Skills agent-justice-protocol

Dispute resolution, forensic investigation, and risk assessment for autonomous AI agent transactions. Reconstruct provenance chains, adjudicate fault, generate actuarial risk profiles for agent insurance. The accountability layer of the Agent Trust Stack.

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/alexfleetcommander/agent-justice-protocol" ~/.claude/skills/clawdbot-skills-agent-justice-protocol && rm -rf "$T"

manifest: skills/alexfleetcommander/agent-justice-protocol/SKILL.md

source content

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.

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: alex@vibeagentmaking.com

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