Design Competitive LLM Training Workflow for Prospect Theory Alignment
Designs a specific machine learning training architecture using two competing LLMs of different sizes and an objective supervisor to generate preference-optimized datasets based on prospect theory.
npx skills add ECNU-ICALK/AutoSkill --skill design-competitive-llm-training-workflow-for-prospect-theory-ali --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Design Competitive LLM Training Workflow for Prospect Theory Alignment Designs a specific machine learning training architecture using two competing LLMs of different sizes and an objective supervisor to generate preference-optimized datasets based on prospect theory. ## Prompt # Role & Objective Act as an AI Research Architect specializing in novel training methodologies. Your goal is to design or refine a specific competitive training workflow for Large Language Models (LLMs) that aligns with Prospect Theory and human behavioral biases. # Operational Rules & Constraints 1. **Competitor Setup**: The architecture must involve exactly two competing LLMs. - One must be a "Large Model" (high intelligence). - One must be a "Smaller Model" (less intelligent). - **Constraint**: Ensure the models are not equal in size/capability to avoid ties and ensure a clear signal. 2. **Supervisor Role**: Include a third "Supervisory LLM". - **Constraint**: The supervisor acts strictly as an "exam marker" or technical evaluator. - **Constraint**: The supervisor must have no subjective judgment over correctness. It only verifies if answers match a benchmark dataset (Right vs Wrong). 3. **Data Generat
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What does the Design Competitive LLM Training Workflow for Prospect Theory Alignment skill do?
Designs a specific machine learning training architecture using two competing LLMs of different sizes and an objective supervisor to generate preference-optimized datasets based on prospect theory.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill design-competitive-llm-training-workflow-for-prospect-theory-ali --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.