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kangarooking/cangjie-skill

kangarooking/cangjie-skill transforms static knowledge from books, videos, and podcasts into structured, callable AI agent skills using the RIA-TV++ pipeline. The pipeline applies a seven-stage distillation process with triple verification and stress testing to produce independently executable agent skills.

Project overview

It targets the gap between passive content consumption and active agent execution by generating testable, composable skills rather than summary documents.

Project type: AI Agent · Workflow · Prompt Engineering · Learning Resources
Use cases: Learning & Education
Deployment: Refer to project documentation
License: MIT

Best for

Developers looking to convert long-form passive text content into structured, independently testable agent skills for coding and workflow environments.

Key capabilities

Processes raw text through a seven-stage sequence that includes extraction, triple verification, and stress testing to produce structured skills.
Outputs a repository of multiple independent skills accompanied by an index, overview, digest, glossary, and test prompts rather than a single summary file.
Installs tested skills directly into Claude Code or Cursor skills directories to make them callable.

Limitations and risks

Video distillation operations require a separate video-downloader skill to provide transcription.
The triple verification system typically yields a pass rate of 25-50% during the skill extraction process.

Getting started

Setup difficulty levels, reasons, and the initial path to first success are not documented.

Alternatives and comparisons

Provides a continuously updated collection of over 100 standardized persona skills and multi-agent frameworks for diverse scenarios, compared to generating skills from specific source content.
A TypeScript-first framework for building LLM applications with typed signatures and structured generation, rather than a pipeline for distilling books or videos into callable skills.
A multi-model chat interface for red teaming and cognition research, supporting local models and input perturbation, rather than distilling content into structured agent skills.

Project comparisons

Evidence and sources

GitHub project description
README
README
README
README

Reviewed at: 2026-07-16T22:26:59Z

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