py-dianoia 0.2.1

Open-source expert reasoning framework distillation tool

Dianoia AI 格物致知

Open-source expert reasoning framework distillation tool

English | 中文

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What is Dianoia?

Dianoia (格物致知) distills expert reasoning patterns from public artifacts — documents, code reviews, design docs — into profile.yaml and SKILL.md formats that any AI agent can consume.

Unlike knowledge extraction, Dianoia captures how experts think: how they define problems, judge quality, prioritize trade-offs, and decide when "good enough" is acceptable.

Inspired by The Relic Condition research on reasoning architecture extraction.

Install

pip install py-dianoia

Requires Python 3.11+

Quick Start

# 1. Initialize a project
dianoia init my-expert

# 2. Add corpus files to my-expert/corpus/
cp ~/expert-docs/*.md my-expert/corpus/

# 3. Extract reasoning patterns
dianoia extract my-expert

# 4. Build output files (profile.yaml + SKILL.md)
dianoia build my-expert

# 5. Validate outputs
dianoia validate my-expert

V2 Features

Interactive TUI for Expert Reasoning

dianoia tui init — First-run setup wizard to configure your LLM provider, model, API key, and expert directory. Saves to ~/.config/dianoia/config.yaml.

dianoia tui — Launch an interactive terminal session to reason with a distilled expert. Select an expert from your configured experts directory and start a phase-guided reasoning conversation.

Interactive Model Switching

Switch providers and models mid-session with /model. Supports 11 providers across international and China markets.

File Reading and Modification

Mention a file path in chat and the expert auto-reads it. After discussion, use /apply to let the expert write changes back — with preview, confirmation, and automatic .bak backup.

Supported Providers

Provider	Models	Env Var
OpenAI	gpt-4o, o3, o4-mini	OPENAI_API_KEY
Anthropic	Claude Sonnet 4, Opus 4	ANTHROPIC_API_KEY
Google	Gemini 2.5 Pro	GOOGLE_API_KEY
Groq	llama, mixtral	GROQ_API_KEY
Mistral	mistral-large, mistral-medium	MISTRAL_API_KEY
xAI	grok	XAI_API_KEY
Together AI	open-source models	TOGETHER_API_KEY
DeepSeek	deepseek-chat, deepseek-reasoner	DEEPSEEK_API_KEY
Zhipu/智谱	glm-4-flash, glm-5.1	ZHIPU_API_KEY
Moonshot/月之暗面	moonshot-v1, kimi-k2	MOONSHOT_API_KEY
Alibaba Qwen/阿里百炼	qwen-turbo, qwen-plus, qwen-max	DASHSCOPE_API_KEY

V2 TUI Quick Start

# First time setup — configure provider, model, API key, experts directory
dianoia tui init

# Distill an expert (existing CLI workflow)
dianoia init my-expert
cp ~/docs/*.md my-expert/corpus/
dianoia extract my-expert
dianoia build my-expert

# Chat with your distilled expert
dianoia tui

Architecture

Corpus Files → ExtractionPipeline (8 layers) → IR → ProfileMapper → profile.yaml
                                                     → SkillRenderer  → SKILL.md

8-Layer Extraction Method

#	Layer	Extracts
1	Ontological	Problem boundaries, classification systems
2	Teleological	Goals, purposes, end-driven reasoning
3	Methodological	Procedures, systematic approaches
4	Axiological	Values, priorities, decision criteria
5	Epistemological	Knowledge sources, evidence standards
6	Conditional	Context-dependent decisions
7	Priority	Trade-off ordering (engineering-specific)
8	Pragmatic	"Good enough" thresholds, practical shortcuts

Stability Filtering

Features only survive if they appear in ≥2 independent texts AND ≥3 distinct contexts. This filters noise and ensures distilled patterns represent genuine expert consensus.

Output Formats

profile.yaml

Structured data for workflow engines (e.g., kitchen-ent):

version: "1.0"
sections:
  identity:
    - key: domain
      value: engineering
      confidence: 0.85
      sources: [doc1.md, doc2.md]

SKILL.md

Follows the Agent Skills open standard:

---
name: "my-expert"
description: "Expert reasoning distilled from ..."
---

# My Expert

## Extracted Reasoning Patterns
...

Language Overlay

Distill once in a language-agnostic base profile, then overlay language-specific customizations:

base-profile.yaml (language-agnostic skeleton)
  + overlay-python.yaml
  + overlay-rust.yaml
  + overlay-go.yaml

Documentation

• Distillation Principle
• Architecture
• CLI Reference
• Language Overlay
• Profile Deliverable
• SKILL Deliverable
• Contributing

Specifications

• IR Schema — JSON Schema for the Intermediate Representation
• Profile Spec — profile.yaml format specification
• SKILL Spec — SKILL.md format specification

Development

git clone https://github.com/bigmoon-dev/dianoia-ai.git
cd dianoia-ai
pip install -e ".[dev]"
pytest

License

Apache-2.0