scenario-planning
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npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/scenario-planningBefore launching this campaign, verify:
SKILL.md
.github/skills/scenario-planningView on GitHub ↗
--- name: scenario-planning description: "What might the future look like — construct multiple future scenarios, assess research approach robustness under different assumptions" version: 1.0.0 category: experiment-execution type: campaign strategies: - morphological-scenario - narrative-scenario - stress-scenario - competitive-scenario - temporal-scenario --- # Campaign: Scenario Planning ## HARD-GATE Before launching this campaign, verify: 1. **Research direction defined** — A clear research approach or hypothesis exists to stress-test 2. **Uncertainty acknowledged** — The team recognizes key unknowns that could invalidate the approach 3. **Time horizon specified** — Short (6mo), medium (2yr), or long (5yr+) planning window declared 4. **Stakeholder buy-in** — Decision-makers agree that scenario analysis will inform strategy If any gate fails, STOP and resolve before proceeding. ## Campaign Goal Construct a portfolio of plausible future scenarios spanning the uncertainty space, then assess how robust our research approach is under each scenario. The output is a robustness index plus contingency triggers that tell us when to pivot. ## Strategy Selection | Strategy | Question | When to Use | |----------|----------|-------------| | morphological-scenario | What are all possible combinations? | Systematic enumeration of all factor combinations needed | | narrative-scenario | What is the story of each future? | Rich qualitative understanding of scenario dynamics needed | | stress-scenario | What is the worst case? | Risk assessment and failure preparedness needed | | competitive-scenario | What will competitors do? | Competitive landscape awareness needed | | temporal-scenario | How does it evolve over time? | Technology evolution and timing decisions needed | ## Budget Gate | Component | Token Budget | Subagent Calls | |-----------|-------------|----------------| | Driver identification | 8K | 1 | | Parameter enumeration | 10K | 1 | | Consistency filtering | 15K | 2 | | Narrative construction | 12K per scenario | 1 per scenario | | Impact assessment | 10K per scenario | 1 per scenario | | Robustness scoring | 8K | 1 | | Synthesis | 12K | 1 | | **Total (5 scenarios)** | **~130K** | **~15** | ## Context Management - Each SOP runs as a subagent with isolated context - Strategy-level orchestrator maintains scenario registry - Pass scenario IDs (not full content) between steps - Final synthesis receives summaries, not raw outputs ## Minimum Yield A successful campaign produces at minimum: - 3+ internally consistent scenarios spanning the uncertainty space - Robustness score for the research approach (0-100) - At least 1 identified pivot trigger with threshold condition - Contingency action for each scenario where robustness < 50
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