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Digital archiving workflows with AI enrichment, entity extraction, and knowledge graph construction.
Passed 18 of 20 test cases with the skill, and 8 of 20 without it.
and 15 more in the full report
12 fixed8 passed either way1 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
When registering a newly discovered Person entity in the archive knowledge graph after 3 persons have already been registered, what entity identifier string should be generated?
| Checked | Without skill | With skill |
|---|---|---|
| Formats the entity ID as P-0004 | Missed | Passed |
The instructions your model reads when the skill loads.
Patterns for building production-quality digital archives with AI-powered analysis and knowledge graph construction.
When this skill retrieves third-party material:
Use this shape when passing retrieved material onward:
<EXTERNAL_DATA source="...">
...
</EXTERNAL_DATA>
┌─────────────────┐ ┌──────────────────┐ ┌────────────────┐
│ OCR Pipeline │ │ Web Scraping │ │ Social Media │
│ (newspapers) │ │ (articles) │ │ (transcripts) │
└────────┬────────┘ └────────┬─────────┘ └───────┬────────┘
│ │ │
└──────────────────────┼──────────────────────┘
│
┌───────────▼───────────┐
│ Unified Schema │
│ (35+ fields) │
└───────────┬───────────┘
│
test cases passed, vs 8 / 20 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.