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Get Started Free →Screen proposed trademark → conflicts + distinctiveness pre-filing. TM DB search (TMview, WIPO, USPTO TESS), Abercrombie spectrum, likelihood-of-confusion (DuPont|EUIPO), common law rights, goods/services overlap. Produces conflict report w/ risk matrix. Use → pre-adopt new brand|logo|slogan — distinct from patent prior art (diff DBs, legal frame, methods).
.claude/skills/thomasmoreai-screen-trademark/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-14 | ✗→✓ | ▲ Improved | 108% | 0% |
| case-13 | ✓→✗ | ▼ Worse | 86% | 0% |
| case-06 | ✓→✓ | = Same ✓ | 134% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 100% | 0% |
| case-05 | ✓→✓ | = Same ✓ | 189% | 0% |
Screen proposed mark → conflicts + assess distinctiveness pre-filing. Search registries, eval Abercrombie, analyze confusion w/ priors, produce report w/ risk ratings.
Establish exact what + which classes.
→ Record of mark, goods/services, Nice classes, jurisdictions. Defines search scope.
If err: Nice ambiguous → err on more classes. Broader screen safer than missing adjacent conflict.
Identical + similar across registries.
→ List of potential conflicts from ≥2 DBs, identical+similar in relevant classes+jurisdictions. Each w/ enough detail for Step 4.
If err: DB unavailable → note gap, proceed w/ available. Common word → large result; prioritize same|closely related Nice before expanding.
Where on Abercrombie spectrum.
→ Clear classification w/ rationale. Descriptive → assess if secondary meaning achievable. Suggestive|arbitrary|fanciful proceed confident.
If err: generic-descriptive border = significant risk. Recommend modify → push toward suggestive (add twist, combine unrelated) or prepare secondary meaning evidence strategy.
Eval if proposed likely confused w/ priors found Step 2.
→ Rated conflict list w/ analysis. Most serious (blocking|high) w/ specific reasoning.
If err: borderline → conservative (higher risk). Safer to flag manageable than miss blocker.
Unregistered rights not in DB searches.
→ Supplementary unreg use list could create conflicts not visible in registry. Especially important US.
If err: common law overwhelming (common word) → focus same industry|category. Common law typically narrow scope — local bakery "Sunrise" doesn't block software "Sunrise."
Competitive proximity detail.
→ Clear goods/services proximity per conflict, strengthening|weakening Step 4 ratings.
If err: relationship unclear (novel categories, convergent industries) → reasonable consumer test: typical buyer seeing both assume same source?
Compile all → structured actionable report.
Conflict Risk Matrix:
+----+-------------------+----------+---------+-------+---------+
| # | Prior Mark | Classes | Juris. | Type | Risk |
+----+-------------------+----------+---------+-------+---------+
| 1 | ACMESOFT | 9, 42 | US, EU | Ident | BLOCK |
| 2 | ACME SOLUTIONS | 42 | US | Sim | HIGH |
| 3 | ACMEX | 35 | EU | Phon | MOD |
| 4 | ACM | 16 | US | Vis | LOW |
+----+-------------------+----------+---------+-------+---------+
Risk: BLOCK = blocking | HIGH | MOD = moderate | LOW | CLEAR
Type: Ident = identical | Sim = similar | Phon = phonetic | Vis = visual→ Complete report w/ ratings, distinctiveness, recommendations. Enables go/no-go.
If err: inconclusive (mixed cross jurisdictions|classes) → present by jurisdiction; let decision-maker weigh business + legal. Qualified "proceed w/ caution" valid.
assess-ip-landscape — broader IP landscape mapping w/in full IP strategysearch-prior-art — patent-focused prior art (diff DBs + legal: novelty|obviousness vs confusion)file-trademark — filing post-screen (not yet avail)| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-06 | pass→pass | 11,387 | 14,538 | +28% | 1 | 1 | 0% | 2,237 | 5,241 | +134% | 0 | 0 | — |
case-01 | fail→fail | 33,535 | 23,743 | -29% | 1 | 1 | 0% | 6,161 | 6,833 | +11% | 0 | 0 | — |
case-02 | fail→fail | 10,748 | 6,757 | -37% | 1 | 1 | 0% | 2,084 | 3,995 | +92% | 0 | 0 | — |
case-03 | fail→fail | 19,270 | 24,477 | +27% | 1 | 1 | 0% | 4,462 | 7,641 | +71% | 0 | 0 | — |
case-04 | pass→pass | 14,533 | 15,526 | +7% | 1 | 1 | 0% | 2,736 | 5,475 | +100% | 0 | 0 | — |
case-05 | pass→pass | 8,041 | 12,193 | +52% | 1 | 1 | 0% | 1,680 | 4,859 | +189% | 0 | 0 | — |
case-07 | pass→pass | 13,828 | 19,635 | +42% | 1 | 1 | 0% | 2,457 | 5,823 | +137% | 0 | 0 | — |
case-08 | pass→pass | 12,055 | 15,279 | +27% | 1 | 1 | 0% | 2,349 | 5,322 | +127% | 0 | 0 | — |
case-09 | pass→pass | 13,017 | 11,960 | -8% | 1 | 1 | 0% | 2,524 | 4,955 | +96% | 0 | 0 | — |
case-10 | pass→pass | 11,604 | 12,112 | +4% | 1 | 1 | 0% | 2,069 | 4,703 | +127% | 0 | 0 | — |
case-11 | pass→pass | 18,167 | 17,140 | -6% | 1 | 1 | 0% | 2,867 | 5,675 | +98% | 0 | 0 | — |
case-12 | pass→pass | 12,255 | 10,772 | -12% | 1 | 1 | 0% | 2,291 | 4,528 | +98% | 0 | 0 | — |
case-13 | pass→fail | 11,340 | 8,817 | -22% | 1 | 1 | 0% | 2,160 | 4,027 | +86% | 0 | 0 | — |
case-14 | fail→pass | 11,847 | 12,259 | +3% | 1 | 1 | 0% | 2,396 | 4,986 | +108% | 0 | 0 | — |
case-15 | pass→pass | 14,210 | 14,979 | +5% | 1 | 1 | 0% | 2,540 | 5,122 | +102% | 0 | 0 | — |
case-16 | pass→pass | 14,366 | 8,634 | -40% | 1 | 1 | 0% | 2,545 | 4,091 | +61% | 0 | 0 | — |
case-17 | pass→pass | 11,617 | 10,802 | -7% | 1 | 1 | 0% | 2,426 | 4,844 | +100% | 0 | 0 | — |
case-18 | pass→pass | 12,395 | 12,086 | -2% | 1 | 1 | 0% | 2,427 | 4,771 | +97% | 0 | 0 | — |
case-19 | pass→pass | 13,697 | 11,328 | -17% | 1 | 1 | 0% | 2,607 | 4,693 | +80% | 0 | 0 | — |
case-20 | pass→pass | 14,741 | 14,226 | -3% | 1 | 1 | 0% | 2,409 | 5,267 | +119% | 0 | 0 | — |
case-21 | pass→pass | 10,760 | 13,724 | +28% | 1 | 1 | 0% | 2,124 | 5,079 | +139% | 0 | 0 | — |
case-22 | pass→pass | 13,953 | 12,461 | -11% | 1 | 1 | 0% | 2,209 | 4,936 | +123% | 0 | 0 | — |
case-23 | pass→pass | 6,477 | 4,277 | -34% | 1 | 1 | 0% | 1,238 | 3,492 | +182% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of 0 percentage points is the difference between those two pass rates over the 23 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.