/auto-experiment

Launch an autonomous experiment agent that runs your deep learning experiments 24/7.

What This Does

This skill starts a THINK → EXECUTE → REFLECT loop that:

1. Reads your PROJECT_BRIEF.md to understand the research goal
2. Analyzes previous results in MEMORY_LOG.md
3. Plans the next experiment (hypothesis + success criteria)
4. Implements code changes and runs a mandatory dry-run
5. Launches GPU training via nohup (tracks PID)
6. Monitors at zero LLM cost (only kill -0 PID + tail log + nvidia-smi)
7. Wakes up when training finishes to analyze results
8. Updates memory and decides: iterate, pivot, or report
9. Repeats

Usage

/auto-experiment
/auto-experiment --project /path/to/my_project --gpu 0
/auto-experiment --project . --max-cycles 5

Prerequisites

The project directory must contain:

PROJECT_BRIEF.md (required)

A frozen reference describing your research goal. Example:

# Goal
Train a ViT-B/16 on ImageNet to reach 78%+ top-1 accuracy.

# Codebase
- Training: train.py
- Config: configs/vit_base.yaml
- Data: /data/imagenet/

# Constraints
- GPU 0-3 available (use DDP)
- Max 90 epochs per run
- Report val accuracy after each run

# Current Best
- ResNet-50 baseline: 76.1%

config.yaml (optional)

Override default agent settings:

agent:
  model: "claude-sonnet-4-6"
  max_cycles: -1          # -1 = unlimited
  max_steps_per_cycle: 3  # max sub-agent dispatches per cycle
  cooldown_interval: 300  # 5 min smart polling

memory:
  brief_max_chars: 3000
  log_max_chars: 2000

monitor:
  poll_interval: 900      # check every 15 min during training
  zero_llm: true

experiment:
  mandatory_dry_run: true

Workflow Details

Phase 1: THINK

• Read PROJECT_BRIEF.md (frozen, max 3000 chars)
• Read MEMORY_LOG.md (rolling, auto-compacted)
• Check for HUMAN_DIRECTIVE.md (highest priority, auto-archived after reading)
• Analyze: What's the current best? What hasn't been tried? What's most promising?
• Output: experiment plan with hypothesis and success criteria

Phase 2: EXECUTE

• Dispatch to Code Agent (5 tools: run_shell, launch_experiment, write_file, read_file, list_files)
• Code Agent implements changes
• Mandatory dry-run (2-step verify, abort if fails)
• Launch training via nohup, capture PID
• Enter zero-cost monitoring loop:
  • kill -0 $PID — is process alive?
  • nvidia-smi — GPU utilization
  • tail -50 logfile — latest training output
  • Zero LLM API calls during this phase

Phase 3: REFLECT

• Parse training logs for metrics (loss, accuracy, FGD, FID, etc.)
• Compare against previous best
• Log milestone if improved (auto-compacted at 1200 chars)
• Log decision (rolling last 15 entries)
• Decide: try another config / pivot direction / generate report

Human Override (anytime)

# Drop a directive file — agent reads it next cycle with highest priority
echo "Try learning rate 1e-5 with cosine schedule" > workspace/HUMAN_DIRECTIVE.md

Memory System

Two-Tier, constant size (~5K chars / ~1500 tokens), no matter how long the agent runs:

Auto-compaction rules:

• Key Results: oldest dropped when section > 1,200 chars
• Recent Decisions: only last 15 entries kept
• Total log hard-capped at 2,000 chars

Cost

Example Output

After a few cycles, your workspace/MEMORY_LOG.md will look like:

# Memory Log

## Key Results
[04-07 14:30] Exp001: ResNet-50 baseline, lr=0.1, acc=76.1%
[04-07 22:15] Exp002: ViT-B/16, lr=1e-3, acc=74.8% (underperforming, lr too high)
[04-08 06:00] Exp003: ViT-B/16, lr=3e-4 + cosine, acc=77.9% (new best!)
[04-08 14:45] Exp004: ViT-B/16, lr=3e-4 + cosine + mixup, acc=78.3% (target reached!)

## Recent Decisions
[04-07 14:30] Start with ResNet-50 baseline to establish reference
[04-07 22:15] ViT lr=1e-3 too high, try 3e-4 next
[04-08 06:00] Cosine schedule helped significantly, try adding regularization
[04-08 14:45] Target reached! Generate final report.