skills/42-wanshuiyin-ARIS/skills/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
development
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
development
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
testing
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
data-ai
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.