# research-pipeline

Runs an end-to-end research workflow from hypothesis generation and experiments to writing a paper PDF. Best for fully automated academic cycles in Claude Code.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 555 installs · ★ 18090
- Author: wanshuiyin
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/HEAD/skills/research-pipeline/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/research-pipeline

## What it does

The skill automates the scientific pipeline across literature exploration, hypothesis discovery, experimental code runs, and manuscript drafting. When theoretical obligations require verification, it hands off theorem proofs to `/lean-formalize`. Experimental code is reviewed by GPT-6-Astra xhigh before execution to prevent wasted compute hours.

Execution relies on tools like Bash, Grep, WebSearch, and the Codex MCP server. Setting `HUMAN_CHECKPOINT` to true pauses after each review round, while `RESUMABLE` writes per-stage snapshots to `.aris/runs/<run_id>.json`. Interrupted jobs can resume directly from their saved identifier. Without an explicit `VENUE` target, paper generation stays deferred after the narrative report.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill research-pipeline -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/research-pipeline/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/research-pipeline/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill research-pipeline -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/research-pipeline/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill research-pipeline -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/research-pipeline/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install wanshuiyin/Auto-claude-code-research-in-sleep research-pipeline
```

2. **Manually: put the files in place**
- File: `.github/skills/research-pipeline/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill research-pipeline -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep --path skills/research-pipeline
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/research-pipeline/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill research-pipeline -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/HEAD/skills/research-pipeline/SKILL.md

## When to use it
- Fetch arXiv metadata to survey related literature and find ideas
- Review experimental code prior to GPU execution
- Resume interrupted research runs using a saved run identifier
- Generate a narrative report and render it into HTML

## Example prompts
- /research-pipeline "Graph neural networks for drug discovery"
- /research-pipeline "Transformer attention bounds" — resume run_2025_03
- /research-pipeline "Diffusion models" — HUMAN_CHECKPOINT: true, AUTO_WRITE: false

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: fef8bb0e1217418d.

BEGIN THIRD-PARTY FILE fef8bb0e1217418d
```text
---
name: research-pipeline
description: "Full end-to-end research pipeline: from a broad research direction through idea discovery, experiments, and review all the way to a polished paper PDF. Use when user says \"全流程\", \"full pipeline\", \"从找idea到投稿\", \"end-to-end research\", or wants the complete autonomous research lifecycle."
argument-hint: "[research-direction] [— resume <run_id>]"
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, WebSearch, WebFetch, Skill, mcp__codex__codex, mcp__codex__codex-reply
---

# Full Research Pipeline: Idea → Experiments → Submission

> **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.


> ⏱ **External cadence: non-judgmental heartbeat only.** An overnight `/loop` /
> `CronCreate` heartbeat may wake, detect a **stalled** phase (no progress, dead
> process, blocked on a freed resource) and **nudge** it forward — it may NEVER
> decide the work is good (paper good enough, proof holds, claim supported).
> Every such verdict stays on its own skill's internal cadence and terminates in
> the cross-model jury. A heartbeat may say "keep going," never "good enough."
> See
> [`shared-references/external-cadence.md`](../shared-references/external-cadence.md)
> (overnight-pipeline rule + stall detection & forced structural pivot). At heartbeat
> startup, touch the run state first each tick and register this run with the watchdog
> `loop` type (so a silent death surfaces as STALE); unregister on completion. The
> watchdog only detects — it never acquits. Each tick also record the new-finding count
> via the `iteration_log.py` helper (resolve through the canonical
> `.aris/tools → tools → $ARIS_REPO/tools → $ARIS_REPO/tools via ~/.aris/repo`
> chain, integration-contract §2; warn-and-skip if unresolved):
> `python3 "$ITER_LOG" note <root> <run_id> <phase> <n>`. On the returned
> `pivot=structural` (stale ≥ 2) the nudge must change a STRUCTURAL constraint and pick an
> untried direction; on `pivot=human` (stale ≥ 4) flag for attention. Counting only —
```
END THIRD-PARTY FILE fef8bb0e1217418d

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