/research-refine-pipeline

name: 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 "串起来", build a pipeline, do it end-to-end, or generate both the method and experiment plan together.'
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, WebSearch, WebFetch, mcp__codex__codex, mcp__codex__codex-reply

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

> **Before each reviewer slot**, follow [Selectable Reviewers](../shared-references/reviewer-backends.md): > 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: > - **[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 us