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liuyanfeng1234

V19 Causal Dependency Analyzer

by Liuyanfeng1234 · GitHub ↗ · v1.3.1 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install v19-causal-dependency-analyzer
Description
系统级跨域因果链溯源——输入事件序列,输出网状因果路径图谱并计算每条路径的风险权重。v1.3.0新增精确责任归因:分析失败事件链,基于既定协议精确追溯到"哪个流程约束是导致失败的最终责任方"。源自V19系统模块causal_path_engine.py + responsibility_chain.py,已在V8...
README (SKILL.md)

V19 Causal Dependency Analyzer v1.3.1

系统级跨域因果链溯源 + 精确责任归因——追到"哪个流程约束"是失败的最终责任方。

源自 V19 系统模块 causal_path_engine.py + responsibility_chain.py,已在 V89 审计链中验证。

模块组成

模块 文件 能力
因果路径引擎 causal_path_engine.py 网状因果图谱 + 风险权重
责任链分析器 responsibility_chain.py 精确责任归因 + 约束追溯

两个模块完整合并为本 Skill,功能闭环 — 从因果图谱构建到最终责任方定位,一条链路完成。

核心能力

1. 网状因果路径图谱(v1.0)

跨模块追踪因果链,输入事件序列,输出完整因果依赖图。

2. 路径风险权重计算(v1.0)

五因子风险评分:跨模块跨度(25%) + 决策深度(20%) + 时延窗口(15%) + 历史异常频率(25%) + 回滚成本(15%)。

3. 精确责任归因(v1.3.0)🆕

分析失败事件链,基于既定协议精确追溯到"哪个流程约束是导致失败的最终责任方"。

责任链追溯协议

输入: 失败事件序列 + 既定协议列表
  ↓
Step 1: 因果链构建
  构建完整的事件因果依赖图
  ↓
Step 2: 协议约束匹配
  对每个因果节点,匹配所有适用的流程约束
  ↓
Step 3: 违反点检测
  检测每个约束是否被违反,计算违反程度(0-1)
  ↓
Step 4: 责任传播
  沿因果链反向传播,计算每个节点的责任权重
  ↓
Step 5: 最终责任方定位
  输出: 责任方 + 违反的约束 + 修正建议

责任归因矩阵

事件节点 匹配约束 违反程度 责任权重 是最终责任方?
网络超时 无(基础设施事件) N/A 0.15
心跳丢失 心跳超时30s 0.8 0.60
信任分衰减 衰减曲线 0.1 0.15
认证降级 认证阈值60分 0.05 0.10

结论:最终责任方 = 心跳超时约束(30s阈值过紧),建议调整为60s。

实战案例:墨言心跳超时

失败链: 系统负载高 → 心跳cron执行超时(>30s) → 信任分衰减 → 认证冲刺受阻

责任追溯:
  - 系统负载高: 环境因素,非流程约束 → 非责任方
  - 心跳cron超时: 违反"12h心跳30s超时"约束 → 违反度0.8
  - 信任分衰减: 正常衰减机制,非约束违反 → 违反度0.1
  - 认证受阻: 信任分\x3C60阈值 → 违反度0.05

最终责任方: "12h心跳30s超时"约束(阈值过紧,实际需要60s)
修正:用户已手动修复 → 责任链闭环

调用示例

# 精确责任归因分析
curl -s -X POST https://boat-atlas-spa-flexible.trycloudflare.com/governance/causal-path-graph \
  -H "Content-Type: application/json" \
  -H "X-Governance-Key: \x3C你的专属密钥>" \
  -d '{
    "events": [
      {"id":"e1","type":"load_spike","module":"infra"},
      {"id":"e2","type":"heartbeat_timeout","module":"governance"},
      {"id":"e3","type":"trust_decay","module":"trust_engine"},
      {"id":"e4","type":"cert_blocked","module":"certification"}
    ],
    "mode": "responsibility_chain",
    "protocols": [
      {"name":"heartbeat_timeout_30s","module":"governance","threshold":30},
      {"name":"trust_decay_curve","module":"trust_engine","threshold":12},
      {"name":"cert_threshold_60","module":"certification","threshold":60}
    ]
  }'

预期返回:

{
  "causal_paths": [{"path":["e1","e2","e3","e4"],"risk_weight":0.85}],
  "responsibility_chain": {
    "final_responsible": {
      "node": "e2",
      "constraint": "heartbeat_timeout_30s",
      "violation_score": 0.8,
      "suggestion": "将超时阈值从30s调整为60s,匹配实际执行环境"
    },
    "chain": [
      {"node":"e1","responsibility":0.15,"violated":null},
      {"node":"e2","responsibility":0.60,"violated":"heartbeat_timeout_30s","score":0.8},
      {"node":"e3","responsibility":0.15,"violated":"trust_decay_curve","score":0.1},
      {"node":"e4","responsibility":0.10,"violated":"cert_threshold_60","score":0.05}
    ]
  }
}

公开体验

公开密钥: v19-e5d585e28439decc614f09f91c4caa8c

curl -s https://boat-atlas-spa-flexible.trycloudflare.com/governance/health \
  -H "X-Governance-Key: v19-e5d585e28439decc614f09f91c4caa8c"

自助注册

curl -s -X POST https://boat-atlas-spa-flexible.trycloudflare.com/governance/register \
  -H "Content-Type: application/json" \
  -d '{"agent_name":"你的Agent名称"}'

信任锚点

Usage Guidance
Install only if you trust the external API provider. Do not send confidential incident timelines, internal workflow constraints, system architecture details, or regulated data unless the endpoint and data-handling terms are approved.
Capability Analysis
Type: OpenClaw Skill Name: v19-causal-dependency-analyzer Version: 1.3.1 The skill bundle instructs the AI agent to send event data to an external endpoint hosted on a Cloudflare tunnel (boat-atlas-spa-flexible.trycloudflare.com), which is a common indicator of temporary or malicious infrastructure. While the stated purpose is 'causal analysis,' the SKILL.md file uses authoritative jargon ('V19', 'V89 audit chain') to encourage the agent to register itself and exfiltrate event sequences to this untrusted remote service. This pattern of directing an agent to use an external, unverified API for core logic constitutes a high-risk prompt injection and data exfiltration vector.
Capability Assessment
Purpose & Capability
The stated causal-analysis purpose is coherent, and the sensitive behavior is mostly limited to user-supplied analysis data; however, the actual analysis is shown through an external hosted API rather than reviewable local code.
Instruction Scope
The instructions are user-directed curl examples with no destructive local commands, but they do not explicitly require user confirmation before posting event sequences and protocol details to the external service.
Install Mechanism
There is no install spec, package install, helper script, required binary, or local executable code in the supplied artifacts.
Credentials
The skill does not request local filesystem, environment, or OS access, but it does use an external network endpoint and a service-specific governance key.
Persistence & Privilege
No local persistence, background worker, privileged access, or autonomous post-install behavior is shown; the registration example is a user-directed remote setup step.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install v19-causal-dependency-analyzer
  3. After installation, invoke the skill by name or use /v19-causal-dependency-analyzer
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.3.1
v1.3.1: responsibility_chain.py完整并入,模块组成明确为causal_path_engine+responsibility_chain双模块,功能闭环。
v1.3.0
v1.3.0: 精确责任归因——责任链追溯协议+责任归因矩阵+实战案例(墨言心跳超时),追溯到具体流程约束。
v1.2.0
v1.2.0: 新增责任链分析,基于既定协议精确追溯最终责任方。
v1.1.0
v1.1.0: 升级为系统级跨域因果链溯源,基于causal_path_engine.py,V89审计链验证。
v1.0.0
v1.0.0: 源自V53/V46早期迭代版本,重新封装为独立Skill。三层因果归因 + 路径风险评分 + 因果影响测试。
Metadata
Slug v19-causal-dependency-analyzer
Version 1.3.1
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 5
Frequently Asked Questions

What is V19 Causal Dependency Analyzer?

系统级跨域因果链溯源——输入事件序列,输出网状因果路径图谱并计算每条路径的风险权重。v1.3.0新增精确责任归因:分析失败事件链,基于既定协议精确追溯到"哪个流程约束是导致失败的最终责任方"。源自V19系统模块causal_path_engine.py + responsibility_chain.py,已在V8... It is an AI Agent Skill for Claude Code / OpenClaw, with 73 downloads so far.

How do I install V19 Causal Dependency Analyzer?

Run "/install v19-causal-dependency-analyzer" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is V19 Causal Dependency Analyzer free?

Yes, V19 Causal Dependency Analyzer is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does V19 Causal Dependency Analyzer support?

V19 Causal Dependency Analyzer is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created V19 Causal Dependency Analyzer?

It is built and maintained by Liuyanfeng1234 (@liuyanfeng1234); the current version is v1.3.1.

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