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
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文(用于「文献综述工具选型」与「一键安装/运行」)。
development
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
documentation
Use when the project collects primary data or runs a field, lab, or survey experiment, before the intervention begins — write the pre-analysis plan, size the sample from a power calculation, and register with the AEA RCT Registry. Apply after the design is chosen in aer-identification and before any outcome data are seen.
tools
Guide economists to authoritative data sources with explicit, confirmed data specifications before retrieval; interfaces with Playwright MCP to navigate portals and extract real data, not articles about data.