Anti-Hallucination

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。

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:

text

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:

text

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:

text

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:

text

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:

text

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

text

[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

text

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

text

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.

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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