---
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."
---

# Full Research Pipeline: Idea → Experiments → Submission

> **Before each reviewer slot**, follow [Selectable Reviewers](../shared-references/reviewer-backends.md):
> resolve invocation → `.aris/reviewer.json` → Codex default (fresh native reviewer)
> 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 is fire-control only.** An overnight scheduler may check
> process/file progress, update a heartbeat, and nudge a stalled phase. It must
> never rerun or replace a reviewer verdict. Register the state file with
> `watchdog.py`, unregister on completion, and use `iteration_log.py` to trigger
> structural pivots after repeated no-progress iterations. See
> [`external-cadence.md`](../shared-references/external-cadence.md).

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

## Optional formal proof route

Before creating an experimental run, route theorem-centered requests for Lean
to [`/lean-formalize`](../lean-formalize/SKILL.md) and report its actual proof
result. Run the experimental stages below when the task includes empirical
research. In a mixed project, use Lean for a user-requested formal proof or a
specific mathematical obligation whose formal verification would materially
help; return its checked scope and remaining obligations to the research summary
and applicable proof audit. Ordinary mathematics stays in the existing theory
skills. Difficulty alone does not make Lean mandatory, and a formal sublemma
does not establish an empirical claim.

## Constants

- **AUTO_PROCEED = true** — When `true`, every selection checkpoint is informational: report the choice and continue in the same turn. When `false`, ask for explicit user confirmation and end the turn at the checkpoint.
- **ARXIV_DOWNLOAD = false** — When `true`, `/research-lit` downloads the top relevant arXiv PDFs during literature survey. When `false` (default), only fetches metadata via arXiv API. Passed through to `/idea-discovery` → `/research-lit`.
- **HUMAN_CHECKPOINT = false** — When `true`, the auto-review loops (Stage 3) 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-review-loop`.
- **REVIEWER_DIFFICULTY = medium** — How adversarial the reviewer is. `medium` (default): standard MCP review. `hard`: adds **Reviewer Memory** + **Debate Protocol**. `nightmare`: GPT reads repo directly via `codex exec` + memory + debate. Passed through to `/auto-review-loop`.
- **CODE_REVIEW = true** — GPT-6-Astra xhigh reviews experiment code before deployment. Catches logic bugs before wasting GPU hours. Set `false` to skip. Passed through to `/experiment-bridge`.
- **BASE_REPO = false** — GitHub repo URL to use as base codebase. When set, `/experiment-bridge` clones the repo first and implements experiments on top of it. When `false` (default), writes code from scratch or reuses existing project files. Passed through to `/experiment-bridge`.
- **COMPACT = false** — When `true`, generates compact summary files for short-context models and session recovery. Passed through to `/idea-discovery` and `/experiment-bridge`.
- **AUTO_WRITE = false** — When `true`, automatically invoke Workflow 3 (`/paper-writing`) after Stage 4. `VENUE` is needed only when Stage 5 begins — a missing venue defers paper writing; it never blocks Stages 1-4. When `false` (default), Stage 4 generates `NARRATIVE_REPORT.md` and stops — user invokes `/paper-writing` manually.
- **VENUE = (unset)** — Target venue for paper writing; bound only when Stage 5 begins. Options: `ICLR`, `NeurIPS`, `ICML`, `CVPR`, `ACL`, `AAAI`, `ACM`, `IEEE_CONF`, `IEEE_JOURNAL`. No default: a missing venue defers paper writing — it never blocks Stages 1-4 and is never guessed.
- **RENDER_HTML = true** — When `true` (default), auto-render `NARRATIVE_REPORT.md` to HTML at Stage 4 completion via `/render-html`. Uses `--no-review` because Stage 3 already produced a traced same-family provisional review. Set `false` to skip. Rendering failure is non-blocking.
- **RESUMABLE = true** — Record per-stage state under `.aris/runs/` and resume
  from the first non-terminal phase. Same-family Codex review produces
  `provisional`; deterministic or completed positive known-cross-family review gates produce `accepted`.

> 💡 Override via argument, e.g., `/research-pipeline "topic" — AUTO_PROCEED: false, human checkpoint: true, difficulty: nightmare, code review: false, base repo: https://github.com/org/project, auto_write: true, venue: NeurIPS`.

## Checkpoint execution rule

Resolve `AUTO_PROCEED` once from `$ARGUMENTS` before Stage 1 and pass that
resolved value to nested workflows.

- **`AUTO_PROCEED=true` is non-blocking.** A checkpoint is a progress update,
  not a question. State the result and the automatically selected next action,
  then continue executing in the **same turn**. Do not ask for confirmation,
  request user input, sleep, wait for silence, or end the turn at a checkpoint.
- **`AUTO_PROCEED=false` is blocking.** Present the options, ask the user, and
  end the turn. Resume only after an explicit reply.

Never implement auto-proceed as “ask, then continue if there is no response.”
Once a turn ends, silence cannot resume the pipeline. The user can still
interrupt a non-blocking run at any time.

This rule governs only `AUTO_PROCEED`-controlled selection checkpoints. If the
user explicitly enables a Feishu **interactive** gate, that external approval
or reply is an intentional blocking exception; wait for that user-controlled
gate rather than treating it as a silence timeout. Feishu off/push-only modes
remain non-blocking under `AUTO_PROCEED=true`.

## Overview

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

```
/idea-discovery → /experiment-bridge → /auto-review-loop → /paper-writing (optional)
├── Workflow 1 ──┤├── Workflow 1.5 ──┤├── Workflow 2 ───┤ ├── Workflow 3 ──┤
```

It orchestrates up to four major workflows in sequence. Workflow 3 (paper writing) is optional and controlled by `AUTO_WRITE`.

## Resumable runs and heartbeat

When `RESUMABLE=true`, resolve helpers through the Codex manifest:

```bash
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills-codex.txt ]; then
  ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills-codex.txt 2>/dev/null) || true
fi
RUN_STATE=""
ITER_LOG=""
WATCHDOG=""
[ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/tools/run_state.py" ] && RUN_STATE="$ARIS_REPO/tools/run_state.py"
[ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/tools/iteration_log.py" ] && ITER_LOG="$ARIS_REPO/tools/iteration_log.py"
[ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/tools/watchdog.py" ] && WATCHDOG="$ARIS_REPO/tools/watchdog.py"
[ -z "$RUN_STATE" ] && [ -f tools/run_state.py ] && RUN_STATE="tools/run_state.py"
[ -z "$ITER_LOG" ] && [ -f tools/iteration_log.py ] && ITER_LOG="tools/iteration_log.py"
[ -z "$WATCHDOG" ] && [ -f tools/watchdog.py ] && WATCHDOG="tools/watchdog.py"
```

Warn-and-skip state tracking if `RUN_STATE` cannot be resolved; never pretend it
was persisted. Phases are `idea-discovery,experiment-bridge,auto-review-loop,summary,paper-writing`.

- New run: `python3 "$RUN_STATE" start . "$RUN_ID" --executor "<actual-executor-model>" --provisional-advances --phases "idea-discovery,experiment-bridge,auto-review-loop,summary,paper-writing"` (the `--provisional-advances` policy is what lets a same-family provisional verdict close a phase for resume — without it, mainline semantics apply and provisional phases stay open).
- Resume: `python3 "$RUN_STATE" resume . "$RUN_ID"`; restart the returned phase.
- Each phase: mark `running`, then `done --artifact <path>`.
- A completed positive same-family review uses `mark-provisional --reviewer
  <actual-reviewer-model> --verdict-id <trace-or-session-id>`. This is terminal
  for resume but not accepted; use the actual executor identity from this run.
- A completed positive known-cross-family review or deterministic verifier uses `accept`.
- If `AUTO_WRITE=false`, mark `paper-writing` as `skipped` after summary.

| Phase | Terminal record |
|---|---|
| idea-discovery | base Codex → provisional; completed positive known-cross-family jury → accepted |
| experiment-bridge | deterministic job/result completion → accepted |
| auto-review-loop | base Codex positive STOP → provisional; completed positive known-cross-family review → accepted |
| summary | deterministic file/render result → accepted |
| paper-writing | verifier report; `overall_assurance=provisional` stays provisional |

For an unattended loop, touch the run state at the start of every tick, register
it once with `watchdog.py --register` as type `loop`, and unregister on
completion. After each tick run `iteration_log.py note <root> <run_id> <phase>
<new-finding-count>`: `pivot=structural` requires a genuinely different approach;
`pivot=human` surfaces the stall. Neither result is a quality verdict. See
[`resumable-runs.md`](../shared-references/resumable-runs.md).

## Pipeline

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

If `RESEARCH_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:

```
/idea-discovery "$ARGUMENTS" — AUTO_PROCEED: $AUTO_PROCEED
```

This internally runs: `/research-lit` → `/idea-creator` → `/novelty-check` → `/research-review`

**Output:** `idea-stage/IDEA_REPORT.md` with ranked, validated, pilot-tested ideas.

**Review Tracing** follows the downstream review skills. Stage 1 and Stage 3 preserve reviewer prompts/responses through their own trace protocols so the final handoff can be audited.

**🚦 Gate 1 — Idea Selection:**

After `idea-stage/IDEA_REPORT.md` is generated, present the top ideas.

**If `AUTO_PROCEED=true` (non-blocking):** report the selection and continue
immediately in the same turn. Do not phrase the update as a question:

```
📋 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

AUTO_PROCEED: selected Idea 1 — [title]. Continuing to Stage 2.
```

**If `AUTO_PROCEED=false` (blocking):** present the same ranking, ask
`Recommended: Idea 1. Shall I proceed with implementation?`, then end the turn.
The user may:
- **Approve the idea** → proceed to Stage 2. `/experiment-bridge` reads `refine-logs/EXPERIMENT_PLAN.md` already generated by `/idea-discovery`.
- **Request changes** (e.g., "combine Idea 1 and 3", "focus more on X") → update the idea prompt with user feedback, re-run `/idea-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 `idea-stage/IDEA_REPORT.md` for future reference.

> ⚠️ **This gate waits for user confirmation when AUTO_PROCEED=false.** When `true`, it auto-proceeds after presenting results. The rest of the pipeline (Stages 2-3) is expensive (GPU time + multiple review rounds), so set `AUTO_PROCEED=false` if you want a final review checkpoint before committing GPU resources.

### Stage 2: Experiment Bridge (Workflow 1.5)

Once the idea is selected (automatically or by the user), delegate implementation and deployment to `/experiment-bridge`:

```
/experiment-bridge "$CHOSEN_IDEA_TITLE" — code review: $CODE_REVIEW, base repo: $BASE_REPO, compact: $COMPACT
```

> 💡 **Queue routing is automatic**: `/experiment-bridge` Phase 4 routes each milestone by job count — ≤5 jobs → `/run-experiment`, ≥10 jobs or teacher→student phase dependencies → `/experiment-queue` (with OOM retry, wave gating, crash-safe state). No manual override is needed.

**What this does (fully autonomous):**
1. Parses `refine-logs/EXPERIMENT_PLAN.md` — extracts milestones, run order, compute budget
2. Implements experiment code — extends pilot to full scale, follows existing codebase conventions
3. **Fresh-agent code review** — GPT-6-Astra xhigh reviews the implementation in a new context; base result is same-family provisional
4. **Sanity check** — runs the smallest experiment first to verify the environment; auto-debugs failures (up to 3 attempts, with `/codex:rescue` fallback)
5. Deploys full experiments — auto-routes by job count (≤5 → `/run-experiment`, ≥10 → `/experiment-queue` with OOM retry, wave gating, crash-safe state)
6. Collects initial results — parses outputs, updates `refine-logs/EXPERIMENT_TRACKER.md`, runs `/training-check` if W&B is configured
7. Auto-plans ablations via `/ablation-planner` if main results are positive

**Output:**
- `refine-logs/EXPERIMENT_RESULTS.md` — structured results by milestone
- `refine-logs/EXPERIMENT_TRACKER.md` — updated run-by-run status
- `EXPERIMENT_LOG.md` (when `COMPACT=true`) — session-recovery-friendly log

**Monitor progress** (while experiments run):

```
/monitor-experiment [server]
```

Wait for `/experiment-bridge` to complete and report its handoff summary before proceeding.

### Stage 3: Auto Review Loop (Workflow 2)

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

```
/auto-review-loop "$ARGUMENTS — [chosen idea title], difficulty: $REVIEWER_DIFFICULTY"
```

**What this does (up to 4 rounds):**
1. GPT-6-Astra 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:** `review-stage/AUTO_REVIEW.md` with full review history and final assessment.

### Stage 4: Research Summary & Writing Handoff

After the auto-review loop completes, prepare the handoff for paper writing.

**Step 1:** Write a final research status report (same as before).

**Step 2:** Generate `NARRATIVE_REPORT.md` from:
- `IDEA_REPORT.md` (chosen idea, hypothesis, novelty justification)
- Implementation details from the repo
- Experiment configs and final results
- `AUTO_REVIEW.md` (review history, weaknesses fixed, remaining limitations)

The narrative report must contain:
- Problem statement and core claim
- Method summary
- Key quantitative results with evidence for each claim
- Figure/table inventory (which exist, which need manual creation)
- Limitations and remaining follow-up items

**Output:** `NARRATIVE_REPORT.md` + research pipeline report.

```markdown
# Research Pipeline Report

**Direction**: $ARGUMENTS
**Chosen Idea**: [title]
**Date**: [start] → [end]
**Pipeline**: idea-discovery → experiment-bridge → auto-review-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

## Writing Handoff
- NARRATIVE_REPORT.md: ✅ generated
- Venue: [VENUE or "not set — run /paper-writing manually"]
- Manual figures needed: [list or "none"]

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

### Stage 5 / Stage 6: Paper Writing (Workflow 3 — Optional)

This is the **Stage 6: Paper Writing** handoff in the broader research lifecycle; it is numbered Stage 5 here because this consolidated pipeline counts the writing handoff after the Stage 4 narrative report.

**Skip this stage if `AUTO_WRITE=false` (default).** Present the `/paper-writing` command for manual use:

```
📝 Research complete. To write the paper:
/paper-writing "NARRATIVE_REPORT.md" — venue: <VENUE>, AUTO_PROCEED: $AUTO_PROCEED
```

**If `AUTO_WRITE=true`:**

🚦 **Gate 2 — Writing Checkpoint:**

```
📝 Research pipeline complete. Ready for Workflow 3.

- Venue: [VENUE]
- Input: NARRATIVE_REPORT.md
- Manual figures required: [list or none]
- Next step: /paper-writing "NARRATIVE_REPORT.md" — venue: [VENUE], AUTO_PROCEED: $AUTO_PROCEED

Proceeding with paper writing...
```

Checks before proceeding (venue binds HERE — Stages 1-4 are venue-independent):
- If `VENUE` is missing: with `AUTO_PROCEED=false`, ask now. With
  `AUTO_PROCEED=true`, do not guess and do not wait — stamp
  "VENUE NOT SPECIFIED — paper writing deferred" in the report and checkpoint,
  leave the paper-writing phase pending, and finish the run cleanly for a later
  resume. Never silently pick a venue.
- If manual figures are required: with `AUTO_PROCEED=false`, pause and list
  them. With `AUTO_PROCEED=true`, record "paper writing deferred (manual
  figures: <list>)" and finish cleanly the same way.

Then invoke:

```
/paper-writing "NARRATIVE_REPORT.md" — venue: $VENUE, AUTO_PROCEED: $AUTO_PROCEED
```

Pass the resolved `AUTO_PROCEED` explicitly so Workflow 3 cannot silently
fall back to its own default mode.

This delegates to Workflow 3 which handles its own phases:
`/paper-plan → /paper-figure → /paper-write → /paper-compile → /auto-paper-improvement-loop`

When Workflow 3 finishes, update the pipeline report with:
- Paper writing completion status
- Final PDF path (`paper/main.pdf`)
- Improvement scores (round 0 → round N)
- Remaining issues

**Output:** `paper/` directory with LaTeX source, compiled PDF, and `PAPER_IMPROVEMENT_LOG.md`.

## Render HTML view (auto, when `RENDER_HTML = true`)

After Stage 4 finalizes `NARRATIVE_REPORT.md` (before paper writing branches), invoke `/render-html` on the narrative report:

```
/render-html "NARRATIVE_REPORT.md" --no-review
```

`--no-review` is intentional: this is an internal handoff doc, not reviewer-facing — the claims already received a traced same-family provisional review in Stage 3. Output: `NARRATIVE_REPORT.html` next to the MD, with embedded source SHA256.

**Non-blocking**: if `/render-html` fails (helper missing, file write error, etc.), log the failure and continue Stage 4 — the HTML view is a convenience artifact, not a pipeline prerequisite.

Skip this step if `RENDER_HTML = false`.

## 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 user for permission — just do it silently.

- **The Stage 1 checkpoint is controlled by AUTO_PROCEED.** When `false`, do not proceed without user confirmation. When `true`, report the top selection and continue in the same turn without asking or waiting.
- **Stages 2-3 can run autonomously** once the idea is selected. This is the "sleep and wake up to results" part.
- **If Stage 3 ends at round 4 without positive assessment**, stop and report remaining issues. Do not loop forever.
- **Budget awareness**: Track total GPU-hours 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. Experiment Bridge | 30-120 min (implement + review + deploy + collect) | Yes ✅ |
| 3. Auto Review | 1-4 hours (depends on experiments) | Yes ✅ |

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