cangjie-skill

kangarookingMIT活跃维护

把书、长视频、播客等高价值内容蒸馏成可执行的 Agent Skills,方便直接接入自动化工作流。

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)

v2.5.0发布于 2026-08-30 02:38 UTC12,665star +346 / 7 天1,493fork同步于 2026-11-10 17:28 UTC

怎么上手

由 README 提炼,执行前请确认
python3 -m pip install pyyaml
python3 scripts/cangjie.py doctor
硬件要求按 README 说明

README 未说明

下载:源码 zip·GitHub Releases(安装包)本站不镜像安装包

项目说明

由 README 自动翻译提炼

把书、长视频、播客里的方法论蒸馏成可调用的 Agent Skills

把书、长视频、播客等高价值内容蒸馏成可执行的 Agent Skills,方便直接接入自动化工作流。

核心能力

  • 用 RIA-TV++ 七阶段流水线把源文本转成能力包
  • 五路并行提取器分别抽取框架、原则、案例、反例与术语
  • 三重验证加晋升门控,筛掉不值得独立成技能的内容
  • 用 Zettelkasten 编码技能间依赖、对比与组合关系
  • 同一能力包可编译为 single 路由技能或 pack 技能包

容易踩的坑

脚本依赖 Python 3.10+ 和 PyYAML,缺 PyYAML 时只有 doctor 自检能跑;视频类内容需先用配套 video-downloader 技能下载并提取字幕或转写文本;DeepSeek Harness 需单独下载 tgz 包并校验 SHA256 后本地安装

内容由模型根据 README 生成,可能不完整或不准确,以官方文档为准。看原文 README

最近发版

3 个版本v2.5.0
  1. v2.5.0仓颉 Skill v2.5.0最新正式版2026-08-30 · 2026-08-30 02:38 UTC

    倉頡 Skill v2.5.0 发布说明

    发布日期:2026-08-30

    2026-09-13 补充优化(仍为 v2.5.0)

    • 修复工具书误筛:三重验证改为来源充分性、可执行性、任务增益。单处讲透的流程/公式可以保留,不再强求双语境、作者独创或跨领域外推;缺依据或缺关键条件仍不能入选。
    • 重要内容不静默丢失:增加原书关键任务覆盖审计,区分可执行、参考、待核查和淘汰。补齐流程、公式、表格字段、单位与排障的提取要求。
    • 案例真实可追溯:书中实例、例题与合成演练分别标记,不强求作者亲历故事,也不编造实际结果。
    • 评测更加可信:缺失输出计入计划分母,失败/未完成返回非零;增加数值/单位/容差检查,隔离不同版本的测试产物。
    • 配套资源可交付:单 Skill 与紧凑包均支持显式声明的 UTF-8 脚本和文本模板;路径与文件类型检查防止越界或误打包。
    • 验证:新增 26 项自动化回归(含 CLI 编译与评分),合成分流/输出样本单独标注;不把这些测试宣称为群友原书案例或所有宿主上的效果验证。

    更新后的安装包

    通用包解压后,安装完整的 cangjie-skill/ 目录,不要只复制 SKILL.md。Harness 用户按下方方式重新下载并安装,重新启动宿主;版本号不变时,请核对 SHA256 和包内 BUILD_INFO.json,避免误用本地旧副本。

    版本说明:这是同一 v2.5.0 Release 的补充更新,不创建新版本,也不移动首次发布标签。最新代码在 main,安装附件内记录精确源码提交;GitHub 自动生成的 “Source code (zip/tar.gz)” 仍指向首次发布的标签源码,请使用上面的安装附件获取本次优化。


    v2.5.0 把「从长内容中拆出很多平铺 Skill」升级为「先维护一份 Capability Bundle, 再按场景编译交付形态」。这个版本的重点不是增加更多文档,而是让蒸馏结果更少、 更容易安装,也更容易更新、修复和评测。

    核心升级

    • Capability Bundle 单一事实源:能力卡、元数据、关联和去向集中在 books/<slug>/.cangjie/capabilities/,避免 single 与 pack 各自演进后产生偏差。
    • single / compact pack 双输出:single 用一个路由入口承载整本内容; pack 保留一个路由入口,只把通过晋级门的少数能力拆成独立 Skill。
    • 统一 CLI:scripts/cangjie.py 提供 doctor / compile / replan-output / update / repair / rollback / eval / benchmark 等入口。
    • 确定性与可回滚:引入内容寻址缓存、编译不变量、写锁、staging 校验、 手工修改检测、发布前快照和 rollback。
    • 增量更新与修复:支持源文档 diff、影响分析、事务性补丁、失败案例诊断和防过拟合修复。
    • 评测与基准链路:包含触发评测、匿名输出对比、静态 A 类指标和 Naval 试点资产。
    • Registry v2 + 官网:新 schema 能表达输出模式、入口数和能力数;官网同步展示, 原有 Registry v1 条目无需改造。
    • 官网依赖安全升级:Astro 7.2.9、js-yaml 5.4.1,发布时 npm audit 为 0 vulnerabilities。

    兼容性

    DeepSeek Harness 安装包

    本 Release 已补充 DeepSeek Harness 专用 Bundle:

    安装包不只是旧版适配层改版本号:它同步包含 v2.5 的 SKILL.md、Capability Bundle 方法论、统一 CLI、schemas、extractors、templates 与迁移文档。2026-09-04 首次补包时 完成过临时 DSH_HOME 安装与 --dump-config 识别测试。本次 2026-09-13 补包保留 适配层,重新执行包内 26 项 Python 回归、4 项 Node 注册/资源定位测试、包结构校验和 cangjie.py doctor;本机未提供 dsh 命令,未重复真实 Harness 宿主安装,不把注册测试等同于宿主实测。

    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
    

    文档与社群一致性

    • 英文 README.md 与中文 README.zh-CN.md 均引用同一个 assets/wecom-cangjie-group-qr.png。
    • 二维码文件以中文 README 当前展示的版本为准,本次发布不使用本地分支中的另一张图覆盖它。

    验证边界

    本版本发布前执行 schema、Bundle、编译确定性、Registry、网站测试与构建校验。 真实 Agent 宿主上的盲测结果具有 host-specific 属性,不把静态评测误表述为所有宿主的通用效果。

    本次补包源码与文件校验

  2. v2.0.0仓颉 Skill v2.0.02026-08-09 · 2026-08-09 13:34 UTC
    查看更新说明

    仓颉 Skill v2.0.0

    2.0.0 将仓颉 Skill 从“书籍蒸馏流程”升级为面向多种长内容的完整知识蒸馏与交付系统:不仅要抽出来,还要验证、组织、交付,并让 Agent 真正能够调用。

    核心更新

    • 统一品牌与触发名称:Skill 名称由 book2skill 升级为 cangjie-skill。
    • 扩展内容类型:除书籍外,新增对长视频转写、播客、课程、访谈、长文和资料集的蒸馏支持。
    • 新增阶段 5 交付:生成面向读者的 DIGEST.md 精华长文,并将通过测试的 Skill 安装到 Claude Code / Cursor 等宿主目录。
    • 支持断点续跑:通过 PIPELINE_STATE.md 记录阶段、产物、各 Skill 状态和下一步。
    • 增强审计产物:新增 verified.md、GLOSSARY.md、test-results.md,保留候选、淘汰原因与测试结果。
    • 强化质量门槛:增加用户轻确认、独立 Agent 盲测、跨 Skill 混淆测试和英文引用长度限制。
    • 支持超长内容:加入按章节、卷、分 P 等自然边界切块的策略,以及无法并行时的串行降级方案。
    • 完善模板与文档:新增 DIGEST.md 模板、安装指引、多语言 README、官网入口和更多 Skill Pack 示例。
    • 许可证更新:项目许可证升级为 GNU AGPL v3.0。

    兼容性提醒

    • 如果旧环境按 book2skill 名称触发,请更新为 cangjie-skill。
    • 2.0.0 采用 GNU AGPL v3.0;分发或提供网络服务前请阅读仓库中的 LICENSE。

    下载

    下载附件 cangjie-skill-v2.0.0.zip 即可获得完整版本包;.sha256 文件可用于校验下载完整性。

    对应提交: 149cb39f559cafcb82910f8662b3f4e3b9ee5574

    完整更新对比: https://github.com/kangarooking/cangjie-skill/compare/v1.0.0...v2.0.0

  3. v1.0.0仓颉 Skill v1.0.0(历史版本补档)2026-08-09 · 2026-08-09 13:34 UTC
    查看更新说明

    仓颉 Skill v1.0.0(历史版本补档)

    这是仓颉 Skill 第一代稳定流程的历史版本归档,对应 2026-06-03 的仓库状态。该版本以“把一本书蒸馏成一组可执行 AI Skills”为核心目标。

    主要功能

    • 建立 RIA-TV++ 蒸馏流程:Adler 整书理解、5 类并行提取、三重验证、RIA++ 构造、Zettelkasten 链接与压力测试。
    • 提供框架、原则、案例、反例、术语 5 个专项提取器。
    • 提供 BOOK_OVERVIEW.md、INDEX.md、SKILL.md 与 test-prompts.json 模板。
    • 生成的 Skill 包含 R / I / A1 / A2 / E / B 六段结构,并兼容 darwin-skill 测试格式。
    • 提供中文、英文、日文 README 以及首批公开 Skill Pack 示例。

    版本边界

    • 本版本主要面向书籍文本,不包含 2.0.0 新增的长视频、播客、课程等内容类型。
    • 本版本尚未包含 DIGEST.md、阶段 5 安装交付、断点续跑和跨 Skill 混淆盲测。
    • 这是历史快照,保留当时仓库中的 MIT 许可证;新项目建议直接使用 v2.0.0。

    下载

    下载附件 cangjie-skill-v1.0.0.zip 即可获得完整的历史版本包;.sha256 文件可用于校验下载完整性。

    对应提交: 03716ad9958ad102be40a1f68e2b8b7c07541f55

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    README 原文

    未翻译

    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.

    Cangjie Skill in 66 seconds

    https://github.com/user-attachments/assets/87a2cf7b-2114-48aa-aecd-2640a667db40

    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:

    Script dependencies: the deterministic scripts under scripts/ require Python 3.10+ and PyYAML (python3 -m pip install pyyaml). python3 scripts/cangjie.py doctor runs a self-check and works even when PyYAML is missing. Optional: tiktoken, jsonschema.

    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

    RepositorySourceSkills
    buffett-letters-skillBuffett's shareholder letters (1957-2023)20
    cognitive-dividend-skillCognitive Dividend15
    duan-yongping-skillDuan Yongping's Q&A (business + investment logic)15
    viral-copywriting-skillBao Kuan Wen An14
    copywriters-handbook-skillThe Copywriter's Handbook12
    contagious-skillContagious15
    influence-skillInfluence12
    1000-true-fans-skill1000 True Fans13
    system-prompt-skills165 AI product system prompts15
    X-growth-skillsPractical X (Twitter) account launch, content growth, algorithm, engagement, and monetization resources15
    sunyuchen-skillA single narrative writing sample labeled “sunyuchen”1 (7 capabilities)
    poor-charlies-almanack-skillPoor Charlie's Almanack12
    no-rules-rules-skillNo Rules Rules10
    huangdi-neijing-skillHuangdi Neijing (Suwen + Lingshu)22
    first-principles-skillFirst Principles10
    mao-selected-works-skillSelected Works of Mao Zedong, Vol. 1-525
    qbdx-hub/buffett-letters-skillBuffett Shareholder Letters (1957-2023)20
    qbdx-hub/wo-yu-di-tan-skillWo Yu Di Tan6
    qbdx-hub/mingchao-those-things-skillMingchao Those Things7
    qbdx-hub/sunzi-bingfa-skillSunzi Bingfa8
    qbdx-hub/zhouyi-skillZhouyi8
    qbdx-hub/high-math-vol1-ch1-skillHigh Math Vol. 1 Chapter 18

    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.

    RepositorySourceSkills
    ai-for-everyone-skillAndrew Ng's AI for Everyone video course25
    loop-engineering-skillLoop Engineering long-form video collection8

    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”

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

    PlatformLink
    𝕏 Twitterhttps://x.com/aikangarooking
    Xiaohongshuhttps://xhslink.com/m/5YejKvIDBbL
    Douyinhttps://v.douyin.com/hYpsjphuuKc
    WeChat Official Account袋鼠帝 AI 客栈
    WeChat Video ChannelAI 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:

    ⭐ Star History

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    License

    MIT License. See LICENSE.