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Get Started Free →Plan Southeast Asia market entry including mode selection, regulatory requirements, cultural research, and go-to-market timeline. Use this skill when the user is considering expansion to SEA, evaluating entry modes (marketplace vs direct vs distributor), or conducting market feasibility studies — even if they say 'should we enter Vietnam', 'how to sell in Indonesia', 'SEA expansion plan', or 'find a local distributor'.
.claude/skills/asgard-ai-platform-xborder-sea-entry/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -39% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -29% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 37% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 34% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 28% | 0% |
IRON LAW: One Country at a Time
SEA is 10+ countries with different languages, regulations, payment systems,
and consumer behaviors. Entering 3 countries simultaneously with limited
resources guarantees failure in all 3. Pick ONE beachhead, prove the model,
then expand. The learnings from country 1 accelerate country 2.| Mode | Investment | Control | Speed | Risk | Best For | |------|----------|---------|-------|------|----------| | E-commerce marketplace (Shopee/Lazada) | Low | Low | Fast (weeks) | Low | Testing demand, consumer products | | Independent e-commerce (own site) | Medium | High | Medium (months) | Medium | Brand building, D2C, subscriptions | | Local distributor / agent | Low-Medium | Low | Medium | Medium | Physical retail, B2B, complex products | | Joint venture | High | Shared | Slow | Medium | Regulated industries, need local partner | | Wholly-owned subsidiary | High | Full | Slow (6-12 months) | High | Long-term commitment, full control |
Score each candidate country (1-5) on:
| Criterion | Weight | Questions to Answer | |-----------|--------|-------------------| | Market size & growth | 25% | GDP, category size, growth rate, digital penetration | | Competition | 20% | How many players? How strong? Any gaps? | | Regulatory ease | 20% | Foreign ownership rules, import regulations, tax complexity | | Cultural proximity | 15% | Language barriers, consumer behavior similarity, existing networks | | Logistics readiness | 10% | Delivery infrastructure, payment ecosystem, returns handling | | Cost of entry | 10% | Setup costs, marketing costs, talent availability |
Consumer research:
Competitive landscape:
Regulatory:
| Month | Milestone | |-------|-----------| | 1 | Country research + marketplace account application | | 2 | Product localization (listings, pricing, images) | | 3 | Initial inventory shipment + soft launch (10-20 SKUs) | | 4-6 | Marketing ramp: marketplace ads, KOL partnerships | | 7-9 | Optimize based on data: best sellers, pricing, logistics | | 10-12 | Evaluate: hit targets → scale / miss → pivot or exit |
markdown# SEA Market Entry Plan: {Company} → {Target Country} ## Country Selection | Country | Market | Competition | Regulation | Culture | Logistics | Score | |---------|--------|------------|-----------|---------|-----------|-------| | {country} | {1-5} | {1-5} | {1-5} | {1-5} | {1-5} | {weighted} | ## Selected: {Country} - Entry mode: {marketplace / distributor / subsidiary} - Rationale: {why this country and mode} ## Market Opportunity - Category size: ${X} - Growth: {X%} - Target segment: {who} - Competitive gap: {what's underserved} ## Go-to-Market Plan | Phase | Action | Timeline | Budget | |-------|--------|----------|--------| | 1 | {action} | {months} | ${X} | ## Key Risks & Mitigation | Risk | Likelihood | Mitigation | |------|-----------|-----------| | {risk} | H/M/L | {action} | ## Success Criteria (12-month) - Revenue: ${X} - Customers: {N} - Break-even: month {N}
references/sea-regulations.md| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | pass→pass | 16,429 | 35,741 | +118% | 1 | 1 | 0% | 2,805 | 4,493 | +60% | 0 | 0 | — |
case-05 | pass→pass | 23,314 | 20,582 | -12% | 1 | 1 | 0% | 4,730 | 5,668 | +20% | 0 | 0 | — |
case-01 | fail→pass | 48,732 | 53,321 | +9% | 1 | 1 | 0% | 8,306 | 5,043 | -39% | 0 | 0 | — |
case-02 | fail→pass | 45,572 | 28,033 | -38% | 1 | 1 | 0% | 7,262 | 5,161 | -29% | 0 | 0 | — |
case-03 | fail→fail | 57,813 | 23,648 | -59% | 1 | 1 | 0% | 8,159 | 5,450 | -33% | 0 | 0 | — |
case-06 | pass→pass | 23,997 | 34,439 | +44% | 1 | 1 | 0% | 3,439 | 5,757 | +67% | 0 | 0 | — |
case-07 | pass→pass | 21,871 | 21,640 | -1% | 1 | 1 | 0% | 3,586 | 4,735 | +32% | 0 | 0 | — |
case-08 | fail→pass | 25,353 | 24,176 | -5% | 1 | 1 | 0% | 3,333 | 4,558 | +37% | 0 | 0 | — |
case-09 | pass→pass | 22,689 | 19,005 | -16% | 1 | 1 | 0% | 3,576 | 4,137 | +16% | 0 | 0 | — |
case-10 | pass→pass | 18,136 | 14,638 | -19% | 1 | 1 | 0% | 2,244 | 3,438 | +53% | 0 | 0 | — |
case-11 | fail→fail | 18,522 | 17,243 | -7% | 1 | 1 | 0% | 2,781 | 3,997 | +44% | 0 | 0 | — |
case-12 | pass→pass | 15,975 | 14,220 | -11% | 1 | 1 | 0% | 2,239 | 3,432 | +53% | 0 | 0 | — |
case-13 | fail→fail | 47,874 | 17,197 | -64% | 1 | 1 | 0% | 7,843 | 4,135 | -47% | 0 | 0 | — |
case-14 | pass→pass | 13,717 | 12,080 | -12% | 1 | 1 | 0% | 1,830 | 2,808 | +53% | 0 | 0 | — |
case-15 | fail→pass | 20,238 | 19,976 | -1% | 1 | 1 | 0% | 3,374 | 4,526 | +34% | 0 | 0 | — |
case-16 | fail→fail | 19,567 | 24,056 | +23% | 1 | 1 | 0% | 3,086 | 4,238 | +37% | 0 | 0 | — |
case-17 | fail→pass | 18,567 | 18,386 | -1% | 1 | 1 | 0% | 2,881 | 3,698 | +28% | 0 | 0 | — |
case-18 | pass→pass | 16,373 | 14,341 | -12% | 1 | 1 | 0% | 2,414 | 3,203 | +33% | 0 | 0 | — |
case-19 | fail→pass | 21,649 | 18,927 | -13% | 1 | 1 | 0% | 2,878 | 4,235 | +47% | 0 | 0 | — |
case-20 | pass→pass | 14,649 | 14,693 | +0% | 1 | 1 | 0% | 2,213 | 3,535 | +60% | 0 | 0 | — |
case-21 | fail→fail | 23,495 | 15,814 | -33% | 1 | 1 | 0% | 3,153 | 3,777 | +20% | 0 | 0 | — |
case-22 | fail→fail | 17,400 | 12,531 | -28% | 1 | 1 | 0% | 2,459 | 3,037 | +24% | 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 +27 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.