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 ```bash 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. - Do not 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.