# Triple Verification Process (V1, V2, V3) in Cangjie-Skill Distillation Explained

> Understand Cangjie-skill's triple verification process V1 V2 V3. Learn how this quality filter validates extracted knowledge for cross-domain evidence predictive power and uniqueness.

- Repository: [kangarooking/cangjie-skill](https://github.com/kangarooking/cangjie-skill)
- Tags: deep-dive
- Published: 2026-08-14

---

**The triple verification process (V1, V2, V3) is a three-stage quality filter in Cangjie-skill that validates extracted knowledge for cross-domain evidence, predictive power, and uniqueness before it becomes a reusable AI skill.**

Cangjie-skill converts unstructured content—books, long videos, podcasts—into agent-ready AI skills through a seven-stage pipeline called **RIA-TV++**. The "TV" stands for **Triple Verification**, a strict gate that eliminates roughly 50-75% of initial candidates to ensure only high-quality insights survive.

## What Is the Triple Verification Process (V1, V2, V3)?

The triple verification process is implemented in the distillation pipeline as a sequential filter. According to the repository's [`README.md`](https://github.com/kangarooking/cangjie-skill/blob/main/README.md) (lines 43-56) and the [`SKILL.md`](https://github.com/kangarooking/cangjie-skill/blob/main/SKILL.md) specification, each verification step targets a specific failure mode that would make a skill unreliable or useless.

The three stages operate as **hard requirements**—a candidate must pass all three to advance. Empirical pass rates range from **25-50%** of initial extractions, reflecting the rigorous standards applied.

## V1 Verification: Cross-Domain Evidence

**V1 verification** ensures a skill has **at least two independent citations** in the source material.

This requirement guarantees the underlying method is **re-occurring** rather than a one-off anecdote. Independent citations might appear as separate paragraphs, distinct sections, or different timestamps in a media file. Without this check, the system risks overfitting to isolated claims that lack broader support.

## V2 Verification: Predictive Power

**V2 verification** tests whether a skill can **answer questions not explicitly stated** in the original text.

This demonstrates **extrapolation capability**—the skill must generalize beyond verbatim retrieval. A candidate that only echoes source content fails here. The system evaluates this by posing novel queries and verifying the skill produces coherent, grounded answers. This criterion ensures skills remain useful in **unseen contexts** rather than degenerating into simple text search.

## V3 Verification: Uniqueness

**V3 verification** filters out **common-sense or trivial statements**.

A skill must represent a **distinct insight** not already widely known. This prevents skill-packs from cluttering with obvious observations (e.g., "water is wet") and ensures each distilled skill **adds genuine value**. The uniqueness check compares candidates against general knowledge bases and flags those lacking novel contribution.

## How Triple Verification Is Implemented

The verification logic appears in the distillation pipeline as shown in this pseudo-code from the repository's methodology:

```python
def triple_verify(candidate):
    # V1 – check for ≥2 independent citations

    if not candidate.has_multiple_citations(min_refs=2):
        return False

    # V2 – check predictive power (can answer novel query)

    if not candidate.can_answer_unasked_question():
        return False

    # V3 – ensure insight is non-trivial

    if candidate.is_common_sense():
        return False

    return True

# Usage in the pipeline

for raw_skill in extracted_candidates:
    if triple_verify(raw_skill):
        approved_skills.append(raw_skill)

```

The method signatures (`has_multiple_citations(min_refs=2)`, `can_answer_unasked_question()`, `is_common_sense()`) reflect the specific criteria documented in [`SKILL.md`](https://github.com/kangarooking/cangjie-skill/blob/main/SKILL.md) under the "三重验证筛选" (triple verification filter) section.

## Where Triple Verification Fits in RIA-TV++

The complete RIA-TV++ pipeline processes raw content through seven stages. Triple verification constitutes the critical **quality gate** after initial extraction:

1. **R**etrieval of source material
2. **I**nsight extraction
3. **A**ggregation of candidates
4. **T**riple Verification (V1, V2, V3) ← **you are here**
5. **V**alidation of outputs
6. **+** Enhancement
7. **+** Packaging

Only post-verification skills proceed to final templating in the `templates/` directory, where they are rendered into callable skill files.

## Key Files and Resources

| File | Location | Purpose |
|------|----------|---------|
| [`README.md`](https://github.com/kangarooking/cangjie-skill/blob/main/README.md) | Lines 43-56 | Defines RIA-TV++ pipeline and verification criteria |
| [`SKILL.md`](https://github.com/kangarooking/cangjie-skill/blob/main/SKILL.md) | Root directory | Formal specification of all pipeline stages |
| `methodology/` | Subdirectory | Deep methodological notes on V1, V2, V3 sub-steps |
| `templates/` | Subdirectory | Output templates for verified skills |

## Summary

- **V1 (Cross-domain evidence)** mandates ≥2 independent citations to prevent overfitting to isolated claims.
- **V2 (Predictive power)** requires extrapolation capability beyond source text for usefulness in novel contexts.
- **V3 (Uniqueness)** filters common-sense content to preserve skill-pack value density.
- The combined filter achieves **25-50% pass rates**, ensuring only robust, agent-ready knowledge survives.
- Implementation spans [`README.md`](https://github.com/kangarooking/cangjie-skill/blob/main/README.md), [`SKILL.md`](https://github.com/kangarooking/cangjie-skill/blob/main/SKILL.md), and pipeline code with clear method signatures for each check.

## Frequently Asked Questions

### What happens if a skill fails just one verification stage?

The skill is **rejected entirely**. The triple verification process (V1, V2, V3) operates as a **conjunctive filter**—all three criteria must be satisfied. There is no partial credit or "soft pass" mechanism, which maintains the high quality bar for the final skill-pack.

### Why does Cangjie-skill use three separate verifications instead of a single combined score?

Each V-stage targets a **distinct failure mode** that a scalar score would blur. V1 catches under-supported claims, V2 catches non-generalizable patterns, and V3 catches trivial content. Separate stages enable **precise debugging** when skills underperform and prevent trade-offs where one strong dimension masks critical weaknesses in another.

### Can the verification thresholds be adjusted for different content types?

The repository documentation in [`SKILL.md`](https://github.com/kangarooking/cangjie-skill/blob/main/SKILL.md) does not describe configurable thresholds. The `min_refs=2` parameter in `has_multiple_citations()` suggests the citation minimum is **hardcoded**, though the `methodology/` directory may contain experimental variants for specialized domains.