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

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

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \\
  --content "Description of what was decided/solved" \\
  --type decision \\
  --tags bdistill-behavioral-xray <relevant-tags>

Agent Team Collaboration

• This skill can be invoked by the orchestrator agent via intelligent routing.
• In Agent Teams mode, results are shared via Qdrant shared memory for cross-agent context.
• In Subagent mode, this skill runs in isolation with its own memory namespace.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns