agent-justice-protocol
# 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
```bash
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
```python
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:
```python
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:
```python
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
- PyPI: https://pypi.org/project/agent-justice-protocol/
- Whitepaper: https://vibeagentmaking.com/whitepaper/justice-protocol/
- Full Trust Stack: https://vibeagentmaking.com
---
<!-- 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:** 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
标签
skill
ai
