Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Calculate TAM/SAM/SOM for market opportunities using top-down, bottom-up, and value theory methodologies. Use this skill when sizing markets, estimating addressable revenue, validating market opportunity for a new venture, or building investor-ready market analysis for a startup pitch or business plan.
.claude/skills/bilal140202-market-sizing-analysis/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-12 | ✗→✓ | ▲ Improved | -2% | 0% |
| case-19 | ✗→✓ | ▲ Improved | -9% | 0% |
| case-01 | ✓→✓ | ＝ Same ✓ | 16% | 0% |
| case-02 | ✓→✓ | ＝ Same ✓ | 42% | 0% |
| case-03 | ✓→✓ | ＝ Same ✓ | 45% | 0% |
Comprehensive market sizing methodologies for calculating Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) for startup opportunities.
Market sizing provides the foundation for startup strategy, fundraising, and business planning. Calculate market opportunity using three complementary methodologies: top-down (industry reports), bottom-up (customer segment calculations), and value theory (willingness to pay).
TAM (Total Addressable Market)
SAM (Serviceable Available Market)
SOM (Serviceable Obtainable Market)
Top-Down Analysis
Bottom-Up Analysis
Value Theory
Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-12 | fail→pass | 19,264 | 16,264 | -16% | 1 | 1 | 0% | 2,881 | 2,831 | -2% | 0 | 0 | — |
case-01 | pass→pass | 15,657 | 16,649 | +6% | 1 | 1 | 0% | 2,685 | 3,103 | +16% | 0 | 0 | — |
case-02 | pass→pass | 14,514 | 17,870 | +23% | 1 | 1 | 0% | 2,869 | 4,062 | +42% | 0 | 0 | — |
case-03 | pass→pass | 15,028 | 18,388 | +22% | 1 | 1 | 0% | 2,926 | 4,240 | +45% | 0 | 0 | — |
case-04 | pass→pass | 14,157 | 12,558 | -11% | 1 | 1 | 0% | 2,326 | 2,378 | +2% | 0 | 0 | — |
case-05 | pass→pass | 13,578 | 12,768 | -6% | 1 | 1 | 0% | 2,028 | 2,434 | +20% | 0 | 0 | — |
case-06 | pass→pass | 13,530 | 11,441 | -15% | 1 | 1 | 0% | 2,228 | 2,189 | -2% | 0 | 0 | — |
case-07 | pass→pass | 15,269 | 7,984 | -48% | 1 | 1 | 0% | 2,290 | 1,581 | -31% | 0 | 0 | — |
case-08 | pass→pass | 18,406 | 24,469 | +33% | 1 | 1 | 0% | 3,070 | 3,183 | +4% | 0 | 0 | — |
case-09 | pass→pass | 12,862 | 10,415 | -19% | 1 | 1 | 0% | 2,066 | 2,050 | -1% | 0 | 0 | — |
case-10 | pass→pass | 14,115 | 25,946 | +84% | 1 | 1 | 0% | 2,136 | 2,532 | +19% | 0 | 0 | — |
case-11 | fail→fail | 12,945 | 13,314 | +3% | 1 | 1 | 0% | 1,977 | 2,367 | +20% | 0 | 0 | — |
case-13 | pass→pass | 17,695 | 13,144 | -26% | 1 | 1 | 0% | 2,955 | 2,624 | -11% | 0 | 0 | — |
case-14 | pass→pass | 14,306 | 11,709 | -18% | 1 | 1 | 0% | 2,475 | 2,354 | -5% | 0 | 0 | — |
case-15 | pass→pass | 8,420 | 22,640 | +169% | 1 | 1 | 0% | 1,384 | 1,823 | +32% | 0 | 0 | — |
case-16 | pass→pass | 14,876 | 17,835 | +20% | 1 | 1 | 0% | 2,151 | 3,224 | +50% | 0 | 0 | — |
case-17 | pass→pass | 8,096 | 6,385 | -21% | 1 | 1 | 0% | 1,301 | 1,322 | +2% | 0 | 0 | — |
case-18 | pass→pass | 15,023 | 13,043 | -13% | 1 | 1 | 0% | 2,230 | 2,456 | +10% | 0 | 0 | — |
case-19 | fail→pass | 10,559 | 7,274 | -31% | 1 | 1 | 0% | 1,683 | 1,539 | -9% | 0 | 0 | — |
case-20 | pass→pass | 14,357 | 13,087 | -9% | 1 | 1 | 0% | 2,380 | 2,385 | +0% | 0 | 0 | — |
case-21 | pass→pass | 7,131 | 5,891 | -17% | 1 | 1 | 0% | 1,135 | 1,411 | +24% | 0 | 0 | — |
case-22 | pass→pass | 15,455 | 15,367 | -1% | 1 | 1 | 0% | 2,503 | 2,875 | +15% | 0 | 0 | — |
case-23 | pass→pass | 9,802 | 7,525 | -23% | 1 | 1 | 0% | 1,628 | 1,537 | -6% | 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 +9 percentage points is the difference between those two pass rates over the 23 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.