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Get Started Free →Apply structured problem-solving using MECE principle, issue trees, hypothesis-driven approach, and the Pyramid Principle. Use this skill when the user faces a complex, ambiguous problem and needs to decompose it systematically, structure a consulting-style analysis, or organize recommendations clearly — even if they say 'where do I start', 'this problem is too big', 'help me break this down', or 'structure my thinking'.
.claude/skills/asgard-ai-platform-meta-structured-problem/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 19% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -16% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -7% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 66% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 31% | 0% |
IRON LAW: MECE or It's Not Structured
Every decomposition must be MECE:
- Mutually Exclusive: No overlap between categories
- Collectively Exhaustive: No gaps — all possibilities covered
"Revenue = New customers + Existing customers" is MECE ✓
"Revenue = Online + Enterprise + Growth" is NOT MECE ✗ (overlapping)Issue Tree: Decompose a question into sub-questions, MECE at each level
"Why is profit declining?"
├── Revenue declining?
│ ├── Volume down?
│ │ ├── New customer acquisition down?
│ │ └── Existing customer churn up?
│ └── Price down?
│ ├── Discounting increased?
│ └── Mix shift to lower-priced products?
└── Costs increasing?
├── COGS up?
└── OpEx up?Hypothesis-Driven Approach: Instead of exploring everything, state a hypothesis and test it
Pyramid Principle (Barbara Minto): Structure communication top-down
80/20 Rule: Focus on the 20% of analysis that drives 80% of the answer. Don't over-analyze secondary branches of the issue tree.
markdown# Structured Analysis: {Problem} ## Problem Statement {One sentence, specific and measurable} ## Issue Tree {MECE decomposition — text or visual} ## Hypothesis {Initial hypothesis with rationale} ## Evidence | Branch | Hypothesis | Evidence | Verdict | |--------|-----------|---------|---------| | {branch} | {sub-hypothesis} | {data found} | Confirmed/Rejected | ## Synthesis (Pyramid Structure) **Recommendation**: {answer first} **Supporting Arguments**: 1. {argument 1 with evidence} 2. {argument 2 with evidence} 3. {argument 3 with evidence} ## Next Steps 1. {action item}
Scenario: "Why is our food delivery app losing market share?"
Issue tree (MECE):
Market share declining
├── Our growth slowing?
│ ├── New user acquisition down?
│ │ ├── Marketing spend reduced?
│ │ └── Conversion rate dropped?
│ └── Existing user activity down?
│ ├── Order frequency declining?
│ └── Users churning?
└── Competitors growing faster?
├── New entrant capturing share?
└── Existing competitor accelerating?Hypothesis: "Existing user activity is down because order frequency declined after the delivery fee increase." Evidence: Order frequency dropped 22% in the month after fee increase. ✓
Pyramid: "Reverse the delivery fee increase for high-frequency users. Order frequency dropped 22% post-increase, and 60% of lost orders came from users who ordered 3+/week. A loyalty tier with waived fees for frequent users would recover an estimated 15% of lost share at a cost of NT$X/month."
references/issue-tree-templates.mdreferences/pyramid-principle.md| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 20,206 | 31,576 | +56% | 1 | 1 | 0% | 3,594 | 4,289 | +19% | 0 | 0 | — |
case-02 | fail→pass | 32,160 | 20,298 | -37% | 1 | 1 | 0% | 5,188 | 4,372 | -16% | 0 | 0 | — |
case-03 | fail→pass | 34,558 | 19,299 | -44% | 1 | 1 | 0% | 4,564 | 4,229 | -7% | 0 | 0 | — |
case-04 | pass→pass | 12,639 | 16,756 | +33% | 1 | 1 | 0% | 2,123 | 3,828 | +80% | 0 | 0 | — |
case-05 | fail→fail | 11,065 | 10,734 | -3% | 1 | 1 | 0% | 1,786 | 2,684 | +50% | 0 | 0 | — |
case-06 | fail→pass | 9,646 | 7,935 | -18% | 1 | 1 | 0% | 1,439 | 2,389 | +66% | 0 | 0 | — |
case-07 | fail→pass | 11,941 | 9,411 | -21% | 1 | 1 | 0% | 1,962 | 2,576 | +31% | 0 | 0 | — |
case-08 | pass→pass | 17,331 | 12,488 | -28% | 1 | 1 | 0% | 2,516 | 3,259 | +30% | 0 | 0 | — |
case-09 | pass→pass | 17,843 | 19,266 | +8% | 1 | 1 | 0% | 2,830 | 3,922 | +39% | 0 | 0 | — |
case-10 | fail→pass | 19,255 | 13,775 | -28% | 1 | 1 | 0% | 3,179 | 3,508 | +10% | 0 | 0 | — |
case-11 | pass→pass | 12,344 | 10,042 | -19% | 1 | 1 | 0% | 1,795 | 2,764 | +54% | 0 | 0 | — |
case-12 | pass→pass | 13,849 | 11,107 | -20% | 1 | 1 | 0% | 2,050 | 2,934 | +43% | 0 | 0 | — |
case-13 | fail→pass | 14,178 | 15,019 | +6% | 1 | 1 | 0% | 1,951 | 3,517 | +80% | 0 | 0 | — |
case-14 | pass→pass | 14,411 | 16,291 | +13% | 1 | 1 | 0% | 2,582 | 3,924 | +52% | 0 | 0 | — |
case-15 | fail→pass | 15,148 | 12,080 | -20% | 1 | 1 | 0% | 2,555 | 3,209 | +26% | 0 | 0 | — |
case-16 | fail→pass | 6,717 | 9,448 | +41% | 1 | 1 | 0% | 1,101 | 2,689 | +144% | 0 | 0 | — |
case-17 | fail→pass | 9,653 | 8,365 | -13% | 1 | 1 | 0% | 1,490 | 2,641 | +77% | 0 | 0 | — |
case-18 | pass→pass | 15,138 | 15,500 | +2% | 1 | 1 | 0% | 2,520 | 3,727 | +48% | 0 | 0 | — |
case-19 | fail→pass | 9,869 | 10,268 | +4% | 1 | 1 | 0% | 1,581 | 1,995 | +26% | 0 | 0 | — |
case-20 | fail→pass | 12,031 | 10,636 | -12% | 1 | 1 | 0% | 1,873 | 2,891 | +54% | 0 | 0 | — |
case-21 | pass→fail | 16,389 | 14,591 | -11% | 1 | 1 | 0% | 2,953 | 3,665 | +24% | 0 | 0 | — |
case-22 | pass→pass | 11,282 | 12,355 | +10% | 1 | 1 | 0% | 1,891 | 3,105 | +64% | 0 | 0 | — |
case-23 | pass→fail | 29,060 | 26,015 | -10% | 1 | 1 | 0% | 4,121 | 6,358 | +54% | 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 +43 percentage points is the difference between those two pass rates over the 23 comparable cases. 2 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.