buaazhanghaonan/adversarial-research-copilot/research-refine-pipeline

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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 both the method and experiment plan together.

Install

npx vskill@latest install buaazhanghaonan/adversarial-research-copilot/research-refine-pipeline

SKILL.md

The same SKILL.md was published earlier as loujc/auto-claude-code-research-in-sleep-manual/research-refine-pipeline.

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

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

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

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