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
Show mcp-stata identity, connected tools, and status. Use when the user asks if mcp-stata is available, asks about access to the toolkit, or asks what Stata tools are connected.
tools
Activate when users mention Stata commands, .do files, regressions, econometrics, stored results, graphs, dataset inspection, replication, or Stata errors. Route the task through mcp-stata tools and the specialized research skills instead of treating it as plain text coding.
development
Build and review paper-ready regression, balance, and summary tables from Stata outputs. Use when the user needs a clean table for a draft, appendix, or coauthor share-out.
tools
Install, configure, update, or verify mcp-stata across Claude Code, Codex, Gemini CLI, Cursor, Windsurf, and VS Code. Activate when users ask to set up the Stata toolkit or troubleshoot the installation.