📦 deps(thirdparty): update snapshots
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### Distill methodologies from books, long-form videos, and podcasts into callable AI Skills
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[](./LICENSE)
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[](./CHANGELOG.md)
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[](./SKILL.md)
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[](https://github.com/openclaw/openclaw)
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[](https://code.claude.com/)
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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.
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## What's New in v2.5.0
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- **Capability Bundle as the single source of truth**: extraction produces stable capability cards and metadata before any installable output is compiled.
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- **Two deterministic delivery modes**: compile one router-style Skill (`single`) or a compact pack with a router plus promoted standalone Skills (`pack`).
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- **A unified local toolchain**: `scripts/cangjie.py` now covers diagnostics, compilation, output replanning, incremental updates, repair, rollback, evaluation, and benchmarking.
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- **Safer evolution**: content-addressed preprocessing, source diffs, impact analysis, transactional patches, edit detection, snapshots, and rollback are included.
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- **Registry v2 and website support**: output mode and capability counts are visible without breaking existing Registry v1 entries.
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See the [v2.5.0 release notes](./docs/releases/v2.5.0.md) and [changelog](./CHANGELOG.md) for the complete scope and migration notes.
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## DeepSeek Harness Plugin
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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.
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## How It Works
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cangjie-skill uses the **RIA-TV++** pipeline to transform source texts—including books, video transcripts, podcast transcripts, and interview notes—into a set of structured skills. The process has seven stages:
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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:
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1. **Whole-Content Comprehension (Adler Analysis)** — Structural, interpretive, critical, and applicability analysis using Mortimer Adler's method, producing `BOOK_OVERVIEW.md`
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2. **Parallel Extraction** — Five specialized extractors (frameworks, principles, cases, counter-examples, glossary) run simultaneously to pull candidate units from the source text
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3. **Triple Verification** — Each candidate must pass three checks: at least 2 independent supporting passages (cross-domain), ability to answer a novel question (predictive power), and non-commonsense uniqueness. Pass rate is typically 25-50%
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4. **RIA++ Construction** — Verified content is structured into six dimensions: R (original quote) / I (own-words reconstruction) / A1 (book cases) / A2 (future trigger scenarios) / E (executable steps) / B (boundaries & blind spots)
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5. **Zettelkasten Linking** — Dependency, contrast, and composition relationships between skills are identified, producing `INDEX.md` with a reference graph
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3. **Triple Verification + Promotion Gate** — Each candidate must pass the evidence checks, then earn an independent entrypoint only when its use cases justify the added routing cost
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4. **RIA++ Capability Construction** — Verified content is structured into R / I / A1 / A2 / E / B capability cards inside `.cangjie/capabilities/`
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5. **Zettelkasten Linking** — Dependencies, contrasts, and compositions are encoded in the Bundle's capability graph and shared glossary
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6. **Pressure Testing** — Test prompts including bait questions (and cross-skill confusion tests) are designed for each skill; failures go back for full reconstruction
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7. **Delivery** — A reader-facing `DIGEST.md` long-form digest is generated (skip the book, read the essence), and tested skills are installed into the Claude Code / Cursor skills directory so they can actually be invoked
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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
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The name RIA-TV++ breaks down as:
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- **RIA**: From Zhao Zhou's bookmark method (Reading / Interpretation / Appropriation)
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