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Get Started Free →Build a translation/terminology glossary so a product's key terms render consistently everywhere. Use when asked to create a glossary, a termbase, a do-not-translate list, or to keep terminology consistent across translators/locales. Produces a glossary — each source term with its approved translation per locale, part of speech, definition/context, and do-not-translate flags — ready for a CAT tool or style guide.
.claude/skills/mohitagw15856-glossary-builder/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -14% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -1% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -4% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 45% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 44% | 0% |
Inconsistent terminology is the most visible localization failure — when "dashboard" is translated three ways across one product, it looks amateur and confuses users. A glossary (termbase) fixes the key terms once, so every translator and every locale uses the approved rendering. This skill builds it: extract the terms that matter, define them in context, and set the approved translation (or do-not-translate flag).
Ask for these only if they aren't already provided:
A termbase table — one row per term:
| Source term | Part of speech | Definition / context | Do-not-translate? | Locale 1] | Locale 2] | |---|---|---|---|---|---| | Dashboard | noun | the main metrics screen | no | 仪表板 | Tableau de bord | | Acme Cloud | proper noun | product name | yes (keep verbatim) | Acme Cloud | Acme Cloud | | sync (verb) | verb | to reconcile data both ways | no | 同步 | synchroniser |
Guidance included:
Output note: structured for import into a CAT tool (Trados/memoQ/Crowdin) or to sit in the localization style guide. Mark any translation that needs native review as (draft — confirm).
Terminology-management practice — termbases, do-not-translate lists, context definitions, CAT-tool glossary structure.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 17,547 | 11,457 | -35% | 1 | 1 | 0% | 3,580 | 3,087 | -14% | 0 | 0 | — |
case-02 | fail→pass | 17,575 | 14,867 | -15% | 1 | 1 | 0% | 3,488 | 3,461 | -1% | 0 | 0 | — |
case-03 | fail→pass | 16,958 | 12,931 | -24% | 1 | 1 | 0% | 3,302 | 3,158 | -4% | 0 | 0 | — |
case-04 | pass→fail | 4,956 | 10,582 | +114% | 1 | 1 | 0% | 1,018 | 2,721 | +167% | 0 | 0 | — |
case-05 | pass→fail | 15,168 | 16,572 | +9% | 1 | 1 | 0% | 2,621 | 3,686 | +41% | 0 | 0 | — |
case-06 | pass→fail | 4,627 | 9,662 | +109% | 1 | 1 | 0% | 1,008 | 2,559 | +154% | 0 | 0 | — |
case-07 | fail→fail | 12,482 | 14,321 | +15% | 1 | 1 | 0% | 2,253 | 3,475 | +54% | 0 | 0 | — |
case-08 | fail→pass | 10,070 | 10,427 | +4% | 1 | 1 | 0% | 1,823 | 2,638 | +45% | 0 | 0 | — |
case-09 | fail→fail | 12,791 | 13,185 | +3% | 1 | 1 | 0% | 2,490 | 3,427 | +38% | 0 | 0 | — |
case-10 | fail→pass | 12,508 | 12,152 | -3% | 1 | 1 | 0% | 2,192 | 3,160 | +44% | 0 | 0 | — |
case-11 | fail→pass | 10,571 | 9,038 | -15% | 1 | 1 | 0% | 2,083 | 2,724 | +31% | 0 | 0 | — |
case-12 | fail→pass | 9,105 | 11,028 | +21% | 1 | 1 | 0% | 1,728 | 2,879 | +67% | 0 | 0 | — |
case-13 | fail→pass | 9,437 | 9,764 | +3% | 1 | 1 | 0% | 1,758 | 2,604 | +48% | 0 | 0 | — |
case-14 | fail→pass | 10,849 | 9,626 | -11% | 1 | 1 | 0% | 2,334 | 2,662 | +14% | 0 | 0 | — |
case-15 | fail→fail | 10,040 | 10,835 | +8% | 1 | 1 | 0% | 2,086 | 2,785 | +34% | 0 | 0 | — |
case-16 | fail→pass | 10,776 | 8,757 | -19% | 1 | 1 | 0% | 1,861 | 2,513 | +35% | 0 | 0 | — |
case-17 | fail→pass | 9,095 | 8,497 | -7% | 1 | 1 | 0% | 1,767 | 2,282 | +29% | 0 | 0 | — |
case-18 | fail→fail | 11,794 | 11,040 | -6% | 1 | 1 | 0% | 2,353 | 2,688 | +14% | 0 | 0 | — |
case-19 | fail→fail | 10,045 | 7,597 | -24% | 1 | 1 | 0% | 1,927 | 2,225 | +15% | 0 | 0 | — |
case-20 | fail→fail | 9,714 | 9,777 | +1% | 1 | 1 | 0% | 1,840 | 2,581 | +40% | 0 | 0 | — |
case-21 | fail→pass | 9,745 | 8,990 | -8% | 1 | 1 | 0% | 1,922 | 2,605 | +36% | 0 | 0 | — |
case-22 | fail→pass | 12,376 | 11,040 | -11% | 1 | 1 | 0% | 1,939 | 2,869 | +48% | 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. 22 cases were attempted. The headline lift of +45 percentage points is the difference between those two pass rates over the 22 comparable cases. 3 cases got worse with the skill loaded, and they are 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.