DeepSeek**: 行为层面 vs prompt层面的定位差异化 / Behavioral vs prompt-level differentiation


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)