Predict plausible competitor research moves, timing, and preemption/priority risk with explicit assumptions.
Scanned 9/24/2026
npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill predict-competitive-move --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: predict-competitive-move
description: "Predict plausible competitor research moves, timing, and preemption/priority risk with explicit assumptions."
---
# predict-competitive-move
## Purpose
Predict plausible competitor research moves, timing, and preemption/priority risk with explicit assumptions.
## Input contract
```yaml
required: [competitor_evidence, research_path, time_horizon]
optional: [evidence, assumptions, prior_results]
constraints: [use named scientific objects; retain provenance and missingness; $\alpha$ = 0.05 and power = 0.8 where applicable]
```
## Procedure
1. Validate the typed inputs and state the decision this operation must support.
2. Apply the declared operation to the named object; record intermediate values that affect interpretation.
3. Check boundary conditions and counterexamples, then emit the result with uncertainty and source links.
## Output contract
```yaml
produces: [predict_competitive_move_result, evidence_trace, uncertainties]
delta_fields: [evidence_updates, uncertainties]
```
## Quality gates
- Inputs are named scientific objects with compatible schemas.
- Every material result has a derivation or source reference.
- Fixed statistical criteria remain exact where applicable: $\alpha$ 0.05 and power 0.8.
## Failure and counterexamples
Return a failed operation with the violated precondition when inputs are incomplete, assumptions are unsupported, or a counterexample defeats the result.
## Provenance map
- intermediate: experiment-execution/competitive-move-prediction
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