cangjie-skill is a free, open source ai development platforms project written in Python and released under MIT. It has 12,008 GitHub stars, 1,413 forks and 23 open issues, and was last pushed 1 months ago. On this registry it ranks #56 of 155 tracked projects in AI Development Platforms, with 5 head-to-head comparisons available. It gained 1,237 stars over the last 30 tracked days.

What is cangjie-skill?

Cangjie Skill is an MIT-licensed Python tool that distills methodologies from books, long-form videos, and podcasts into callable AI Skills, built for developers and knowledge workers who assemble agent workflows on OpenClaw, Claude Code, or DeepSeek Harness.

What it is

Cangjie Skill sits in the AI & Machine Learning / AI Development Platforms category. It is written in Python, licensed under MIT, currently at version 2.5.0, and uses the RIA-TV++ method. The project takes high-value long-form sources and distills them into executable Agent Skills rather than passive notes, and its topic list covers agent-workflows, ai-skills, automation, book-to-skill, knowledge-distillation, prompt-engineering, skill-generator, and templates. It targets three agent platforms: OpenClaw, Claude Code, and DeepSeek Harness.

The concrete problem it solves is the gap between consuming a book, video, or podcast and having something an agent can actually invoke. The pipeline produces a Capability Bundle first — stable capability cards and metadata — before any installable output is compiled, so extraction and delivery stay separate. That replaces the manual work of hand-authoring skills and prompts from long-form source material, and it makes the resulting skills deterministic in shape rather than improvised per session.

Key capabilities

  • Two deterministic delivery modes: compile a single router-style Skill (single) or a compact pack containing a router plus promoted standalone Skills (pack).
  • Capability Bundle as the single source of truth: extraction yields stable capability cards and metadata before any installable output is compiled.
  • A unified local toolchain in scripts/cangjie.py covering diagnostics, compilation, output replanning, incremental updates, repair, rollback, evaluation, and benchmarking.
  • Safer evolution through content-addressed preprocessing, source diffs, impact analysis, transactional patches, edit detection, snapshots, and rollback.
  • Task-first validation that retains complete procedures and formulas explained in a single source location, with output scoring that counts missing runs and checks numeric values and units.
  • Compiled Skills can carry declared scripts and text templates.
  • Registry v2 and website support expose output mode and capability counts without breaking existing Registry v1 entries.

Who uses it and how

  • Agent builders on OpenClaw, Claude Code, or DeepSeek Harness who need reusable Skills derived from books, long-form videos, or podcasts instead of one-off prompts.
  • Maintainers of evolving skill packs who rely on edit detection, source diffs, impact analysis, transactional patches, snapshots, and rollback to update a bundle without breaking it.
  • DeepSeek Harness users who install the bundled adapter from the release tarball and serve it through the web profile.
  • Contributors who browse Skill Packs and detail pages on cangjie-skill.com and submit contributions there, while the GitHub repository remains the sole source for code, methodology, and templates.

Getting started

Download the generic Skill ZIP cangjie-skill-2.5.0.zip, verify its SHA256 checksum, extract it, and install the complete cangjie-skill/ directory; check BUILD_INFO.json for the source commit and refresh date. DeepSeek Harness users instead place dsh-cangjie-skill-2.5.0.tgz in ~/.dsh/packages, verify it with shasum -a 256 -c, then add it with dsh plugin --profile web add and start dsh web.

How it compares

The facts name no paid products or comparable tools that this project replaces, so it stands alone in this registry. Its nearest relationships are the agent platforms it plugs into — OpenClaw, Claude Code, and DeepSeek Harness — rather than any competing distillation tool.

When to use it — and when not to

A self-hoster must run the local toolchain in scripts/cangjie.py and manage refreshes by hand: the original tag is unchanged, so GitHub's automatic source archives do not contain the 2026-09-13 refresh, and existing users must download the refreshed package again. The official website handles browsing, navigation, and contribution entry only, so anyone expecting a hosted, no-install pipeline should look elsewhere. The README also reads heavily as release notes, which makes first-time orientation harder than the feature set warrants.

project readme (upstream, from github) — read inline

简体中文 · English · 日本語

Cangjie Skill

Distill methodologies from books, long-form videos, and podcasts into callable AI Skills

License: MIT Version: 2.5.0 Method: RIA--TV++ Platform: OpenClaw Platform: Claude Code Platform: DeepSeek Harness

Finish reading, watching, or listening—and leave with a methodology you can invoke.

Official Website

🌐 Visit the Cangjie Skill official website

The website provides visual Skill Pack browsing, a beginner-friendly usage guide, Skill detail pages, and a contribution submission entry. This GitHub repository remains the sole source for cangjie-skill code, methodology, and templates; the website provides presentation, navigation, and usage guidance.

What's New in v2.5.0

  • Capability Bundle as the single source of truth: extraction produces stable capability cards and metadata before any installable output is compiled.
  • Two deterministic delivery modes: compile one router-style Skill (single) or a compact pack with a router plus promoted standalone Skills (pack).
  • A unified local toolchain: scripts/cangjie.py now covers diagnostics, compilation, output replanning, incremental updates, repair, rollback, evaluation, and benchmarking.
  • Safer evolution: content-addressed preprocessing, source diffs, impact analysis, transactional patches, edit detection, snapshots, and rollback are included.
  • Registry v2 and website support: output mode and capability counts are visible without breaking existing Registry v1 entries.

See the v2.5.0 release notes and changelog for the complete scope and migration notes.

2026-09-13 refresh (still v2.5.0): task-first validation now retains complete procedures and formulas explained in a single source location. Output scoring counts missing runs and checks numeric values/units; compiled Skills can carry declared scripts and text templates. Download the refreshed generic Skill ZIP · SHA256. Extract it and install the complete cangjie-skill/ directory. Existing users must download the refreshed package; check BUILD_INFO.json for the source commit and refresh date. The original tag is unchanged, so GitHub's automatic source archives do not contain this refresh.

DeepSeek Harness Plugin

cangjie-skill also provides a standalone installation package for DeepSeek Harness. The adapter layer is bundled in the Release package, so no platform-specific wrapper files are added to this repository.

After installing DeepSeek Harness, download the v2.5.0 package and checksum, verify it, then install from the local tarball:

mkdir -p ~/.dsh/packages
curl -fL "https://github.com/kangarooking/cangjie-skill/releases/download/v2.5.0/dsh-cangjie-skill-2.5.0.tgz" \
  -o ~/.dsh/packages/dsh-cangjie-skill-2.5.0.tgz
curl -fL "https://github.com/kangarooking/cangjie-skill/releases/download/v2.5.0/dsh-cangjie-skill-2.5.0.tgz.sha256" \
  -o ~/.dsh/packages/dsh-cangjie-skill-2.5.0.tgz.sha256
(cd ~/.dsh/packages && shasum -a 256 -c dsh-cangjie-skill-2.5.0.tgz.sha256)
dsh plugin --profile web add ~/.dsh/packages/dsh-cangjie-skill-2.5.0.tgz
dsh web

Download the DeepSeek Harness plugin (for Cangjie Skill v2.5.0) · SHA256 checksum

After starting a new task, you can say:

Use cangjie-skill to distill this book into a set of executable Agent Skills: <file path>

Why This Exists

There's a recent viral idea: distilling colleagues into AI skills. Even after someone leaves, their experience, tone, and work style can be partially replicated by AI. nuwa-skill does exactly this — creating "human skills" like an Elon Musk skill or a Warren Buffett skill. The companion darwin-skill handles automatic skill evolution.

Distilling people is valuable — nuwa-skill has already proven this. Distilling the content people have expressed systematically is a complementary dimension: a book, a long-form interview, a podcast episode, or a long Bilibili or YouTube video can contain methodologies that took the creator years to refine. Rather than imitating someone's expression style, extracting those methodologies and turning them into tools that solve real problems is equally valuable.

There's also a real pain point: you may read many books, save many videos, and listen to many podcasts, yet still struggle to apply what you learned. Content-rich long videos are published every day, are often time-sensitive, and can be difficult to absorb in one viewing; they may not be represented in an AI model's training data at all. Once this content is distilled into skills, an AI agent can invoke the knowledge in real scenarios instead of letting it gather dust in notes, bookmarks, or watch-later lists.

So cangjie-skill has one clear goal: distill every piece of high-value content worth distilling. It works not only with books, but also with videos that have subtitles or transcripts, podcasts, interviews, talks, courses, long-form articles, and document collections. Whenever content contains extractable, verifiable, and transferable methodologies, cangjie-skill can turn them into independently callable, composable, and pressure-testable AI skill packs.

For video content, we recommend using the video-downloader skill alongside cangjie-skill. Use it first to download the video, extract subtitles or audio transcripts, and collect key materials; then pass the resulting text to cangjie-skill for methodology extraction, skill construction, and pressure testing.

What Problems It Solves

  • Reading many books, watching many videos, or listening to many podcasts without applying them — knowledge stays at the "read/watched/listened/saved" level and cannot be invoked in real decisions
  • Summaries, notes, and organized transcripts are compression, not structured reuse — after reading or watching, you still do not know "what to use when"
  • Only a small fraction of high-value content deserves to become a tool — strict filtering is needed, not wholesale inclusion
  • Existing methods for reading, watching, and learning are designed for people, not agents — distillation must be execution-oriented rather than consumption-oriented

How It Works

cangjie-skill uses the RIA-TV++ pipeline to transform source texts—including books, video transcripts, podcast transcripts, and interview notes—into a reusable Capability Bundle, then compiles that source into installable skills. The process has seven stages:

  1. Whole-Content Comprehension (Adler Analysis) — Structural, interpretive, critical, and applicability analysis using Mortimer Adler's method, producing BOOK_OVERVIEW.md
  2. Parallel Extraction — Five specialized extractors (frameworks, principles, cases, counter-examples, glossary) run simultaneously to pull candidate units from the source text
  3. Triple Verification + Promotion Gate — Check source sufficiency, executability, and task utility by candidate type. A complete procedure or formula explained once can qualify; repetition or author originality is not mandatory. References and unresolved candidates remain auditable, and standalone entrypoints are decided separately
  4. RIA++ Capability Construction — Verified content is structured into R / I / A1 / A2 / E / B capability cards inside .cangjie/capabilities/
  5. Zettelkasten Linking — Dependencies, contrasts, and compositions are encoded in the Bundle's capability graph and shared glossary
  6. Pressure Testing — Test prompts including bait questions (and cross-skill confusion tests) are designed for each skill; failures go back for full reconstruction
  7. Deterministic Compilation and Delivery — The same Bundle compiles to single or compact pack, alongside a reader-facing DIGEST.md, validation results, and installable artifacts

The name RIA-TV++ breaks down as:

  • RIA: From Zhao Zhou's bookmark method (Reading / Interpretation / Appropriation)
  • TV: Triple Verification
  • ++: Agent-oriented extensions — E (Execution) + B (Boundary)

Effect Examples

Example 1: From a Book or Long-Form Video to a Skill Toolkit

User Need

"I want to turn the core methodologies from a book or a long Bilibili/YouTube video into reusable AI skills, not just a summary."

How cangjie-skill reasons

  • Check whether the source material has reusable methodological units
  • Distinguish what deserves to be a standalone skill vs. background material
  • Output a structured skill repository, not a single summary document

Example Output

The result will not be one summary document. It will be a multi-skill repository with BOOK_OVERVIEW.md, INDEX.md, a reader-facing DIGEST.md, a GLOSSARY.md, multiple */SKILL.md files, and test-prompts.json for trigger testing.

Example 2: Structured Reuse, Not Compression

User Need

"I don't want a long explanatory article. I want a skill pack my agent can reuse."

How cangjie-skill reasons

  • Target is structured reuse, not narrative compression
  • Prioritize triggerable, composable, testable skill units
  • Reject material that doesn't deserve standalone skill status

Example Output

The system produces multiple skill modules with trigger conditions, boundaries, execution patterns, and related-skill links — rather than flattening the source into one generalized note.

Generated Skill Packs

Repository Source Skills
buffett-letters-skill Buffett's shareholder letters (1957-2023) 20
cognitive-dividend-skill Cognitive Dividend 15
duan-yongping-skill Duan Yongping's Q&A (business + investment logic) 15
viral-copywriting-skill Bao Kuan Wen An 14
copywriters-handbook-skill The Copywriter's Handbook 12
contagious-skill Contagious 15
influence-skill Influence 12
1000-true-fans-skill 1000 True Fans 13
system-prompt-skills 165 AI product system prompts 15
X-growth-skills Practical X (Twitter) account launch, content growth, algorithm, engagement, and monetization resources 15
sunyuchen-skill A single narrative writing sample labeled “sunyuchen” 1 (7 capabilities)
poor-charlies-almanack-skill Poor Charlie's Almanack 12
no-rules-rules-skill No Rules Rules 10
huangdi-neijing-skill Huangdi Neijing (Suwen + Lingshu) 22
first-principles-skill First Principles 10
mao-selected-works-skill Selected Works of Mao Zedong, Vol. 1-5 25
qbdx-hub/buffett-letters-skill Buffett Shareholder Letters (1957-2023) 20
qbdx-hub/wo-yu-di-tan-skill Wo Yu Di Tan 6
qbdx-hub/mingchao-those-things-skill Mingchao Those Things 7
qbdx-hub/sunzi-bingfa-skill Sunzi Bingfa 8
qbdx-hub/zhouyi-skill Zhouyi 8
qbdx-hub/high-math-vol1-ch1-skill High Math Vol. 1 Chapter 1 8

Video Distillation

These repositories are built from subtitles or transcripts of long-form videos, courses, or video collections. They demonstrate cangjie-skill's ability to distill methodologies from non-book content.

Repository Source Skills
ai-for-everyone-skill Andrew Ng's AI for Everyone video course 25
loop-engineering-skill Loop Engineering long-form video collection 8

More high-value books are planned for distillation. Future candidates include, but are not limited to, The Prince.

Additional external source (included with the author's permission):

Repository Structure

cangjie-skill/
├── README.md              ← You are here (default)
├── README.zh-CN.md        ← Simplified Chinese version
├── README.ja.md           ← Japanese version
├── LICENSE                ← MIT License
├── SKILL.md               ← Meta-skill definition (full execution spec for cangjie-skill)
├── methodology/           ← RIA-TV++ stage-by-stage methodology docs
├── extractors/            ← Prompt definitions for the 5 parallel extractors
└── templates/             ← SKILL.md / INDEX.md / BOOK_OVERVIEW.md templates

Ecosystem

cangjie-skill is part of a larger skill ecosystem:

  • nuwa-skill — Distills people (thinking styles, expression DNA)
  • cangjie-skill (this repo) — Distills books (methodologies, frameworks, principles)
  • darwin-skill — Evolves any skill

They interlock: nuwa distills people, cangjie distills books, darwin keeps them evolving.

More Skills

  • Buffett Letters Skill — 20 investment reasoning skills from Buffett's 60+ years of shareholder letters
  • Poor Charlie's Almanack Skill — 12 decision-making and judgment skills from Charlie Munger's core thinking methods
  • No Rules Rules Skill — 10 organizational design skills from Netflix's culture of freedom and responsibility
  • Cognitive Dividend Skill — 15 cognitive tool skills for thinking upgrades from Cognitive Dividend
  • Duan Yongping Skill — 15 business and investment skills from Duan Yongping's Q&A collection
  • Viral Copywriting Skill — 14 sales copywriting and diagnosis skills from Bao Kuan Wen An
  • Copywriters Handbook Skill — 12 sales copywriting, headline, and benefit translation skills from The Copywriter's Handbook
  • Contagious Skill — 15 STEPPS propagation strategy and word-of-mouth diagnosis skills from Contagious
  • Influence Skill — 12 persuasion psychology, compliance mechanism, and defensive judgment skills from Influence
  • 1000 True Fans Skill — 13 personal branding, true fan development, and trust-based monetization skills from 1000 True Fans
  • System Prompt Skills — 15 system prompt design skills distilled from 165 AI product system prompts
  • X Growth Skills — 15 skills for X account launch, content, algorithms, engagement, review, and monetization
  • sunyuchen-skill — A restrained narrative writing skill covering cold opens, operational detail, short dialogue, emotional restraint, and object callbacks
  • Huangdi Neijing Skill — 22 methodology skills from Huangdi Neijing, including 12 from Suwen and 10 from Lingshu
  • First Principles Skill — 10 skills on axiomatic reasoning, boundary-breaking innovation, and organizational refresh from First Principles
  • Mao Selected Works Skill — 25 cognition, strategy, organization, and execution skills from Selected Works of Mao Zedong
  • qbdx-hub Buffett Letters Skill — 20 investment and capital allocation skills from Buffett shareholder letters
  • qbdx-hub Wo Yu Di Tan Skill — 6 skills on limits, suffering, writing, and self-anchoring from Wo Yu Di Tan
  • qbdx-hub Mingchao Those Things Skill — 7 skills on power structure, institutional failure, and historical explanation from Mingchao Those Things
  • qbdx-hub Sunzi Bingfa Skill — 8 skills on strategic judgment, resource control, and action selection from Sunzi Bingfa
  • qbdx-hub Zhouyi Skill — 8 skills on situational diagnosis, timing, and advance-retreat boundaries from Zhouyi
  • qbdx-hub High Math Vol. 1 Chapter 1 Skill — 8 learning skills on limits, infinitesimals, and continuity from High Math Vol. 1 Chapter 1

External Source (included with the author's permission):

  • book2startup — includes skills distilled from The Lean Startup, The Art of War, Zhuangzi, and I Ching
  • book2skill — includes AI-Agent skills distilled from Chanlun and The Classic of Tea

Contributors

Thank you to the following contributors for expanding the cangjie-skill ecosystem:

  • shenqistart — contributed the external book2skill reference and additions across the Chinese, English, and Japanese READMEs
  • qbdx-hub — contributed 6 Cangjie whole-book/chapter distillation example repositories and additions across the Chinese, English, and Japanese READMEs

About the Author

袋鼠帝 kangarooking — AI blogger and indie developer. Creator of the AI Top WeChat Official Account “袋鼠帝 AI 客栈”

Kangarooking personal WeChat QR code

Volcengine Navigation KOL, Baidu Qianfan Developer Ambassador, GLM Evangelist, Trae Kunming's First Fellow

Platform Link
𝕏 Twitter https://x.com/aikangarooking
Xiaohongshu https://xhslink.com/m/5YejKvIDBbL
Douyin https://v.douyin.com/hYpsjphuuKc
WeChat Official Account 袋鼠帝 AI 客栈
WeChat Video Channel AI 袋鼠帝

WeChat Official Account「袋鼠帝 AI 客栈」QR code:

If you also want to distill methodologies from books, long-form videos, podcasts, and courses into callable Agent Skills, join the cangjie-skill WeCom community group:

cangjie-skill WeCom community group QR code

⭐ Star History

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License

MIT License. See LICENSE.

Frequently asked questions

Is cangjie-skill free to use?

cangjie-skill is open source under the MIT licence. There is no licence fee and no seat count — you can self-host it or, where the project offers one, pay a vendor for a managed version instead.

What does cangjie-skill do?

把书、长视频、播客等高价值内容蒸馏成可执行的 Agent Skills(Distill high-value content from books, long-form videos, podcasts, and more into executable Agent Skills)

What is cangjie-skill written in?

cangjie-skill is primarily written in Python. Its source is publicly available at https://github.com/kangarooking/cangjie-skill, and it has 12,008 GitHub stars.