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* feat: add bdistill behavioral-xray and knowledge-extraction skills Two MCP-powered skills for AI model analysis: - behavioral-xray: Self-probe across 6 dimensions with HTML reports - knowledge-extraction: Domain knowledge extraction via Ollama for LoRA training Repository: https://github.com/FrancyJGLisboa/bdistill Install: pip install bdistill * fix: remove curl|sh install command, update skills for current capabilities - Removed pipe-to-shell Ollama install (flagged by docs security policy) - Replaced with link to https://ollama.com - Updated knowledge-extraction to reflect in-session mode, adversarial validation, tabular ML data, and compounding knowledge base - Updated behavioral-xray with red-team and compliance use cases - Removed ChatML/fine-tuning language — output is reference data
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3.2 KiB
name, description, category, risk, source, date_added, author, tags, tools
| name | description | category | risk | source | date_added | author | tags | tools | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bdistill-behavioral-xray | X-ray any AI model's behavioral patterns — refusal boundaries, hallucination tendencies, reasoning style, formatting defaults. No API key needed. | ai-testing | safe | community | 2026-03-20 | FrancyJGLisboa |
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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