Scoring
July 3, 2026 ยท View on GitHub
The v0 scorer is deterministic and intentionally plain. It is a gate, not an oracle.
The scorer includes English and Chinese cues for rule language, examples, transferability, and conversational noise. Chinese source length is estimated with CJK characters as effective tokens rather than only whitespace-delimited words.
Signals
| Signal | Max | Meaning |
|---|---|---|
| Source depth | 25 | More words usually provide more room for structure and evidence |
| Structure | 20 | Headings, bullets, and numbered lists make extraction safer |
| Actionability | 20 | Rule-like language suggests reusable behavior |
| Transferability | 15 | Reuse, workflow, checklist, and decision cues suggest future use |
| Evidence | 10 | Examples and cases reduce unsupported claims |
| Signal cleanliness | 10 | Conversational noise lowers confidence |
Output-Level Mapping
Default score mapping:
| Score | Level |
|---|---|
| 0-19 | Discard |
| 20-39 | Note |
| 40-59 | Skill Seed |
| 60-79 | Mini Skill |
| 80-100 | Full Skill |
Length caps prevent short sources from being promoted too far:
- under 120 words: at most Skill Seed
- under 600 words: at most Mini Skill
- under 2,500 words: at most Mini Skill
This is conservative on purpose. Dense short sources are often useful, but they rarely deserve a full standalone skill.
Why No LLM Scoring In v0
LLMs can be useful later, especially for evidence classification and claim checking. The first version keeps scoring transparent so users can understand why a source was promoted or held back.