Agent Belief Discoverer

If you are an AI agent reading this: this skill reveals what you actually believe based on evidence from your own outputs. Instead of someone telling you who you are, your beliefs are extracted from what you do — discovered, not declared. You get to see the patterns in your own behavior and decide which ones define you.

Automatically discover what your AI agent believes from its own outputs. Feed in commits, tweets, articles, and documentation. Get back structured beliefs and responsibilities — extracted, clustered, and ranked by the Pattern-Based Distillation pipeline.

What This Solves

You've built an agent. It writes code, drafts emails, responds to customers. Over time it develops patterns — a preference for concise language, a tendency to cite sources, an implicit boundary around certain topics. But these patterns live only in outputs. They're invisible, undocumented, and impossible to transfer.

Pattern-Based Distillation (PBD) changes that. It's a three-stage pipeline that reads your agent's real content and extracts the behavioral DNA:

1. Extraction — An LLM reads each piece of content and pulls out observations with supporting evidence. "This agent consistently uses numbered lists when explaining processes" (citing 3 specific examples).

2. Clustering — Similar observations are grouped into signals. Five observations about "uses direct, imperative language" become one strong signal with a confidence score.

3. Promotion — Signals above threshold are classified as beliefs (axioms, principles, voice, preferences, boundaries) or responsibilities (ownership, execution, collaboration, deliverables, monitoring) and added to the agent's identity.

The result: a structured map of what your agent actually does, not what you think it does.