skills/54-scdenney-open-science-skills/skills/diverge/SKILL.md
Before implementing, generate 3-5 conceptually distinct approaches labeled by creativity dimension (Novel, Surprising, Diverse, Conventional), then hold for selection. Brainstorm-then-select to resist defaulting to the most obvious solution.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research divergeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Interrupt the default path of jumping to the most probable — and least creative — solution.
This is an original Open Science Skills workflow grounded in Creative Preference Optimization (Ismayilzada et al., 2025; background in reference/creative-preference-optimization.md). Standard preference alignment (RLHF/DPO) optimizes for the most human-expected output, which is by construction the least surprising one. The paper's most accessible remedy — its own "brainstorm-then-select" baseline — needs no fine-tuning: force divergence before convergence by generating several conceptually distinct approaches, requiring that at least one is surprising and one is novel, and deferring quality and implementation until after selection.
Use diverge for creative, architectural, or analytical work where more than one non-obvious solution exists. To delegate the brainstorm to a second model family, use the sibling diverge-codex.
Use /diverge <task> when:
Do not use for rote tasks with one correct answer (e.g., fix this syntax error).
Given $ARGUMENTS:
If the task is ambiguous about what "good" looks like, ask one focused question before proceeding. Skip this if the goal is clear. Do not ask about implementation details.
Produce 3–5 approaches that are genuinely conceptually distinct. Differences must be in underlying mechanism, not surface vocabulary.
Label each with its primary creativity dimension:
For each approach provide:
Do not implement. Present all approaches, then ask:
"Which approach should I pursue? Or should I synthesize elements from multiple?"
Implement the selected approach directly. If the user asks to synthesize, identify which elements are mechanically compatible and propose a brief hybrid plan before implementing.
tools
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
tools
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".
tools
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
tools
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".