auto-deep-researcher-24x7

What this skill does

```markdown
---
name: auto-deep-researcher-24x7
description: Autonomous AI agent that runs deep learning experiments 24/7 using a Leader-Worker architecture with zero-cost monitoring and constant-size memory.
triggers:
  - "run my deep learning experiments automatically"
  - "set up autonomous experiment agent"
  - "automate hyperparameter tuning overnight"
  - "run experiments while I sleep"
  - "set up 24/7 ML experiment loop"
  - "autonomous deep learning research agent"
  - "auto experiment with GPU monitoring"
  - "continuous experiment iteration with LLM"
---

# Auto Deep Researcher 24x7

> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.

An autonomous AI agent that runs your deep learning experiments 24/7. It follows a **THINK → EXECUTE → REFLECT** loop: plans the next experiment, launches training on GPU, monitors at zero LLM cost (just process checks + log reads), then reflects and iterates — all without human intervention.

**Key properties:**
- Zero-cost monitoring during training (no LLM calls while GPU trains)
- Constant-size memory via rolling `MEMORY_LOG.md`
- Leader-Worker architecture for multi-GPU / multi-project
- ~$0.08/day average LLM cost
- Human stays in control via `PROJECT_BRIEF.md` and `HUMAN_DIRECTIVE.md`

---

## Installation

### Prerequisites

- Python 3.10+
- 1+ NVIDIA GPU
- Anthropic or OpenAI API key
- Claude Code, Codex CLI, or similar AI coding agent

### Clone and Install

```bash
git clone https://github.com/Xiangyue-Zhang/auto-deep-researcher-24x7.git
cd auto-deep-researcher-24x7
pip install -r requirements.txt
```

### Set API Key

```bash
# For Anthropic (Claude)
export ANTHROPIC_API_KEY=your_key_here

# For OpenAI (Codex)
export OPENAI_API_KEY=your_key_here
```

---

## Quickstart (3 Steps)

### Step 1 — Create your project folder

```bash
mkdir my-experiment
cd my-experiment
```

### Step 2 — Write `PROJECT_BRIEF.md`

This is the only required file. It defines the goal, constraints, and search strategy.

```markdown
# Goal
Train a ResNet-50 on CIFAR-100 to reach 80%+ top-1 accuracy.

# Codebase
Create the training code from scratch in PyTorch.

# What to Try
- Start with a basic ResNet-50 baseline (lr=0.1, SGD, cosine schedule).
- If accuracy < 75%, improve optimizer and learning rate schedule.
- If accuracy is 75-80%, try data augmentation (CutMix, AutoAugment).
- If accuracy > 80%, stop and write final report.

# Constraints
- Use GPU 0 only.
- Max 100 epochs per run.
- Do not change the backbone architecture.
```

### Step 3 — Launch the agent

```bash
# Inside Claude Code or Codex CLI:
/auto-experiment --project /path/to/my-experiment --gpu 0
```

The `workspace/` directory is auto-created. The agent starts its loop immediately.

---

## Project Structure

```
my-experiment/
├── PROJECT_BRIEF.md          # ← You write this (required)
├── HUMAN_DIRECTIVE.md        # ← Optional: override next cycle direction
├── config.yaml               # ← Optional: override defaults
└── workspace/                # ← Auto-created by agent
    ├── MEMORY_LOG.md         # Rolling memory of results and decisions
    ├── experiments/          # Per-run code, configs, logs
    ├── progress_tracking/    # Local text notes (if no Obsidian)
    │   ├── Dashboard.txt
    │   └── Daily/YYYY-MM-DD.txt
    └── results/              # Parsed results per experiment
```

---

## Key CLI Commands

```bash
# Start autonomous experiment loop
/auto-experiment --project /path/to/project --gpu 0

# Check current status (goal, best result, cycle count, running jobs)
/experiment-status

# Generate structured progress report
/progress-report

# Manually sync Obsidian notes (if configured)
/obsidian-sync

# Run a single THINK-EXECUTE-REFLECT cycle (manual trigger)
/run-cycle --project /path/to/project
```

---

## Configuration (`config.yaml`)

All fields are optional. The agent runs with defaults if this file is absent.

```yaml
# config.yaml — place in your project root

agent:
  model: "claude-opus-4-5"          # or "gpt-4o", "claude-sonnet-4-5"
  max_cycles: 50                    # stop after N cycles (0 = unlimited)
  cycle_timeout_hours: 6            # max hours per experiment before abort
  reflect_on_failure: true          # still call REFLECT if training crashes

gpu:
  devices: [0]                      # list of GPU IDs to use
  memory_fraction: 0.9              # reserved VRAM fraction per job

monitoring:
  poll_interval_seconds: 60         # how often to check process + logs
  log_tail_lines: 50                # lines from training log to read

memory:
  max_log_entries: 20               # rolling window size in MEMORY_LOG.md
  summarize_on_overflow: true       # summarize old entries instead of delete

obsidian:
  enabled: false                    # set true to sync to Obsidian vault
  vault_path: "~/Documents/MyVault" # path to vault root
  auto_append_daily: true           # append to daily note each cycle

safety:
  dry_run_before_launch: true       # always do a quick dry-run first
  block_path_traversal: true        # harden tool execution
  block_shell_injection: true
```

---

## Controlling the Agent at Runtime

### Stable direction — `PROJECT_BRIEF.md`

Edit this to change the overall goal or constraints. Takes effect at the next THINK phase.

### Temporary redirect — `HUMAN_DIRECTIVE.md`

Create or edit this file to override the next cycle only. The agent reads it, acts on it, then clears it.

```markdown
# HUMAN_DIRECTIVE.md (temporary — agent will clear after reading)

- Stop exploring augmentation for now.
- Return to the last baseline and try a different learning rate schedule.
- Do not change batch size this cycle.
```

### Common directive patterns

```markdown
# Narrow the search space
- Only tune learning rate and weight decay this cycle.
- Do not modify the model architecture.

# Prevent thrashing on a weak branch
- If accuracy gain is below 0.3 points for 3 consecutive runs, abandon this direction.
- Return to the best checkpoint and try a new idea.

# Force result verification
- If a result is more than 2 points above the previous best, rerun with a new seed.
- Do not record improvement until both runs agree within 0.5 points.

# Emergency stop
- Stop all new launches after the current run finishes.
- Write a summary of results so far.
```

---

## Code Examples

### Minimal training script the agent can generate and iterate on

The agent writes and modifies this itself, but knowing the pattern helps you write `PROJECT_BRIEF.md` clearly:

```python
# workspace/experiments/run_003/train.py
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from torchvision.models import resnet50
import argparse, json, time, pathlib

def main(args):
    device = torch.device(f"cuda:{args.gpu}")

    transform_train = transforms.Compose([
        transforms.RandomCrop(32, padding=4),
        transforms.RandomHorizontalFlip(),
        transforms.AutoAugment(transforms.AutoAugmentPolicy.CIFAR10),
        transforms.ToTensor(),
        transforms.Normalize((0.5071, 0.4867, 0.4408),
                             (0.2675, 0.2565, 0.2761)),
    ])
    transform_val = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.5071, 0.4867, 0.4408),
                             (0.2675, 0.2565, 0.2761)),
    ])

    train_ds = torchvision.datasets.CIFAR100("./data", train=True,
                                              download=True, transform=transform_train)
    val_ds   = torchvision.datasets.CIFAR100("./data", train=False,
                                              transform=transform_val)
    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=args.batch_size,
                                               shuffle=True, num_workers=4)
    val_loader   = torch.utils.data.DataLoader(val_ds, batch_size=256,
                                               shuffle=False, num_workers=4)

    model = resnet50(num_classes=100).to(devi