/aris-experiment-bridge

name: aris-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, Agent, Skill, mcp__codex__codex, mcp__codex__codex-reply
license: MIT
metadata:
  author: wanshuiyin/ARIS
  version: "1.0.0"

Workflow 1.5: Experiment Bridge

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-5.4 review → deploy → collect → initial results ready
refine-logs/EXPERIMENT_TRACKER.md     code        (cross-model)    /aris-run-experiment     for /aris-auto-review-loop
refine-logs/FINAL_PROPOSAL.md

Constants

• **CODE_REVIEW = true** — GPT-5.4 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_CANDIDATES.md` instead of full `IDEA_REPORT.md` if available, (2) append experiment results to `EXPERIMENT_LOG.md` after collection.

> Override: `/aris-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 `/aris-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_CANDIDATES.md`** — compact idea summary (preferred when `COMPACT: true`) 5. **`IDEA_REPORT.md`** — full brainstorm output (fallback)

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

Workflow

Phase 0: Compute Resource Guard (MANDATORY)

**Before parsing the experiment plan or writing any code**, verify that compute resources are available by running `/aris-compute-guard`.

If `/aris-compute-guard` is not available as a sub-skill, perform the check inline:

1. Read `CLAUDE.md` to determine the target environment (`gpu: local`, `remote`, `vast`, or `modal`) 2. Check GPU availability:

• **Local (Linux):** `nvidia-smi --query-gpu=index,memory.used,memory.total --format=csv,noheader` — free if `memory.used < 500 MiB`
• **Local (Mac):** `python3 -c "import torch; print(torch.backends.mps.is_available())"`
• **Remote:** `ssh <server> nvidia-smi --query-gpu=index,memory.used,memory.total --format=csv,noheader`
• **Modal:** `modal token verify` — always available if authenticated

**If compute is NOT available → STOP the entire bridge workflow.**

⚠️ COMPUTE RESOURCES UNAVAILABLE — Experiment Bridge Halted

Cannot proceed with implementing and deploying experiments because compute
resources are not available.

[reason and suggestions]

I will NOT proceed with code implementation or experiment deployment, as there
is no compute target to run the experiments on. Please resolve the compute
issue and re-run /aris-experiment-bridge.

**Do NOT implement experiment code if you cannot deploy it.** Writing code without a deployment target wastes time and may lead to hallucinated results during the collection phase.

**If compute IS available → proceed to Phase 1.**

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.

Phase 2: Implement Experiment Code

**If `BASE_REPO` is set** — clone the repo first:

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 analys