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Multi-source cross-validation of gap authenticity — cross-database search, temporal sensitivity testing, false-gap filtering, stakeholder confirmation.
Passed 19 of 20 test cases with the skill, and 15 of 20 without it.
and 3 more in the full report
5 fixed16 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
A search on Semantic Scholar yields zero hits for a specific signal processing technique, whereas general web searches show several engineering blogs discussing it. What does this indicate about the gap?
| Checked | Without skill | With skill |
|---|---|---|
| Classify the absence as a database-specific gap rather than a universal research gap. | Missed | Passed |
The instructions your model reads when the skill loads.
Multi-source verification of gap authenticity.
Subagent: cross-database-verification, temporal-sensitivity-testing, false-gap-filtering, stakeholder-confirmation Import: web-search, paper-search
For each gap candidate: verify across 3+ databases (Semantic Scholar, Google Scholar, arXiv, domain-specific), test if gap persists across 2/5/10 year windows, apply false-gap heuristics (wrong search terms? already solved? inherently unanswerable?), confirm with stakeholder simulation.
<HARD-GATE>
- cross-database checks: >= 3 per gap
- temporal windows tested: >= 2 per gap
- false-gap filter applied: >= 1 per gap
</HARD-GATE>
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| cross-database-verification | Verify gap existence across multiple databases (Semantic Scholar, Google Scholar, arXiv, domain-specific). Distinguishes database-specific gaps from universal gaps. |
| deep-insight-paper-search | AI-powered paper summary and search. Import of literature-engine/literature-search skill. AI summary level — cite as "AI-extracted" not "paper states". |
test cases passed, vs 15 / 20 without
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