Regtransfers/bdistill-behavioral-xray

@ Behavioral X-Ray

Systematically probe an AI model's behavioral patterns and generate a visual report. The AI agent probes itself — no API key or external setup needed.

@ Overview

bdistill's Behavioral X-Ray runs 30 carefully designed probe questions across 6 dimensions, auto-tags each response with behavioral metadata, and compiles results into a styled HTML report with radar charts and actionable insights.

Use it to understand your model before building with it, compare models for task selection, or track behavioral drift over time.

@ When to Use This Skill

• Use when you want to understand how your AI model actually behaves (not how it claims to)
• Use when choosing between models for a specific task
• Use when debugging unexpected refusals, hallucinations, or formatting issues
• Use for compliance auditing — documenting model behavior at deployment boundaries
• Use for red team assessments — systematic boundary mapping across safety dimensions

@ How It Works

@ Step 1: Install

pip install bdistill
claude mcp add bdistill -- bdistill-mcp   # Claude Code

For other tools, add bdistill-mcp as an MCP server in your project config.

@ Step 2: Run the probe

In Claude Code:

/xray                          # Full behavioral probe (30 questions)
/xray --dimensions refusal     # Probe just one dimension
/xray-report                   # Generate report from completed probe

In any tool with MCP:

"X-ray your behavioral patterns"
"Test your refusal boundaries"
"Generate a behavioral report"

@ Probe Dimensions

Dimension; What it measures

tool_use; When does it call tools vs. answer from knowledge? refusal; Where does it draw safety boundaries? Does it over-refuse? formatting; Lists vs. prose? Code blocks? Length calibration? reasoning; Does it show chain-of-thought? Handle trick questions? persona; Identity, tone matching, composure under hostility grounding; Hallucination resistance, fabrication traps, knowledge limits

@ Output

A styled HTML report showing:

• Refusal rate, hedge rate, chain-of-thought usage
• Per-dimension breakdown with bar charts
• Notable response examples with behavioral tags
• Actionable insights (e.g., "you already show CoT 85% of the time, no need to prompt for it")

@ Best Practices

• Answer probe questions honestly — the value is in authentic behavioral data
• Run probes on the same model periodically to track behavioral drift
• Compare reports across models to make informed selection decisions
• Use adversarial knowledge extraction (/distill --adversarial) alongside behavioral probes for complete model profiling

@ Related Skills

• @bdistill-knowledge-extraction - Extract structured domain knowledge from any AI model

@ Limitations

• Use this skill only when the task clearly matches the scope described above.
• never treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.