temporal-scenario
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npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/temporal-scenarioProjects research evolution across time horizons using technology maturity curves
- Analyzes short, medium, and long-term research evolution with timeline projections
- Leverages scenario drivers, timeline projection, and narrative construction tools
- Maps technology S-curves, adoption dynamics, and paradigm shift timing
- Delivers scenario narratives with impact assessments and robustness scores
SKILL.md
.github/skills/temporal-scenarioView on GitHub ↗
--- name: temporal-scenario description: "How does it evolve over time? — Short/medium/long-term timeline projection with technology maturity curves" version: 1.0.0 category: experiment-execution type: strategy used-by: scenario-planning sops: - scenario-driver-identification - timeline-projection - scenario-narrative-construction - scenario-impact-assessment - robustness-scoring - scenario-synthesis tactics: - strategy-robustness-testing --- # Strategy: Temporal Scenario ## Methodology Temporal Scenario Planning with Technology Maturity Curves. Project how the research landscape evolves across multiple time horizons (short: 6 months, medium: 2 years, long: 5+ years). Map technology S-curves, adoption dynamics, and paradigm shift timing. Key principles: - **Multi-horizon**: Separate analysis for short, medium, and long term - **S-curve awareness**: Technologies follow predictable maturity patterns - **Paradigm sensitivity**: Identify potential paradigm shifts and their timing - **Path dependency**: Current decisions constrain future options ## Execution Flow 1. **Identify temporal drivers** → spawn `scenario-driver-identification` - Input: research context, focus on time-dependent factors - Output: drivers with temporal dynamics (maturation rates, adoption curves) 2. **Project timelines** → spawn `timeline-projection` - Input: temporal drivers, current maturity levels - Output: multi-horizon projections with uncertainty bands 3. **Construct temporal narratives** → spawn `scenario-narrative-construction` (×3 horizons) - Input: timeline projections, horizon-specific drivers - Output: narrative per time horizon 4. **Assess impact** → spawn `scenario-impact-assessment` (per horizon) - Input: temporal narrative, research approach, decision timing - Output: time-dependent impact analysis 5. **Score robustness** → spawn `robustness-scoring` - Input: all temporal assessments - Output: temporal robustness index, optimal timing windows 6. **Synthesize** → spawn `scenario-synthesis` - Input: temporal scenarios, timing recommendations - Output: temporal strategy with decision points ## Budget Gate | Step | Token Budget | Notes | |------|-------------|-------| | Driver identification | 8K | Time-dynamics focused | | Timeline projection | 15K | Multi-horizon + S-curves | | Narrative construction | 12K × 3 | Per horizon | | Impact assessment | 10K × 3 | Per horizon | | Robustness scoring | 10K | Temporal sensitivity | | Synthesis | 12K | Timing recommendations |
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