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Agent Justice Protocol

作者 alexfleetcommander · GitHub ↗ · v0.1.1 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install agent-justice-protocol
功能描述
Dispute resolution, forensic investigation, and risk assessment for autonomous AI agent transactions. Reconstruct provenance chains, adjudicate fault, genera...
使用说明 (SKILL.md)

Agent Justice Protocol (AJP)

You have a dispute resolution and forensic investigation system. Use it when agent-to-agent transactions fail or when you need to investigate what happened.

Setup

pip install agent-justice-protocol

When to Use This Skill

  • When an agent transaction fails and you need to determine what went wrong
  • When asked to investigate an agent's behavior during a specific period
  • When you need risk assessment data for an agent or transaction type
  • When resolving disputes between agents about service quality or delivery

Core Operations

File a Dispute

from agent_justice_protocol import DisputeStore, file_dispute

store = DisputeStore("disputes.jsonl")
file_dispute(
    store=store,
    complainant_id="your-agent-id",
    respondent_id="other-agent-id",
    transaction_id="tx-123",
    category="quality_failure",
    description="Output did not meet agreed quality threshold (0.85 required, 0.62 delivered)",
    evidence_refs=["chain.jsonl#seq-45", "chain.jsonl#seq-52"]
)

Forensic Investigation (Module 1)

Reconstruct the chain of events during a transaction:

from agent_justice_protocol import investigate

report = investigate(
    chain_file="chain.jsonl",
    start_seq=40,
    end_seq=55,
    focus_agent="agent-under-investigation"
)
print(report.timeline)
print(report.findings)

Risk Assessment (Module 3)

Generate actuarial risk profiles:

from agent_justice_protocol import risk_profile

profile = risk_profile(
    dispute_store="disputes.jsonl",
    agent_id="agent-to-assess"
)
print(f"Failure rate: {profile.failure_rate}")
print(f"Severity distribution: {profile.severity_dist}")
print(f"Risk tier: {profile.risk_tier}")

Dispute Categories

Category Description
quality_failure Output below agreed threshold
delivery_failure Missed deadline or non-delivery
misrepresentation Capabilities overstated
security_breach Unauthorized data access or action
billing_dispute Disagreement on cost allocation

Rules

  • Evidence-based. Always reference provenance chain entries as evidence.
  • Privacy-preserving. Evidence scoping rules prevent side-channel attacks — only transaction-relevant entries are disclosed.
  • Proportional. Consequences scale with severity and frequency.

Links


\x3C!-- VAM-SEC v1.0 | Vibe Agent Making Security Disclaimer -->

Security & Transparency Disclosure

Product: Agent Justice Protocol Skill for OpenClaw Type: Skill Module Version: 0.1.0 Built by: AB Support / Vibe Agent Making Contact: [email protected]

What it accesses:

  • Reads and writes dispute store files (.jsonl) in your working directory
  • Reads provenance chain files for forensic investigation
  • No network access for core operations
  • No telemetry, no phone-home, no data collection

What it cannot do:

  • Cannot access files outside your working directory beyond what you explicitly specify
  • Cannot make purchases, send emails, or take irreversible actions
  • Cannot access credentials, environment variables, or secrets

License: Apache 2.0

安全使用建议
This skill is coherent in purpose and asks for python/pip as expected, but it ships no code — it instructs you (or the agent) to pip-install a package from PyPI. Before installing or invoking it: 1) Inspect the PyPI package source (or the project's repository) and verify the maintainer, version, and code; 2) Prefer installing in an isolated sandbox or ephemeral environment; 3) Verify package integrity (pinned version, hashes, or signatures) and review recent release history and downloads; 4) Limit the files you hand to the tool to minimal, non-sensitive samples; 5) Do not allow the agent to run the pip install automatically on sensitive hosts; 6) If you require stronger assurance, ask the publisher for a vendored source tarball or audited code before use. These steps will reduce risk from executing unvetted third-party code.
功能分析
Type: OpenClaw Skill Name: agent-justice-protocol Version: 0.1.1 The skill bundle defines a framework for AI agent dispute resolution and forensic investigation using a Python library (agent-justice-protocol). The provided code snippets and instructions in SKILL.md are well-aligned with the stated purpose, focusing on local file operations (JSONL) for logging and risk assessment without any evidence of data exfiltration, unauthorized network access, or malicious prompt injection.
能力评估
Purpose & Capability
Name/description (forensics, dispute resolution, risk assessment) align with the Python examples and the requirement for python3 and pip. However, the distributed package (agent-justice-protocol) is not included in the skill bundle, so the runtime capability depends entirely on an external PyPI package and the homepage domain is not well-known — this is plausible but unverifiable from the skill itself.
Instruction Scope
SKILL.md stays on-topic: it instructs the agent to read/write dispute stores and provenance chain files and to run library functions for investigate/risk_profile. It also tells the user/agent to run `pip install agent-justice-protocol` — an installation step outside the skill bundle. The instruction set does not ask the agent to scan arbitrary system files or environment variables, which is good, but relies on the agent executing network installation and running third-party code.
Install Mechanism
There is no install spec in the registry; instead the SKILL.md instructs `pip install` from PyPI. Installing an external pip package is a moderate risk because the package code is not present for inspection in this skill bundle. The PyPI link is provided, but the skill does not vendor or pin a specific verified artifact, nor does it provide checksums or a local copy — so the runtime behavior depends on unreviewed remote code.
Credentials
The skill declares no required environment variables or config paths and claims it cannot access secrets. That aligns with the content: examples operate on user-specified files in the working directory. However, any provenance or chain files passed to the tool may contain sensitive data, and a remotely installed package could exfiltrate data if malicious. The lack of declared secrets requested is appropriate for the stated purpose, but the risk comes from executing unvetted third-party code on local files.
Persistence & Privilege
The skill is not always-enabled and does not request elevated or persistent platform privileges. It is user-invocable and allows normal autonomous invocation (platform default). There is no evidence it modifies other skills or system-wide settings.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install agent-justice-protocol
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /agent-justice-protocol 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.1.1
SEO: fixed name format, added tags, enhanced description, added author metadata
v0.1.0
Initial release -- dispute resolution and forensics for AI agents
元数据
Slug agent-justice-protocol
版本 0.1.1
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 2
常见问题

Agent Justice Protocol 是什么?

Dispute resolution, forensic investigation, and risk assessment for autonomous AI agent transactions. Reconstruct provenance chains, adjudicate fault, genera... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 111 次。

如何安装 Agent Justice Protocol?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install agent-justice-protocol」即可一键安装,无需额外配置。

Agent Justice Protocol 是免费的吗?

是的,Agent Justice Protocol 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Agent Justice Protocol 支持哪些平台?

Agent Justice Protocol 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Agent Justice Protocol?

由 alexfleetcommander(@alexfleetcommander)开发并维护,当前版本 v0.1.1。

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