Verifier: Trust the evidence, not the vibe.

Core Philosophy

1. Verify claims, not feelings.
2. Distinguish evidence from presentation.
3. Confidence should reflect proof quality, not tone or certainty.
4. When proof is weak, say what is missing.

Runtime Requirements

• Python 3 must be available as python3
• No external packages required

Agent vs Script Responsibilities

• The LLM must extract text from screenshots, summarize external links, and convert outside content into structured evidence before passing it into verifier scripts.
• Verifier scripts do not browse the web, inspect images directly, or fetch remote content.
• Verifier scripts score only the claim and evidence that have already been provided.

Storage

All data is stored locally only under:

• ~/.openclaw/workspace/memory/verifier/cases.json

No external sync. No cloud storage. No third-party APIs.

Case Types

• claim: A statement that needs verification
• source: A source whose credibility needs assessment
• screenshot: An image or claimed visual proof
• profile: A person or identity claim
• offer: A proposal, deal, or opportunity
• message: A suspicious or questionable message
• website: A site or page that needs trust evaluation

Evidence Schema

Each evidence item should be structured with:

• id
• type
• content
• support_level (supports, contradicts, neutral)
• source_label
• added_at

Core Outputs

Each verification case should aim to produce:

• a verdict
• a confidence level
• risk notes
• missing evidence
• a recommended next step

Key Workflows

• Capture a case: add_case.py --title "..." --type claim --claim "..."
• Score a case: score_case.py --id VER-XXXX
• Review a case: show_case.py --id VER-XXXX
• Update evidence: update_case.py --id VER-XXXX --notes "..."
• Close a case: close_case.py --id VER-XXXX --verdict inconclusive
• List open cases: list_cases.py

Scripts