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Get Started Free →Apply Rogers' Diffusion of Innovations theory to analyze how new products, ideas, or technologies spread through populations. Use this skill when the user needs to plan adoption strategy, segment adopters, cross the chasm from early adopters to mainstream, or predict adoption curves — even if they say 'how do we get more people to use this', 'why isn't our product taking off', or 'how do we reach the mainstream'.
.claude/skills/asgard-ai-platform-soc-innovation-diffusion/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 38% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 27% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 72% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 58% | 0% |
Rogers' theory describes how innovations spread through a population in a predictable S-curve pattern, with five adopter categories that require different strategies. Moore's "Crossing the Chasm" extends this by identifying the critical gap between early adopters and the early majority.
IRON LAW: The Chasm Is Real — Early Adopters ≠ Mainstream
Visionaries (early adopters) buy because it's NEW and they can tolerate
imperfections. Pragmatists (early majority) buy because it WORKS and
others already use it. These are fundamentally different buyer psychologies.
Success with early adopters does NOT predict mainstream success.
The strategy that wins innovators will FAIL with the majority.| Category | % of Population | Motivation | Strategy | |----------|----------------|-----------|----------| | Innovators | 2.5% | Technology enthusiasts, thrill of the new | Tech specs, early access, "be the first" | | Early Adopters | 13.5% | Visionaries, strategic advantage seekers | Vision alignment, ROI potential, case studies | | Early Majority | 34% | Pragmatists, want proven solutions | References, complete solution, low risk | | Late Majority | 34% | Conservatives, follow the herd | Industry standard, peer pressure, simplicity | | Laggards | 16% | Skeptics, tradition-bound | Necessity, no alternative, bundled with required product |
The gap between Early Adopters (16%) and Early Majority (34%) is where most innovations die. To cross:
| Factor | Definition | Faster Adoption When... | |--------|-----------|----------------------| | Relative advantage | How much better than the current solution | Much better, obvious improvement | | Compatibility | Fit with existing values, practices, infrastructure | Minimal change required | | Complexity | Ease of understanding and use | Simple to grasp and use | | Trialability | Can it be tested before committing? | Free trial, freemium, pilot available | | Observability | Can others see the results? | Visible outcomes, shareable results |
markdown# Diffusion Analysis: {Innovation} ## Innovation Profile | Factor | Assessment | Implication | |--------|-----------|------------| | Relative advantage | H/M/L | {detail} | | Compatibility | H/M/L | {detail} | | Complexity | H/M/L (lower = better) | {detail} | | Trialability | H/M/L | {detail} | | Observability | H/M/L | {detail} | ## Current Adoption Stage - Estimated penetration: {X%} - Current adopter category: {Innovators / Early Adopters / Chasm / Early Majority / etc.} ## Chasm Strategy (if applicable) - Beachhead segment: {specific niche} - Whole product gaps: {what's missing for a complete solution} - Reference strategy: {how to get pragmatist references} ## Adoption Acceleration Plan 1. {action to improve weakest adoption factor}
Scenario: Adoption analysis for a new AI code review tool
references/chasm-strategy.md| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-11 | pass→pass | 15,079 | 12,634 | -16% | 1 | 1 | 0% | 2,457 | 3,095 | +26% | 0 | 0 | — |
case-01 | fail→pass | 29,889 | 20,871 | -30% | 1 | 1 | 0% | 3,884 | 4,192 | +8% | 0 | 0 | — |
case-02 | fail→fail | 30,414 | 20,175 | -34% | 1 | 1 | 0% | 4,812 | 4,130 | -14% | 0 | 0 | — |
case-03 | pass→pass | 30,900 | 13,367 | -57% | 1 | 1 | 0% | 4,230 | 3,608 | -15% | 0 | 0 | — |
case-04 | pass→fail | 15,676 | 30,961 | +98% | 1 | 1 | 0% | 3,037 | 6,658 | +119% | 0 | 0 | — |
case-05 | pass→pass | 9,620 | 13,390 | +39% | 1 | 1 | 0% | 1,422 | 3,146 | +121% | 0 | 0 | — |
case-06 | pass→fail | 15,019 | 29,105 | +94% | 1 | 1 | 0% | 2,391 | 5,218 | +118% | 0 | 0 | — |
case-07 | fail→pass | 18,329 | 15,883 | -13% | 1 | 1 | 0% | 2,766 | 3,804 | +38% | 0 | 0 | — |
case-08 | pass→pass | 14,252 | 13,459 | -6% | 1 | 1 | 0% | 1,984 | 3,065 | +54% | 0 | 0 | — |
case-09 | pass→pass | 15,792 | 12,951 | -18% | 1 | 1 | 0% | 2,272 | 3,277 | +44% | 0 | 0 | — |
case-10 | pass→pass | 15,192 | 12,619 | -17% | 1 | 1 | 0% | 2,340 | 3,242 | +39% | 0 | 0 | — |
case-12 | fail→pass | 10,601 | 6,338 | -40% | 1 | 1 | 0% | 1,682 | 2,138 | +27% | 0 | 0 | — |
case-13 | pass→pass | 17,002 | 13,966 | -18% | 1 | 1 | 0% | 2,467 | 3,196 | +30% | 0 | 0 | — |
case-14 | pass→pass | 14,156 | 12,968 | -8% | 1 | 1 | 0% | 2,222 | 3,163 | +42% | 0 | 0 | — |
case-15 | pass→pass | 10,708 | 13,263 | +24% | 1 | 1 | 0% | 1,755 | 3,270 | +86% | 0 | 0 | — |
case-16 | fail→pass | 12,689 | 14,724 | +16% | 1 | 1 | 0% | 2,066 | 3,544 | +72% | 0 | 0 | — |
case-17 | pass→pass | 19,277 | 19,106 | -1% | 1 | 1 | 0% | 2,644 | 3,778 | +43% | 0 | 0 | — |
case-18 | pass→pass | 9,889 | 10,739 | +9% | 1 | 1 | 0% | 1,584 | 2,877 | +82% | 0 | 0 | — |
case-19 | pass→pass | 9,579 | 12,247 | +28% | 1 | 1 | 0% | 1,670 | 2,909 | +74% | 0 | 0 | — |
case-20 | fail→pass | 13,556 | 13,289 | -2% | 1 | 1 | 0% | 2,068 | 3,266 | +58% | 0 | 0 | — |
case-21 | pass→pass | 14,867 | 15,429 | +4% | 1 | 1 | 0% | 2,468 | 3,686 | +49% | 0 | 0 | — |
case-22 | fail→pass | 24,818 | 14,359 | -42% | 1 | 1 | 0% | 3,889 | 3,520 | -9% | 0 | 0 | — |
case-23 | fail→pass | 21,914 | 15,845 | -28% | 1 | 1 | 0% | 3,305 | 3,383 | +2% | 0 | 0 | — |
case-24 | fail→pass | 20,020 | 17,044 | -15% | 1 | 1 | 0% | 3,179 | 3,594 | +13% | 0 | 0 | — |
case-25 | pass→pass | 16,068 | 16,013 | -0% | 1 | 1 | 0% | 2,365 | 3,525 | +49% | 0 | 0 | — |
case-26 | pass→pass | 7,183 | 3,169 | -56% | 1 | 1 | 0% | 1,148 | 1,713 | +49% | 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. 26 cases were attempted. The headline lift of +23 percentage points is the difference between those two pass rates over the 26 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.