skills/42-wanshuiyin-ARIS/skills/skills-codex/research-refine-pipeline/SKILL.md
Run an end-to-end workflow that chains `research-refine` and `experiment-plan`. Use when the user wants a one-shot pipeline from vague research direction to focused final proposal plus detailed experiment roadmap, or asks to "串起来", build a pipeline, do it end-to-end, or generate both the method and experiment plan together.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research research-refine-pipelineInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Refine and concretize: $ARGUMENTS
Use this skill when the user does not want to stop at a refined method. The goal is to produce a coherent package that includes:
This skill composes two existing workflows:
research-refine for method refinementexperiment-plan for claim-driven validation planningFor stage-specific detail, read these sibling skills only when needed:
../research-refine/SKILL.md../experiment-plan/SKILL.mdDo not plan a large experiment suite on top of an unstable method. First stabilize the thesis. Then turn the stable thesis into experiments.
refine-logs/FINAL_PROPOSAL.mdrefine-logs/REVIEW_SUMMARY.mdrefine-logs/REFINEMENT_REPORT.mdrefine-logs/EXPERIMENT_PLAN.mdrefine-logs/EXPERIMENT_TRACKER.mdrefine-logs/PIPELINE_SUMMARY.mdrefine-logs/FINAL_PROPOSAL.md already exists and still matches the current request.research-refine stage.research-refine rather than planning experiments for the wrong method.Run the research-refine workflow and keep its V3 philosophy intact:
Exit this stage only when these are explicit:
If the verdict is still REVISE, continue into experiment planning only if the remaining weaknesses are clearly documented.
Before the experiment stage, write a short gate check:
If these answers are not crisp, tighten the final proposal first.
Run the experiment-plan workflow grounded in:
refine-logs/FINAL_PROPOSAL.mdrefine-logs/REVIEW_SUMMARY.mdrefine-logs/REFINEMENT_REPORT.mdEnsure the experiment plan covers:
Write refine-logs/PIPELINE_SUMMARY.md:
# Pipeline Summary
**Problem**: [problem]
**Final Method Thesis**: [one sentence]
**Final Verdict**: [READY / REVISE / RETHINK]
**Date**: [today]
## Final Deliverables
- Proposal: `refine-logs/FINAL_PROPOSAL.md`
- Review summary: `refine-logs/REVIEW_SUMMARY.md`
- Experiment plan: `refine-logs/EXPERIMENT_PLAN.md`
- Experiment tracker: `refine-logs/EXPERIMENT_TRACKER.md`
## Contribution Snapshot
- Dominant contribution:
- Optional supporting contribution:
- Explicitly rejected complexity:
## Must-Prove Claims
- [Claim 1]
- [Claim 2]
## First Runs to Launch
1. [Run]
2. [Run]
3. [Run]
## Main Risks
- [Risk]:
- [Mitigation]:
## Next Action
- Proceed to `/run-experiment`
Pipeline complete.
Method output:
- refine-logs/FINAL_PROPOSAL.md
Experiment output:
- refine-logs/EXPERIMENT_PLAN.md
- refine-logs/EXPERIMENT_TRACKER.md
Pipeline summary:
- refine-logs/PIPELINE_SUMMARY.md
Best next step:
- /run-experiment
Large file handling: If the Write tool fails due to file size, immediately retry using Bash (cat << 'EOF' > file) to write in chunks. Do NOT ask the user for permission — just do it silently.
Do not let the experiment plan override the Problem Anchor.
Do not widen the paper story after method refinement unless a missing validation block is truly necessary.
Reuse the same claims across FINAL_PROPOSAL.md, EXPERIMENT_PLAN.md, and PIPELINE_SUMMARY.md.
Keep the main paper story compact.
If the method is intentionally simple, defend that simplicity in the experiment plan rather than adding new components.
If the method uses a modern LLM / VLM / Diffusion / RL primitive, make its necessity test explicit.
If the method does not need a frontier primitive, say that clearly and avoid forcing one.
Prefer the staged skills when the user only needs one stage; use this skill for the integrated flow.
/research-refine-pipeline -> one-shot method + experiment planning
/research-refine -> method refinement only
/experiment-plan -> experiment planning only
/run-experiment -> execution
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.