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/analyze-future-scenarios

Construct and compare plausible future/competitive/temporal/stress scenarios and test whether the research path remains robust across them.

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de-anthropocentric-research-engine
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$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill analyze-future-scenarios --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/analyze-future-scenarios

Context preview

The summary Claude sees to decide when to auto-load this skill.

Construct and compare plausible future/competitive/temporal/stress scenarios and test whether the research path remains robust across them.

SKILL.md

analyze-future-scenarios.SKILL.md
name: analyze-future-scenarios
description: "Construct and compare plausible future/competitive/temporal/stress scenarios and test whether the research path remains robust across them."

analyze-future-scenarios

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.

1. You MUST load skill `identify-scenario-drivers` to rank the horizon's high-impact uncertainties. 2. You MUST load skill `enumerate-dimension-values` to enumerate representative, boundary, and adversarial driver values. 3. You MUST load skill `evaluate-compatibility` to prune incompatible combinations while retaining near-boundary cases. 4. You MUST load skill `construct-scenario` to assemble distinct baseline, counterfactual, and extreme-but-plausible worlds. 5. You MUST load skill `evaluate-scenario-impact` to score the research path inside each fixed world and record failure triggers. 6. You MUST load skill `evaluate-scenario-robustness` to aggregate per-world results and expose fragile assumptions. 7. You MUST load skill `predict-competitive-move` to forecast credible competitor moves and preemption risk. 8. You MUST load skill `analyze-temporal-trajectory` to 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.

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Ships withde-anthropocentric-research-engine

The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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Repo: yogsoth-ai/de-anthropocentric-research-engine