/research-refine

name: research-refine
description: 'Turn a vague research direction into a problem-anchored, elegant, frontier-aware, implementation-oriented method plan via iterative GPT-6-Astra review. Use when the user says "refine my approach", "帮我细化方案", "decompose this problem", "打磨idea", "refine research plan", "细化研究方案", or wants a concrete research method that stays simple, focused, and top-venue ready instead of a vague or overbuilt idea.'
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, WebSearch, WebFetch, mcp__codex__codex, mcp__codex__codex-reply, mcp__antigravity__antigravity, mcp__antigravity__antigravity-reply, mcp__grok__grok, mcp__grok__grok-reply, mcp__claude-review__review, mcp__claude-review__review_reply, mcp__claude-review__review_start, mcp__claude-review__review_reply_start, mcp__claude-review__review_status

Research Refine: Problem-Anchored, Elegant, Frontier-Aware Plan Refinement

> **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 research problem is already visible but the technical route is still fuzzy. The goal is not to produce a bloated proposal or a benchmark shopping list. The goal is to turn a vague direction into a **problem -> focused method -> minimal validation** document that is concrete enough to implement, elegant enough to feel paper-worthy, and current enough to resonate in the foundation-model era.

Four principles dominate this skill:

1. **Do not lose the original problem.** Freeze an immutable **Problem Anchor** and reuse it in every round. 2. **The smallest adequate mechanism wins.** Prefer the minimal intervention that directly fixes the bottleneck. 3. **One paper, one dominant contribution.** Prefer one sharp thesis plus at most one supporting contribution. 4. **Modern leverage is a prior, not a decoration.** When LLM / VLM / Diffusion / RL / distillation / inference-time scaling naturally fit the bottleneck, use them concretely. Do not bolt them on as buzzwords.

User input (PROBLEM + vague APPROACH)
  -> Phase 0 (Claude): Freeze Problem Anchor
  -> Phase 1 (Claude): Scan grounding papers -> identify technical gap -> choose the sharpest route -> write focused proposal
  -> Phase 2 (Codex/GPT-6-Astra): Review for fidelity, specificity, contribution quality, and frontier leverage
  -> Phase 3 (Claude): Anchor check + simplicity check -> revise method -> rewrite full proposal
  -> Phase 4 (Codex, same thread): Re-evaluate revised proposal
  -> Repeat Phase 3-4 until OVERALL SCORE >= 9 or MAX_ROUNDS reached
  -> Phase 5: Save full history to refine-logs/
  -> Optional handoff: /experiment-plan for a detailed execution-ready experiment roadmap

Constants

• **REVIEWER_MODEL = `gpt-6-astra`** — Reviewer model used via Codex MCP.
• **MAX_ROUNDS = 5** — Maximum review-revise rounds.
• **SCORE_THRESHOLD = 9** — Minimum overall score to stop.
• **OUTPUT_DIR = `refine-logs/`** — Directory for round files and final report.
• **MAX_LOCAL_PAPERS = 15** — Maximum local papers/notes to scan for grounding.
• **MAX_CORE_EXPERIMENTS = 3** — Default cap for core validation blocks inside this skill.
• **MAX_PRIMARY_CLAIMS = 2** — Soft cap for paper-level claims. Prefer one dominant claim plus one supporting claim.
• **MAX_NEW_TRAINABLE_COMPONENTS = 2** — Soft cap for genuinely new trainable pieces. Exceed only if the paper breaks otherwise.

> Override via argument if needed, e.g. `/research-refine "problem | approach" -- max rounds: 3, threshold: 9`.

State Persistence (Checkpoint Recovery)

Long-running refinement sessions may fail mid-way (e.g., API timeout, context compaction, or session interruption). To avoid losing completed work, persist state to `refine-logs/REFINE_STATE.json` after each phase boundary:

{
  "phase": "review",
  "round": 1,
  "threadId": "019cd392-...",
  "last_score": 6.5,
  "last_verdict": "REVISE",
  "status": "in_progress",
  "timestamp": "2026-03-22T20:00:00"
}

**Field definitions:**

| Field | Values | Meaning | |-------|--------|---------| | `phase` | `"anchor"` / `"proposal"` / `"review"` / `"refine"` / `"done"` | Last **completed** phase | | `round` | 0–MAX_ROUNDS | Current round number | | `threadId` | string or null | Reviewer thread ID for `codex-reply` continuity | | `last_score` | number or null | Most recent overall score from reviewer | | `last_verdict` | string or null | Most recent verdict (READY / REVISE / RETHINK) | | `status` | `"in_progress"` / `"completed"` | Loop status | | `timestamp` | ISO 8601 | When state was last written |

**Write rules:**

• **Write after each phase completes** (not before). Overwrite each time — only the latest state matters.
• **On completion** (Phase 5 finished), set `"status": "completed"`.

Output Structure

refine-logs/
├── REFINE_STATE.json
├── round-0-initial-proposal.md
├── round-1-review.md
├── round-1-refinement.md
├── round-2-review.md
├── round-2-refinement.md
├── ...
├── REVIEW_SUMMARY.md
├── FINAL_PROPOSAL.md
├── REFINEMENT_REPORT.md
└── score-history.md

Every `round-N-refinement.md` must contain a **full anchored proposal**, not just incremental fixes.

Workflow

Initialization (Checkpoint Recovery)

Before starting any phase, check whether a previous run left a checkpoint:

1. *