analyze-future-scenarios
SkillDev toolsUse this analyze claude skill to build and compare future scenarios and test whether a research plan holds up.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the analyze-future-scenarios skill
About this skill
Construct and compare plausible future/competitive/temporal/stress scenarios and test whether the research path remains robust across them.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/analyze-future-scenarios/SKILL.md and read by ahel’s review.
Purpose
Construct and compare plausible future/competitive/temporal/stress scenarios and test whether the research path remains robust across them.
Input contract
required: [research_path, scenario_axes, uncertainty_drivers]
optional: [assumptions, prior_findings, evidence_updates]
constraints: [consume named scientific objects; preserve provenance; keep unresolved uncertainty visible]
Execution protocol
Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.
- You MUST load skill
identify-scenario-driversto rank the horizon's high-impact uncertainties. - You MUST load skill
enumerate-dimension-valuesto enumerate representative, boundary, and adversarial driver values. - You MUST load skill
evaluate-compatibilityto prune incompatible combinations while retaining near-boundary cases. - You MUST load skill
construct-scenarioto assemble distinct baseline, counterfactual, and extreme-but-plausible worlds. - You MUST load skill
evaluate-scenario-impactto score the research path inside each fixed world and record failure triggers. - You MUST load skill
evaluate-scenario-robustnessto aggregate per-world results and expose fragile assumptions. - You MUST load skill
predict-competitive-moveto forecast credible competitor moves and preemption risk. - You MUST load skill
analyze-temporal-trajectoryto order outcomes through time and reveal regime changes.
Deviation: reorder only when a dependency is already satisfied or unavailable; record the reason and confidence effect.
Output contract
produces: [scenario_set, impact_comparison, robustness_assessment]
delta_fields: [findings, decisions]
Thresholds and quality gates
- Each output is traceable to an input object, operation, and evidence reference.
- Scope, assumptions, and unresolved alternatives remain explicit.
- Retain $\alpha$ 0.05 and power 0.8 wherever the predeclared statistical design requires them.
Failure and counterexamples
Stop synthesis when a required object is absent, a precondition is violated, or a counterexample invalidates the proposed conclusion; return the partial delta with the failure recorded.
Provenance map
- intermediate: experiment-execution/scenario-planning [campaign]
- resolved: morphological-scenario
- resolved: narrative-scenario
- resolved: stress-scenario
- resolved: competitive-scenario
- resolved: temporal-scenario
- resolved: parameter-space-construction
- resolved: cross-consistency-filtering
- resolved: strategy-robustness-testing
Preserved source criteria ledger
| source | criterion | treatment |
|---|---|---|
| resolved v3 entries above | node-specific criteria | retained and specialized to the v4 object contract |
| experiment-execution/statistical-testing | $\alpha$ = 0.05 | fixed value retained where applicable |
| experiment-execution/sample-size-estimation | power = 0.8 | fixed value retained where applicable |
Context checkpoint / Delta notes
Return the node-specific research-state delta and preserve findings, evidence updates, uncertainties, decisions, open questions, and recommended jumps as applicable.
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
analyze-future-scenarios- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine