formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced…
What will competitors do? — Competitive method progress prediction and
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill competitive-scenario --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/competitive-scenarioContext preview
The summary Claude sees to decide when to auto-load this skill.
What will competitors do? — Competitive method progress prediction and
name: competitive-scenario description: What will competitors do? — Competitive method progress prediction and time window analysis version: 1.0.0 category: experiment-execution type: strategy sops: - scenario-driver-identification - competitive-move-prediction - timeline-projection - scenario-impact-assessment - robustness-scoring - scenario-synthesis tactics: - strategy-robustness-testing dependencies: sops: - competitive-move-prediction - robustness-scoring - scenario-driver-identification - scenario-impact-assessment - scenario-synthesis - timeline-projection tactics: - strategy-robustness-testing
Competitive Intelligence Scenario Planning. Predict competitor progress, publication timelines, and methodological breakthroughs that could affect our research positioning. Assess time windows of opportunity and first-mover advantages.
Key principles:
1. **Identify competitive drivers** → spawn `scenario-driver-identification`
2. **Predict competitor moves** → spawn `competitive-move-prediction` (×3-5 competitors)
3. **Project timelines** → spawn `timeline-projection`
4. **Assess impact** → spawn `scenario-impact-assessment` (per competitive scenario)
5. **Score robustness** → spawn `robustness-scoring`
6. **Synthesize** → spawn `scenario-synthesis`
| Step | Token Budget | Notes | |------|-------------|-------| | Driver identification | 8K | Competitor-focused | | Move prediction | 10K × N | N = 3-5 key competitors | | Timeline projection | 12K | Multi-horizon | | Impact assessment | 10K × N | Per competitive scenario | | Robustness scoring | 8K | Competitive positioning | | Synthesis | 12K | Strategy recommendations |
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Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | strategy-robustness-testing | Orchestrates impact assessment and robustness scoring to evaluate research approach resilience across scenarios |
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | competitive-move-prediction | Predict competitor progress, publications, and strategic moves | | robustness-scoring | Compute robustness index across scenarios with sensitivity analysis | | scenario-driver-identification | Identify key uncertainty drivers using PESTEL framework scanning | | scenario-impact-assessment | Assess each scenario's impact on the research approach across multiple dimensions | | scenario-synthesis | Comprehensive scenario analysis report synthesizing all scenario work | | timeline-projection | Extrapolate research landscape timelines using trend analysis and milestone projection |
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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.
Repo: yogsoth-ai/de-anthropocentric-research-engine
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