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research-refine-pipeline

Turn a vague research direction into a focused proposal and a detailed experiment roadmap. This workflow connects method refinement with validation planning, producing review records, an experiment tracker, and a summary of what to run next.

中文简介正在准备中。当前显示英语简介。

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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 "串起来", build a pipeline, do it end-to-end, or generate both the method and experiment plan together.

实测评分 未实测

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适用场景

  • Turn a rough research idea into a focused proposal
  • Plan experiments that test the paper's claims
  • Organize experiment order, budget, and decision points
  • Review a proposal and its experiment plan together

包含的文件(1)

  • SKILL.md6.9 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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

Before each reviewer slot, follow Selectable Reviewers: resolve invocation → .aris/reviewer.json → Claude Code default (Codex MCP) with reviewer_route.py through the canonical helper resolver, preserving this slot's tier, fresh contexts, source/image inputs and deterministic gates. Codex examples/pins below apply only to backend=codex; other routes use their own first-call/reply contract. Save the route and actual returned identity; unavailable selections are REVIEW_UNAVAILABLE. Classify acceptance from actual families/completion/scope. Forward invocation reviewer/model/effort overrides to reviewer-bearing children. Generation workers are unchanged.

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:

# 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:

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

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