Experiment Bridge

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-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)

1. Compute budget — total estimated GPU-hours
2. 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:

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.

1. 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
• Proper logging (wandb if configured in CLAUDE.md)

1. Follow the plan's run order — implement sanity-stage experiments first, then baselines, then main method, then ablations.

1. Self-review before deploying:

• Are all hyperparameters from EXPERIMENT_PLAN.md reflected in argparse?
• Is the random seed fixed and controllable?
• Are results saved in a parseable format (JSON/CSV)?
• Does the code match FINAL_PROPOSAL.md's method description?

Phase 2.5: Cross-Model Code Review (when CODE_REVIEW = true)

Skip this step if `CODE_REVIEW` is `false`.

Before deploying, send the experiment code to GPT-6-Astra xhigh for review:

mcp__codex__codex:
  model: gpt-6-astra
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    Review the following experiment implementation for correctness.

    ## Experiment Plan:
    [paste key sections from EXPERIMENT_PLAN.md]

    ## Method Description:
    [paste from FINAL_PROPOSAL.md]

    ## Implementation:
    [paste the experiment scripts]

    Check for:
    1. Does the code correctly implement the method described in the proposal?
    2. Are all hyperparameters from the plan reflected in the code?
    3. Are there any logic bugs (wrong loss function, incorrect data split, missing eval)?
    4. Is the evaluation metric computed correctly?
    5. **CRITICAL: Does evaluation use the dataset's actual ground truth labels — NOT another model's output as ground truth?** This is a common and severe bug.
    6. Any potential issues (OOM risk, numerical instability, missing seeds)?

    For each issue found, specify: CRITICAL / MAJOR / MINOR and the exact fix.

    === SCOPE LIMITS (these bound what you PROPOSE, never what you look for) ===
    Report anything that is actually wrong here — including a rare-looking case, if
    this repo actually produces it. Then keep the fix in scope:
    1. This is a RESEARCH-WORKFLOW tool, not a security paper. Verification is
       welcome; over-defense is not. Assume a cooperating operator on their own
       machine — a malicious local user is NOT in the threat model.
    2. Do NOT propose SHA / hash / content-fingerprint / digest-binding schemes.
       Reporting a real defect in hashing code that already exists is fine.
    3. NO speculative machinery: do not add feature flags, migration frameworks,
       compat layers, wrappers, pins, or similar mechanisms unless evidence shows
       a current repo defect they fix or an explicit existing invariant they must
       preserve. "Load-bearing", "compatibility", and "not scaffolding" are labels,
       not evidence. Point to the failing path/artifact or invariant, and check the
       proposal's factual premises, such as whether a named package version exists.
    4. NO corner-case obsession: exotic encodings, symlink races, RTL text and
       millisecond races are out of scope unless you can show the case arises here.
    5. Where a rubric or checklist is genuinely needed, do not over-mechanize
       judgement. A clear sentence a human reads beats a scored table nobody
       maintains.
    Exception: code that runs remote commands, starts a network service, or installs
    an MCP server runs on the user's machine with their credentials — trust-boundary
    findings there are in scope and the default is strict.
    Say plainly when something is correct. Do not manufacture findings.

On review results:

• No CRITICAL issues → proceed to Phase 3