# Mathmodel Pipeline

> 数模竞赛全流程流水线：赛题分析 → 建模方案 → 代码求解 → 自动优化 → 论文撰写。当用户说'全流程'、'full pipeline'、'从赛题到论文'、'一键建模'时使用。

- Skill: `best6668/mathmodel-pipeline` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add best6668/mathmodel-pipeline`
- Raw SKILL.md: https://api.skillmd.com/api/skills/best6668/mathmodel-pipeline/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: best6668 (https://skillmd.com/u/best6668)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/best6668/mathmodel-pipeline

---


# Full Research Pipeline: Idea → Experiments → Submission

End-to-end autonomous research workflow for: **$ARGUMENTS**

## Constants

- **AUTO_PROCEED = true** — When `true`, Gate 1 auto-selects the top-ranked idea (highest pilot signal + novelty confirmed) and continues to implementation. When `false`, always waits for explicit user confirmation before proceeding.
- **ARXIV_DOWNLOAD = false** — When `true`, `/problem-analysis` downloads the top relevant 数模论文库 PDFs during literature survey. When `false` (default), only fetches metadata via 数模论文库 API. Passed through to `/modeling-discovery` → `/problem-analysis`.
- **HUMAN_CHECKPOINT = false** — When `true`, the auto-review loops (Stage 4) pause after each round's review to let you see the score and provide custom modification instructions before fixes are implemented. When `false` (default), loops run fully autonomously. Passed through to `/auto-optimize-loop`.

> 💡 Override via argument, e.g., `/mathmodel-pipeline "topic" — AUTO_PROCEED: false, human checkpoint: true`.

## Overview

This skill chains the entire research lifecycle into a single pipeline:

```
/modeling-discovery → implement → /run-solver → /auto-optimize-loop → 可提交
├── Workflow 1 ──┤            ├────────── Workflow 2 ──────────────┤
```

It orchestrates two major workflows plus the implementation bridge between them.

## Pipeline

### Stage 1: Idea Discovery (Workflow 1)

If `PROBLEM_BRIEF.md` exists in the project root, it will be automatically loaded as detailed context (replaces one-line prompt). See `templates/RESEARCH_BRIEF_TEMPLATE.md`.

Invoke the idea discovery pipeline:

```
/modeling-discovery "$ARGUMENTS"
```

This internally runs: `/problem-analysis` → `/model-creator` → `/feasibility-check` → `/model-review`

**Output:** `MODEL_REPORT.md` with ranked, validated, pilot-tested ideas.

**🚦 Gate 1 — Human Checkpoint:**

After `MODEL_REPORT.md` is generated, **pause and present the top ideas to the user**:

```
📋 Idea Discovery complete. Top ideas:

1. [Idea 1 title] — Pilot: POSITIVE (+X%), Novelty: CONFIRMED
2. [Idea 2 title] — Pilot: WEAK POSITIVE (+Y%), Novelty: CONFIRMED
3. [Idea 3 title] — Pilot: NEGATIVE, eliminated

Recommended: Idea 1. Shall I proceed with implementation?
```

**If AUTO_PROCEED=false:** Wait for user confirmation before continuing. The user may:
- **Approve an idea** → proceed to Stage 2.
- **Pick a different idea** → proceed with their choice.
- **Request changes** (e.g., "combine Idea 1 and 3", "focus more on X") → update the idea prompt with user feedback, re-run `/modeling-discovery` with refined constraints, and present again.
- **Reject all ideas** → collect feedback on what's missing, re-run Stage 1 with adjusted research direction. Repeat until the user commits to an idea.
- **Stop here** → save current state to `MODEL_REPORT.md` for future reference.

**If AUTO_PROCEED=true:** Present the top ideas, wait 10 seconds for user input. If no response, auto-select the #1 ranked idea (highest pilot signal + novelty confirmed) and proceed to Stage 2. Log: `"AUTO_PROCEED: selected Idea 1 — [title]"`.

> ⚠️ **This gate waits for user confirmation when AUTO_PROCEED=false.** When `true`, it auto-selects the top idea after presenting results. The rest of the pipeline (Stages 2-4) is expensive (GPU time + multiple review rounds), so set `AUTO_PROCEED=false` if you want to manually choose which idea to pursue.

### Stage 2: Implementation

Once the user confirms which idea to pursue:

1. **Read the idea details** from `MODEL_REPORT.md` (hypothesis, experimental design, pilot code)

2. **Implement the full experiment**:
   - Extend pilot code to full scale (multi-seed, full dataset, proper baselines)
   - Add proper evaluation metrics and logging (wandb if configured)
   - Write clean, reproducible experiment scripts
   - Follow existing codebase conventions

3. **Code review**: Before deploying, do a self-review:
   - Are all hyperparameters configurable via argparse?
   - Is the random seed fixed and controllable?
   - Are results saved to JSON/CSV for later analysis?
   - Is there proper logging for debugging?

### Stage 3: Deploy Experiments (Workflow 2 — Part 1)

Deploy the full-scale experiments:

```
/run-solver [experiment command]
```

**What this does:**
- Check GPU availability on configured servers
- Sync code to remote server
- Launch experiments in screen sessions with proper CUDA_VISIBLE_DEVICES
- Verify experiments started successfully

**Monitor progress:**

```
/monitor-solver [server]
```

Wait for experiments to complete. Collect results.

### Stage 4: Auto Review Loop (Workflow 2 — Part 2)

Once initial results are in, start the autonomous improvement loop:

```
/auto-optimize-loop "$ARGUMENTS — [chosen idea title]"
```

**What this does (up to 4 rounds):**
1. GPT-5.4 xhigh reviews the work (score, weaknesses, minimum fixes)
2. Claude Code implements fixes (code changes, new experiments, reframing)
3. Deploy fixes, collect new results
4. Re-review → repeat until score ≥ 6/10 or 4 rounds reached

**Output:** `AUTO_REVIEW.md` with full review history and final assessment.

### Stage 5: Final Summary

After the auto-review loop completes, write a final status report:

```markdown
# Research Pipeline Report

**Direction**: $ARGUMENTS
**Chosen Idea**: [title]
**Date**: [start] → [end]
**Pipeline**: modeling-discovery → implement → run-solver → auto-optimize-loop

## Journey Summary
- Ideas generated: X → filtered to Y → piloted Z → chose 1
- Implementation: [brief description of what was built]
- Experiments: [number of GPU experiments, total compute time]
- Review rounds: N/4, final score: X/10

## Final Status
- [ ] Ready for submission / [ ] Needs manual follow-up

## Remaining TODOs (if any)
- [items flagged by reviewer that weren't addressed]

## Files Changed
- [list of key files created/modified]
```

## 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.

- **Human checkpoint after Stage 1 is controlled by AUTO_PROCEED.** When `false`, do not proceed without user confirmation. When `true`, auto-select the top idea after presenting results.
- **Stages 2-4 can run autonomously** once the user confirms the idea. This is the "sleep and wake up to results" part.
- **If Stage 4 ends at round 4 without positive assessment**, stop and report remaining issues. Do not loop forever.
- **Budget awareness**: Track total compute-minutes across the pipeline. Flag if approaching user-defined limits.
- **Documentation**: Every stage updates its own output file. The full history should be self-contained.
- **Fail gracefully**: If any stage fails (no good ideas, experiments crash, review loop stuck), report clearly and suggest alternatives rather than forcing forward.

## Typical Timeline

| Stage | Duration | Can sleep? |
|-------|----------|------------|
| 1. Idea Discovery | 30-60 min | Yes if AUTO_PROCEED=true |
| 2. Implementation | 15-60 min | Yes (autonomous after Gate 1) |
| 3. Deploy | 5 min + experiment time | Yes ✅ |
| 4. Auto Review | 1-4 hours (depends on experiments) | Yes ✅ |

**Sweet spot**: Run Stage 1-2 in the evening, launch Stage 3-4 before bed, wake up to a reviewed paper.