Introduction
Nuwa Skill is an open-source framework for extracting structured thinking patterns from a person's written or spoken content and packaging them as reusable AI agent skills. It targets anyone who wants to capture expertise — their own or someone else's — in a format that LLM agents can directly consume and apply.
What Nuwa Skill Does
- Analyzes a corpus of content (articles, transcripts, posts) to identify recurring mental models and decision heuristics
- Extracts communication patterns including tone, structure, and rhetorical preferences
- Generates a structured skill file that encodes the person's reasoning style as agent instructions
- Supports multiple input formats including text files, URLs, and transcript exports
- Produces skills compatible with Claude Code, Cursor, and other agent harnesses that accept markdown-based skill definitions
Architecture Overview
Nuwa Skill operates as a multi-stage pipeline. The ingestion layer normalizes diverse input formats into plain text chunks. An analysis stage uses LLM calls to identify patterns across the corpus — recurring frameworks, decision criteria, writing conventions, and domain-specific vocabulary. A synthesis stage consolidates these patterns into a coherent skill definition with structured sections for context, triggers, instructions, and examples. The final output is a markdown file conforming to common agent skill formats. The pipeline is implemented in Python and uses configurable LLM backends for the analysis and synthesis steps.
Self-Hosting & Configuration
- Clone the repository and install dependencies with pip; runs on any system with Python 3.10+
- Configure your preferred LLM provider (OpenAI, Anthropic, or compatible APIs) via environment variables
- Point the tool at a directory of source content or provide URLs for web-based content
- Adjust extraction parameters such as chunk size and analysis depth in the configuration file
- Output skill files can be placed directly into
.claude/skills/or equivalent directories for immediate use
Key Features
- Content-agnostic extraction works with blog posts, podcast transcripts, forum replies, and long-form writing
- Produces portable skill definitions that work across multiple agent platforms
- Iterative refinement mode lets you review and adjust extracted patterns before finalizing
- Supports batch processing of large content libraries for prolific authors
- Open architecture allows adding custom analysis modules for domain-specific pattern extraction
Comparison with Similar Tools
- Custom GPTs (OpenAI) — require manual prompt writing; Nuwa Skill automates pattern extraction from existing content
- Claude Projects — let you upload reference docs for context; Nuwa Skill goes further by distilling actionable instructions from the content
- Fabric (Daniel Miessler) — provides pre-built prompt patterns; Nuwa Skill generates personalized patterns from individual content
- MemGPT / Letta — focus on long-term memory for agents; Nuwa Skill focuses on capturing reasoning style rather than facts
- Prompt engineering by hand — time-consuming and inconsistent; Nuwa Skill systematizes the extraction process
FAQ
Q: What kind of content works best as input? A: Content where the author explains their reasoning — blog posts with opinions, podcast transcripts with discussions, and advisory writing — yields the richest skill extractions.
Q: Does it work with non-English content? A: Yes, as long as the underlying LLM supports the language. The extraction pipeline is language-agnostic; quality depends on the LLM's proficiency in that language.
Q: How is this different from just uploading documents to an AI chat? A: Uploading documents provides raw context. Nuwa Skill distills that context into structured instructions — triggers, decision rules, and output formats — that an agent can follow without re-reading the source material.
Q: Can I merge skills from multiple people? A: The tool generates one skill per corpus. You can run it on separate content sets and use multiple skills simultaneously in agents that support skill stacking.