Experiment BridgeCAUTION

ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: experiment-bridge
description: "Workflow 1.5: Bridge between idea discovery and auto review. Reads EXPERIMENT_PLAN.md, implements experiment code, deploys to GPU, collects initial results. Use when user says \"实现实验\", \"implement experiments\", \"bridge\", \"从计划到跑实验\", \"deploy the plan\", or has an experiment plan ready to execute."
argument-hint: "[experiment-plan-path-or-topic]"
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, Skill, 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
---

# Workflow 1.5: Experiment Bridge

> **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.


Implement and deploy experiments from plan: **$ARGUMENTS**

## Overview

This skill bridges Workflow 1 (idea discovery + method refinement) and Workflow 2 (auto review loop). It takes the experiment plan and turns it into running experiments with initial results.

```
Workflow 1 output:                    This skill:                                    Workflow 2 input:
refine-logs/EXPERIMENT_PLAN.md   →   implement → GPT-6-Astra review → deploy → collect → initial results ready
refine-logs/EXPERIMENT_TRACKER.md     code        (cross-model)    /run-experiment     for /auto-review-loop
refine-logs/FINAL_PROPOSAL.md
```

## Constants

- **CODE_REVIEW = true** — GPT-6-Astra xhigh reviews experiment code before deployment. Catches logic bugs before wasting GPU hours. Set `false` to skip.
- **AUTO_DEPLOY = true** — Automatically deploy experiments after implementation + review. Set `false` to manually inspect code before deploying.
- **SANITY_FIRST = true** — Run the sanity-stage experiment first (smallest, fastest) before launching the rest. Catches setup bugs early.
- **MAX_PARALLEL_RUNS = 4** — Maximum number of experiments to deploy in parallel (limited by available GPUs).
- **BASE_REPO = false** — GitHub repo URL to use as base codebase. When set, clone the repo first and implement experiments on top of it. When `false` (default), write code from scratch or reuse existing project files.
- **COMPACT = false** — When `true`, (1) read `idea-stage/IDEA_CANDIDATES.md` instead of full `idea-stage/IDEA_REPORT.md` if available, (2) append experiment results to `EXPERIMENT_LOG.md` after collection.

> Override: `/experiment-bridge "EXPERIMENT_PLAN.md" — compact: true, base repo: https://github.com/org/project`

## Inputs

This skill expects one or more of:

1. **`refine-logs/EXPERIMENT_PLAN.md`** (best) — claim-driven experiment roadmap from `/experiment-plan`
2. **`refine-logs/EXPERIMENT_TRACKER.md`** — run-by-run execution table
3. **`refine-logs/FINAL_PROPOSAL.md`** — method description for implementation context
4. **`idea-stage/IDEA_CANDIDATES.md`** — compact idea summary (preferred when `COMPACT: true`) *(fall back to `./IDEA_CANDIDATES.md` if not found)*
5. **`idea-stage/IDEA_REPORT.md`** — full brainstorm output *(fall back to `./IDEA_REPORT.md` if not found)*

If none exist, ask the user what experiments to implement.

## Workflow

### Phase 1: Parse the Experiment Plan

Read `EXPERIMENT_PLAN.md` and extract:

1. **Run order and milestones** — which experiments run first (sanity → baseline → main → ablation → polish)
2. **For each experiment block:**
   - Dataset / split / task
   - Compared systems and variants
   - Metrics to compute
   - Setup details (backbone, hyperparameters, seeds)
   - Success criterion
   - Priority (MUST-RUN vs NICE-TO-HAVE)
3. **Compute budget** — total estimated GPU-hours
4. **Method details** from `FINAL_PROPOSAL.md` — what exactly to implement

Present a brief summary:

```
📋 Experiment plan loaded:
- Milestones: [N] (sanity → baseline → main → ablation)
- Must-run experiments: [N]
- Nice-to-have: [N]
- Estimated GPU-hours: [X]

Proceeding to implementation.
```

**Research-contract fallback**: if `idea-stage/docs/research_contract.md` does
not exist yet (idea selected outside `/idea-discovery`, or an older run),
create it now from `templates/RESEARCH_CONTRACT_TEMPLATE.md` using the selected
idea + claims from the experiment plan. Downstream `/result-to-claim` and
`/ablation-planner` read this file as the claims source, and session recovery
(`docs/SESSION_RECOVERY_GUIDE.md`) depends on it existing.

### Phase 2: Implement Experiment Code

**If `BASE_REPO` is set** — clone the repo first:
```bash
git clone <BASE_REPO> base_repo/
# Read the repo's README, understand its structure, find entry points
# Implement experiments by modifying/extending this codebase
```

For each milestone (in order), write the experiment scripts:

1. **Check existing code** — scan the project (or cloned `base_repo/`) for existing experiment scripts, model code, data loaders. Reuse as much as possible.

2. **Implement missing pieces:**
   - Training scripts with proper argparse (all hyperparameters configurable)
   - Evaluation scripts computing the specified metrics
   - Data loading / preprocessing if needed
   - Baseline implementations if not already present
   - Fixed random seeds for reproducibility
   - Results saved to JSON/CSV for later analysis
   - 

CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (1)

If using vast.ai instances, destroy them after all experiments complete and results are downloaded. Running instances cost money every second

Gates applied: no_behavioural_pass.

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