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Prompts Research Refinement and Experiment Planning Pipeline

agent research skill risk: low

Research Refinement and Experiment Planning Pipeline

Runs an end-to-end workflow chaining research-refine and experiment-plan stages to convert a vague research direction into a final proposal, review history, and claim-driven experi…

SKILL 1 file

SKILL.md
---
name: auto-claude-code-research-in-sleep-research-refine-pipeline
description: "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 /\"/u4e32/u8d77/u6765/\", build a pipeline, do it end-to-end, or generate bot"
---
# 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:

1. `research-refine` for method refinement
2. `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
```

## Output Protocols

> Follow these shared protocols for all output files:
> - **[Output Versioning Protocol](../../shared-references/output-versioning.md)** — write timestamped file first, then copy to fixed name
> - **[Output Manifest Protocol](../../shared-references/output-manifest.md)** — log every output to MANIFEST.md
> - **[Output Language Protocol](../../shared-references/output-language.md)** — respect the project's language setting

## 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
```

INPUTS

$ARGUMENTS REQUIRED

user-supplied research direction or request to refine

REQUIRED CONTEXT

  • $ARGUMENTS (vague research direction)
  • existing refine-logs/FINAL_PROPOSAL.md if present

OPTIONAL CONTEXT

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

ROLES & RULES

  1. Do not plan a large experiment suite on top of an unstable method. First stabilize the thesis. Then turn the stable thesis into experiments.
  2. Do not let the experiment plan override the Problem Anchor.
  3. Do not widen the paper story after method refinement unless a missing validation block is truly necessary.
  4. Reuse the same claims across FINAL_PROPOSAL.md, EXPERIMENT_PLAN.md, and PIPELINE_SUMMARY.md.
  5. Keep the main paper story compact.
  6. If the method is intentionally simple, defend that simplicity in the experiment plan rather than adding new components.
  7. If the method uses a modern LLM / VLM / Diffusion / RL primitive, make its necessity test explicit.
  8. If the method does not need a frontier primitive, say that clearly and avoid forcing one.
  9. Prefer the staged skills when the user only needs one stage; use this skill for the integrated flow.
  10. If the Write tool fails due to file size, immediately retry using Bash to write in chunks. Do NOT ask the user for permission.

EXPECTED OUTPUT

Format
markdown
Schema
markdown_sections · Pipeline Summary, Problem, Final Method Thesis, Final Verdict, Final Deliverables, Contribution Snapshot, Must-Prove Claims, First Runs to Launch, Main Risks, Next Action, Pipeline complete
Constraints
  • write timestamped file first then copy to fixed name
  • log every output to MANIFEST.md
  • respect the project's language setting
  • produce exactly the six named refine-logs files
  • follow the exact PIPELINE_SUMMARY.md and final user summary templates

SUCCESS CRITERIA

  • Produce a coherent package with final proposal, review history, experiment roadmap, and pipeline summary
  • Stabilize the thesis before planning experiments
  • Exit refinement only when final method thesis, dominant contribution, rejected complexity, key claims and risks are explicit
  • Write crisp gate check answers before experiment stage
  • Ensure experiment plan covers main anchor result, novelty isolation, simplicity check, frontier necessity check, run order, budget and gates

FAILURE MODES

  • Planning experiments for a stale or misaligned proposal
  • Continuing to experiment planning when verdict is still REVISE without documented weaknesses
  • Letting experiment plan override the Problem Anchor
  • Widening the paper story unnecessarily after refinement

CAVEATS

Dependencies
  • ../research-refine/SKILL.md
  • ../experiment-plan/SKILL.md
  • ../../shared-references/output-versioning.md
  • ../../shared-references/output-manifest.md
  • ../../shared-references/output-language.md
  • refine-logs/FINAL_PROPOSAL.md
  • refine-logs/REVIEW_SUMMARY.md
  • refine-logs/REFINEMENT_REPORT.md
Missing context
  • Definition or link for 'Problem Anchor'
  • Explicit success criteria for exiting Phase 1 with 'REVISE' verdict
Ambiguities
  • V3 philosophy is referenced but not defined in the prompt
  • Garbled text "/"/u4e32/u8d77/u6765/" in the description field
  • Does not specify what constitutes a 'strong and aligned' proposal for reuse vs. re-run decision

QUALITY

OVERALL
0.82
CLARITY
0.75
SPECIFICITY
0.85
REUSABILITY
0.80
COMPLETENESS
0.90

IMPROVEMENT SUGGESTIONS

  • Replace the garbled Chinese characters in the name/description with clear English equivalent
  • Add a one-sentence definition or pointer for 'V3 philosophy' and 'Problem Anchor' inside the prompt

USAGE

Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.

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