research-refine-pipeline

Original：🇺🇸 English

Translated

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.

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Sourcewanshuiyin/auto-claude-code-research-in-sleep

Added on2026-03-19

NPX Install

npx skill4agent add wanshuiyin/auto-claude-code-research-in-sleep research-refine-pipeline

SKILL.md Content

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Research Refine Pipeline: End-to-End Method and Experiment Planning

Refine and concretize: $ARGUMENTS

Overview

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:

a problem-anchored, elegant final proposal
the review history explaining why the method is focused
a detailed experiment roadmap tied to the paper's claims
a compact pipeline summary that says what to run next

This skill composes two existing workflows:

```
research-refine
```
for method refinement
```
experiment-plan
```
for claim-driven validation planning

For stage-specific detail, read these sibling skills only when needed:

```
../research-refine/SKILL.md
```
```
../experiment-plan/SKILL.md
```

Core Rule

Do not plan a large experiment suite on top of an unstable method. First stabilize the thesis. Then turn the stable thesis into experiments.

Default Outputs

```
refine-logs/FINAL_PROPOSAL.md
```
```
refine-logs/REVIEW_SUMMARY.md
```
```
refine-logs/REFINEMENT_REPORT.md
```
```
refine-logs/EXPERIMENT_PLAN.md
```
```
refine-logs/EXPERIMENT_TRACKER.md
```
```
refine-logs/PIPELINE_SUMMARY.md
```

Workflow

Phase 0: Triage the Starting Point

Extract the problem, rough approach, constraints, resources, and target venue.
Check whether
```
refine-logs/FINAL_PROPOSAL.md
```
already exists and still matches the current request.
If the proposal is missing, stale, or materially different from the current request, run the full
```
research-refine
```
stage.
If the proposal is already strong and aligned, reuse it and jump to experiment planning.
If in doubt, prefer re-running
```
research-refine
```
rather than planning experiments for the wrong method.

Phase 1: Method Refinement Stage

Run the

research-refine

workflow and keep its V3 philosophy intact:

preserve the Problem Anchor
prefer the smallest adequate mechanism
keep one dominant contribution
modernize only when it improves the paper

Exit this stage only when these are explicit:

the final method thesis
the dominant contribution
the complexity intentionally rejected
the key claims and must-run ablations
the remaining risks, if any

If the verdict is still

REVISE

, continue into experiment planning only if the remaining weaknesses are clearly documented.

Phase 2: Planning Gate

Before the experiment stage, write a short gate check:

What is the final method thesis?
What is the dominant contribution?
What complexity was intentionally rejected?
Which reviewer concerns still matter for validation?
Is a frontier primitive central, optional, or absent?

If these answers are not crisp, tighten the final proposal first.

Phase 3: Experiment Planning Stage

Run the

experiment-plan

workflow grounded in:

```
refine-logs/FINAL_PROPOSAL.md
```
```
refine-logs/REVIEW_SUMMARY.md
```
```
refine-logs/REFINEMENT_REPORT.md
```

Ensure the experiment plan covers:

the main anchor result
novelty isolation
a simplicity or deletion check
a frontier necessity check if applicable
run order, budget, and decision gates

Phase 4: Integration Summary

Write

refine-logs/PIPELINE_SUMMARY.md

:

markdown

# 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`

Phase 5: Present a Brief Summary to the User

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

Key Rules

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.

Composing with Other Skills

/research-refine-pipeline -> one-shot method + experiment planning
/research-refine   -> method refinement only
/experiment-plan   -> experiment planning only
/run-experiment    -> execution