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Get Started Free →Apply the Dynamic Capabilities framework (Teece et al., 1997) — sensing, seizing, and transforming — to analyze how firms adapt, integrate, and reconfigure competences in rapidly changing environments. Use this skill when the user needs to explain why some firms sustain advantage while others decline, evaluate organizational agility, distinguish operational from strategic capabilities, or when they ask 'how do we stay competitive as the market shifts', 'why did this firm fail to adapt', or 'what
.claude/skills/asgard-ai-platform-grad-strat-dynamic-cap/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -27% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -34% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-13 | ✗→✓ | ▲ Improved | -15% | 0% |
Dynamic capabilities are the firm's capacity to purposefully create, extend, or modify its resource base (Teece et al., 1997; Helfat et al., 2007). They explain HOW firms achieve and sustain competitive advantage in environments of rapid change — where RBV's static view is insufficient.
IRON LAW: Dynamic capabilities ≠ operational capabilities.
Operational capabilities enable current operations (doing things right).
Dynamic capabilities change the operational capability set (doing the right things).
Conflating them invalidates the analysis.Key assumptions:
| Cluster | Definition | Key Activities | |---------|-----------|----------------| | Sensing | Identify and shape opportunities and threats | Scanning, R&D, market research, customer listening | | Seizing | Mobilize resources to capture opportunities | Business model design, investment decisions, governance | | Transforming | Continuous renewal and reconfiguration | Restructuring, knowledge management, culture change |
markdown## Dynamic Capabilities Assessment: [Context] ### Environmental Dynamism - Rate of change: [low/moderate/high/hyper-competitive] - Key disruption vectors: ... ### Capability Audit | Capability | Type (Ordinary/Dynamic) | Cluster (S/S/T) | Strength | |------------|------------------------|------------------|----------| | [name] | [type] | [cluster] | [1-5] | ### Gap Analysis - Sensing gaps: ... - Seizing gaps: ... - Transforming gaps: ... ### Recommendations 1. [action per cluster]
A legacy retailer has strong operational logistics (ordinary capability) but weak sensing — no systematic process to track e-commerce trends. Recommendation: invest in digital market intelligence before seizing digital channel opportunities.
Labeling "innovation" as a dynamic capability without specifying which cluster it belongs to or how it differs from routine R&D operations. Dynamic capabilities must be tied to specific sensing/seizing/transforming activities.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 33,325 | 36,947 | +11% | 1 | 1 | 0% | 5,034 | 3,667 | -27% | 0 | 0 | — |
case-02 | fail→pass | 37,463 | 20,384 | -46% | 1 | 1 | 0% | 6,081 | 4,015 | -34% | 0 | 0 | — |
case-03 | fail→fail | 26,252 | 17,119 | -35% | 1 | 1 | 0% | 4,124 | 3,653 | -11% | 0 | 0 | — |
case-04 | pass→pass | 22,660 | 18,955 | -16% | 1 | 1 | 0% | 3,378 | 4,026 | +19% | 0 | 0 | — |
case-05 | pass→pass | 18,080 | 16,298 | -10% | 1 | 1 | 0% | 3,020 | 3,429 | +14% | 0 | 0 | — |
case-06 | pass→pass | 13,044 | 11,152 | -15% | 1 | 1 | 0% | 2,170 | 2,863 | +32% | 0 | 0 | — |
case-07 | fail→fail | 24,722 | 16,987 | -31% | 1 | 1 | 0% | 3,305 | 3,626 | +10% | 0 | 0 | — |
case-08 | fail→fail | 50,634 | 21,148 | -58% | 1 | 1 | 0% | 8,239 | 4,100 | -50% | 0 | 0 | — |
case-09 | fail→pass | 23,271 | 19,620 | -16% | 1 | 1 | 0% | 3,634 | 3,911 | +8% | 0 | 0 | — |
case-10 | fail→fail | 44,897 | 18,357 | -59% | 1 | 1 | 0% | 6,299 | 3,696 | -41% | 0 | 0 | — |
case-11 | fail→fail | 27,568 | 19,139 | -31% | 1 | 1 | 0% | 3,941 | 3,935 | -0% | 0 | 0 | — |
case-12 | fail→pass | 22,679 | 20,856 | -8% | 1 | 1 | 0% | 3,423 | 3,708 | +8% | 0 | 0 | — |
case-13 | fail→pass | 37,719 | 24,736 | -34% | 1 | 1 | 0% | 5,085 | 4,314 | -15% | 0 | 0 | — |
case-14 | fail→fail | 46,410 | 20,048 | -57% | 1 | 1 | 0% | 6,416 | 3,918 | -39% | 0 | 0 | — |
case-15 | fail→pass | 16,601 | 19,756 | +19% | 1 | 1 | 0% | 2,559 | 3,598 | +41% | 0 | 0 | — |
case-16 | fail→fail | 24,144 | 22,040 | -9% | 1 | 1 | 0% | 3,726 | 4,212 | +13% | 0 | 0 | — |
case-17 | fail→fail | 35,341 | 19,315 | -45% | 1 | 1 | 0% | 5,379 | 3,466 | -36% | 0 | 0 | — |
case-18 | fail→fail | 28,981 | 30,199 | +4% | 1 | 1 | 0% | 4,893 | 4,188 | -14% | 0 | 0 | — |
case-19 | fail→fail | 28,953 | 28,592 | -1% | 1 | 1 | 0% | 4,285 | 5,086 | +19% | 0 | 0 | — |
case-20 | fail→pass | 30,450 | 15,156 | -50% | 1 | 1 | 0% | 4,702 | 3,423 | -27% | 0 | 0 | — |
case-21 | fail→pass | 27,174 | 19,660 | -28% | 1 | 1 | 0% | 4,581 | 3,868 | -16% | 0 | 0 | — |
case-22 | fail→fail | 24,584 | 22,344 | -9% | 1 | 1 | 0% | 3,726 | 4,277 | +15% | 0 | 0 | — |
case-23 | fail→fail | 24,579 | 16,105 | -34% | 1 | 1 | 0% | 4,306 | 3,404 | -21% | 0 | 0 | — |
case-24 | fail→pass | 35,210 | 17,833 | -49% | 1 | 1 | 0% | 4,860 | 3,613 | -26% | 0 | 0 | — |
case-25 | fail→fail | 55,967 | 16,445 | -71% | 1 | 1 | 0% | 7,496 | 3,592 | -52% | 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. 25 cases were attempted. The headline lift of +36 percentage points is the difference between those two pass rates over the 25 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.