---

SKILL-SPECIFIC GUIDANCE

When to Use This Skill

• Validating ML baselines before novel methods (Deep Research SOP Pipeline D)
• Reproducing experiments for NeurIPS/ICML challenges
• Verifying SOTA claims before building on prior work
• Creating reproducibility packages for Quality Gate 1
• Debugging performance gaps between published and reproduced results

When NOT to Use This Skill

• Quick proof-of-concept implementations (use prototyping)
• When exact reproducibility not critical (demo projects)
• Papers without code or detailed methodology
• Industry benchmarks without academic rigor

Success Criteria

• Results within +/- 1% of published metrics
• 3+ successful reproductions in fresh Docker
• All 47+ hyperparameters documented
• Reproducibility package tested independently
• Dataset checksums verified (SHA256)
• Random seeds documented, deterministic results

Edge Cases & Limitations

• Missing hyperparameters: GitHub issues, contact authors, supplements
• Framework version mismatches: test PyTorch 1.7 vs 1.13, document differences
• Hardware differences: V100 vs A100 precision, document in notes
• Non-deterministic operations: force torch.use_deterministic_algorithms(True)
• Dataset preprocessing: compare sample outputs, validate checksums

Critical Guardrails

• NEVER claim reproduction without statistical validation (t-test, CI)
• ALWAYS document exact framework versions (pip freeze, conda export)
• NEVER skip random seed validation (test 3+ runs)
• ALWAYS verify dataset integrity (SHA256, sample counts, splits)
• NEVER assume default hyperparameters (extract from paper/code)

Evidence-Based Validation

• Validate determinism: run 3x, verify identical results (variance = 0)
• Cross-validate metrics: recompute from saved outputs
• Test fresh environment: Docker from scratch, no cached deps
• Verify statistical power: calculate required N, check reported N
• Confirm effect size: compute Cohen's d, verify significance

---

name: baseline-replication description: "Replicate published ML baseline experiments with exact reproducibility
\ (\xB11% tolerance) for Deep Research SOP Pipeline D. Use when validating baselines,
\ reproducing experiments, verifying published results, or preparing for novel method
\ development." version: 1.0.0 category: research tags:

• research
• analysis
• planning author: ruv

---

Baseline Replication

Overview

Replicates published machine learning baseline methods with exact reproducibility, ensuring results match within ±1% tolerance. This skill implements Deep Research SOP Pipeline D baseline validation, which is a prerequisite for developing novel methods.

Prerequisites

• Python 3.8+ with PyTorch/TensorFlow
• Docker (for reproducibility)
• Git and Git LFS
• Access to datasets (HuggingFace, academic repositories)

What This Skill Does

1. Extracts methodology from papers and code repositories
2. Validates datasets match baseline specifications exactly
3. Implements baseline with exact hyperparameters
4. Runs experiments with deterministic settings
5. Validates results within ±1% statistical tolerance
6. Creates reproducibility package tested in fresh Docker environment
7. Generates Quality Gate 1 validation checklist

---

Quick Start (30 minutes)

Basic Replication

# 1. Specify baseline to replicate
BASELINE_PAPER="BERT: Pre-training of Deep Bidirectional Transformers (Devlin et al., 2019)"
BASELINE_CODE="https://github.com/google-research/bert"
TARGET_METRIC="Accuracy on SQuAD 2.0"
PUBLISHED_RESULT=0.948

# 2. Run replication workflow
./scripts/replicate-baseline.sh \
  --paper "$BASELINE_PAPER" \
  --code "$BASELINE_CODE" \
  --metric "$TARGET_METRIC" \
  --expected "$PUBLISHED_RESULT"

# 3. Review results
cat output/baseline-bert/replication-report.md

Expected output:

✓ Paper analyzed: Extracted 47 hyperparameters
✓ Dataset validated: SQuAD 2.0 matches baseline
✓ Implementation complete: 12 BERT layers, 110M parameters
✓ Training complete: 3 epochs, 26.3 GPU hours
✓ Results validated: 0.945 vs 0.948 (within ±1% tolerance)
✓ Reproducibility verified: 3/3 fresh reproductions successful
→ Quality Gate 1: APPROVED

---

Step-by-Step Guide

Phase 1: Paper Analysis (15 minutes)

Extract Methodology

# Coordinate with researcher agent
./scripts/analyze-paper.sh --paper "arXiv:2103.00020"

The script extracts:

• Model architecture (layers, hidden sizes, attention heads)
• Training hyperparameters (learning rate, batch size, warmup)
• Optimization details (optimizer type, weight decay, dropout)
• Dataset specifications (splits, preprocessing, tokenization)
• Evaluation metrics (primary and secondary)

Output: baseline-specification.md with all extracted details

Validate Completeness

# Check for missing hyperparameters
./scripts/validate-spec.sh baseline-specification.md

Common Missing Details:

• Learning rate schedule (linear warmup, cosine decay)
• Random seeds (NumPy, PyTorch, Python)
• Hardware specifications (GPU type, memory)
• Framework versions (PyTorch 1.7 vs 1.13 numerical differences)

If details missing:

1. Check paper supplements
2. Check official code config files
3. Check GitHub issues
4. Contact authors

---

Phase 2: Dataset Validation (20 minutes)

Coordinate with data-steward Agent

# Validate dataset matches baseline specs
./scripts/validate-dataset.sh \
  --dataset "SQuAD 2.0" \
  --splits "train:130k,dev:12k" \
  --preprocessing "WordPiece tokenization, max_length=384"

data-steward checks:

• Exact dataset version (v2.0, not v1.1)
• Sample counts match (training: 130,319 examples)
• Data splits match (80/10/10 vs 90/10)
• Preprocessing matches (lower-casing, accent stripping)
• Checksum validation (SHA256 hashes)

Output: dataset-validation-report.md

---

Phase 3: Implementation (2 hours)

Coordinate with coder Agent

# Implement baseline with exact specifications
./scripts/implement-baseline.sh \
  --spec baseline-specification.md \
  --framework pytorch \
  --template resources/templates/bert-base.py

coder creates:

# baseline-bert-implementation.py
import torch
import random
import numpy as np

# CRITICAL: Set all random seeds for reproducibility
torch.manual_seed(42)
torch.cuda.manual_seed_all(42)
np.random.seed(42)
random.seed(42)

# CRITICAL: Enable deterministic mode
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.use_deterministic_algorithms(True)

# Exact hyperparameters from paper
config = {
    "num_layers": 12,
    "hidden_size": 768,
    "num_attention_heads": 12,
    "intermediate_size": 3072,
    "max_position_embeddings": 512,
    "learning_rate": 5e-5,  # From paper section 4.2
    "batch_size": 32,       # Per-GPU batch size
    "num_epochs": 3,
    "warmup_steps": 10000,  # 10% of training steps
    "weight_decay": 0.01,
    "dropout": 0.1
}

Unit Tests:

pytest baseline-bert-implementation_test.py -v

Output: Fully tested implementation matching baseline exactly

---

Phase 4: Experiment Execution (4-8 hours)

Coordinate with tester Agent

# Run experiments with monitoring
./scripts/run-experiments.sh \
  --implementation baseline-bert-implementation.py \
  --config config/bert-squad.yaml \
  --gpus 4 \
  --monitor true

tester executes:

1. Environment Setup:

   # Create deterministic environment
   docker build -t baseline-bert:v1.0 -f Dockerfile .
   docker run --gpus all -v $(pwd):/workspace baseline-bert:v1.0
   

2. Training with Monitoring:

   # Log training curves
   from torch.utils.tensorboard import SummaryWriter
   writer = SummaryWriter('logs/baseline-bert')
   
   for epoch in range(3):
       for batch in dataloader:
           loss = model(batch)
           writer.add_scalar('Loss/train', loss, global_step)
           writer.add_scalar('LR', optimizer.param_groups[0]['lr'], global_step)
   

3. Checkpoint Saving:

   # Save best checkpoint
   torch.save({
       'epoch': epoch,
       'model_state_dict': model.state_dict(),
       'optimizer_state_dict': optimizer.state_dict(),
       'loss': loss,
       'accuracy': accuracy
   }, 'checkpoints/best-model.pt')
   

Output:

• training.log - Complete training logs
• best-model.pt - Best checkpoint
• metrics.json - All evaluation metrics

---

Phase 5: Results Validation (30 minutes)

Statistical Comparison

# Compare reproduced vs published results
./scripts/compare-results.sh \
  --reproduced 0.945 \
  --published 0.948 \
  --tolerance 0.01

Validation checks:

import scipy.stats as stats

# Paired t-test
reproduced = [0.945, 0.946, 0.944]  # 3 runs
published = 0.948

difference = np.mean(reproduced) - published
percent_diff = (difference / published) * 100

# Within tolerance?
within_tolerance = abs(difference / published) <= 0.01

# Statistical significance
t_stat, p_value = stats.ttest_1samp(reproduced, published)
confidence_interval = stats.t.interval(0.95, len(reproduced)-1,
                                       loc=np.mean(reproduced),
                                       scale=stats.sem(reproduced))

print(f"Reproduced: {np.mean(reproduced):.3f} ± {np.std(reproduced):.3f}")
print(f"Published: {published:.3f}")
print(f"Difference: {difference:.3f} ({percent_diff:.2f}%)")
print(f"Within ±1% tolerance: {within_tolerance}")
print(f"95% CI: [{confidence_interval[0]:.3f}, {confidence_interval[1]:.3f}]")

If results differ > 1%:

# Debug systematically
./scripts/debug-divergence.sh \
  --reproduced 0.932 \
  --published 0.948

Common causes:

• Random seed not propagated to all libraries
• Framework version differences (PyTorch 1.7 vs 1.13)
• Hardware differences (V100 vs A100 numerical precision)
• Missing hyperparameter (learning rate schedule)
• Dataset preprocessing mismatch

Output: baseline-bert-comparison.md with statistical analysis

---

Phase 6: Reproducibility Packaging (30 minutes)

Coordinate with archivist Agent

# Create complete reproducibility package
./scripts/create-repro-package.sh \
  --name baseline-bert \
  --code baseline-bert-implementation.py \
  --model best-model.pt \
  --env requirements.txt

archivist creates:

baseline-bert-repro.tar.gz
├── README.md                    # ≤5 steps to reproduce
├── requirements.txt             # Exact versions
├── Dockerfile                   # Exact environment
├── src/
│   ├── baseline-bert-implementation.py
│   ├── data_loader.py
│   └── train.py
├── data/
│   └── download_instructions.txt
├── models/
│   └── best-model.pt
├── logs/
│   └── training.log
├── results/
│   ├── metrics.json
│   └── comparison.csv
└── MANIFEST.txt                 # SHA256 checksums

README.md (≤5 steps):

# BERT SQuAD 2.0 Baseline Reproduction

## Quick Reproduction (3 steps)

1. Build Docker environment:
   ```bash
   docker build -t bert-squad:v1.0 .

2. Download SQuAD 2.0 dataset:

   ./download_data.sh
   

3. Run training:

   docker run --gpus all -v $(pwd):/workspace bert-squad:v1.0 python src/train.py
   

Expected result: 0.945 ± 0.001 accuracy (within ±1% of published 0.948)

#### Test Reproducibility
```bash
# Fresh Docker reproduction
./scripts/test-reproducibility.sh --package baseline-bert-repro.tar.gz --runs 3

Output: 3 successful reproductions with deterministic results

---

Phase 7: Quality Gate 1 Validation (15 minutes)

Coordinate with evaluator Agent

# Validate Quality Gate 1 requirements
./scripts/validate-gate-1.sh --baseline baseline-bert

evaluator checks:

• ✅ Baseline specification complete (47/47 hyperparameters)
• ✅ Dataset validation passed
• ✅ Implementation tested (100% unit test coverage)
• ✅ Results within ±1% tolerance (0.945 vs 0.948)
• ✅ Reproducibility verified (3/3 fresh reproductions)
• ✅ Code documented and archived

Decision Logic:

if results_within_tolerance and reproducibility_verified:
    decision = "APPROVED"
elif minor_gaps_fixable:
    decision = "CONDITIONAL"  # e.g., 1.2% difference but deterministic
else:
    decision = "REJECT"  # e.g., 5% difference, non-deterministic

Output: gate-1-validation-checklist.md

# Quality Gate 1: Baseline Validation

## Status: APPROVED

### Requirements
- [x] Baseline specification document complete
- [x] Dataset validation passed
- [x] Implementation tested and reviewed
- [x] Results within ±1% of published (0.945 vs 0.948)
- [x] Reproducibility package tested in fresh environment
- [x] Documentation complete
- [x] All artifacts archived

### Evidence
- Baseline spec: `baseline-bert-specification.md`
- Dataset validation: `dataset-validation-report.md`
- Implementation: `baseline-bert-implementation.py` (100% test coverage)
- Results comparison: `baseline-bert-comparison.md`
- Reproducibility package: `baseline-bert-repro.tar.gz` (3/3 successful)

### Approval
**Date**: 2025-11-01
**Approved By**: evaluator agent
**Next Step**: Proceed to Pipeline D novel method development

---

Advanced Features

Multi-Baseline Comparison

# Compare multiple baselines simultaneously
./scripts/compare-baselines.sh \
  --baselines "bert-base,roberta-base,electra-base" \
  --dataset "SQuAD 2.0" \
  --metrics "accuracy,f1,em"

Ablation Study Integration

# Once baseline validated, run ablations
./scripts/run-ablations.sh \
  --baseline baseline-bert \
  --ablations "no-warmup,no-weight-decay,smaller-lr"

Continuous Validation

# Set up monitoring for baseline drift
./scripts/setup-monitoring.sh \
  --baseline baseline-bert \
  --schedule "weekly" \
  --alert-threshold 0.02

---

Troubleshooting

Issue: Missing Hyperparameters

Symptoms: Specification validation fails with missing details Cause: Paper doesn't document all hyperparameters Solution:

# Check official code config files
grep -r "learning_rate\|batch_size\|warmup" ${BASELINE_CODE}/

# Check GitHub issues
gh issue list --repo ${BASELINE_REPO} --search "hyperparameter"

# Contact authors
./scripts/contact-authors.sh --paper "arXiv:2103.00020" --question "learning rate schedule"

Issue: Results Diverge > 1%

Symptoms: Reproduced 0.932, published 0.948 (1.7% difference) Solution:

# Systematic debugging
./scripts/debug-divergence.sh --detailed

# Check random seeds
python -c "import torch; print(torch.initial_seed())"

# Check framework version
python -c "import torch; print(torch.__version__)"

# Enable detailed logging
python baseline-bert-implementation.py --debug --log-level DEBUG

Issue: Non-Deterministic Results

Symptoms: 3 runs produce 0.945, 0.951, 0.938 (high variance) Solution:

# Force deterministic mode
import torch
torch.use_deterministic_algorithms(True)
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'

# Check for non-deterministic operations
torch.set_deterministic(True)  # Will error if non-deterministic ops used

Issue: Docker Environment Fails

Symptoms: Docker build or run errors Solution:

# Check Docker resources
docker system df
docker system prune -a  # Free up space if needed

# Use pre-built base image
docker pull pytorch/pytorch:1.13.1-cuda11.6-cudnn8-runtime

# Debug interactively
docker run -it --gpus all pytorch/pytorch:1.13.1 bash

---

Output Files

File	Description	Size
baseline-{method}-specification.md	Extracted methodology	~5KB
dataset-validation-report.md	Dataset validation results	~2KB
baseline-{method}-implementation.py	Clean implementation	~10KB
baseline-{method}-implementation_test.py	Unit tests	~5KB
training.log	Complete training logs	~100MB
best-model.pt	Best checkpoint	~400MB
metrics.json	All evaluation metrics	~1KB
baseline-{method}-comparison.md	Results comparison	~3KB
baseline-{method}-comparison.csv	Metrics table	~1KB
baseline-{method}-repro.tar.gz	Reproducibility package	~450MB
gate-1-validation-checklist.md	Quality Gate 1 evidence	~3KB

---

Integration with Deep Research SOP

Pipeline D: Method Development

Baseline replication is mandatory before novel method development:

Pipeline D Flow:
1. Replicate Baseline (this skill) → Quality Gate 1
2. Develop Novel Method (method-development skill)
3. Ablation Studies (5+ ablations required)
4. Statistical Validation (p < 0.05)
5. Submit for Gate 2 review

Agent Coordination

Sequential workflow:

researcher:
  - Analyze paper
  - Extract methodology
  - Identify data sources
  ↓
data-steward:
  - Validate datasets
  - Check integrity
  - Verify preprocessing
  ↓
coder:
  - Implement baseline
  - Add unit tests
  - Code review
  ↓
tester:
  - Run experiments
  - Monitor training
  - Collect metrics
  ↓
archivist:
  - Create repro package
  - Test fresh reproduction
  - Archive artifacts
  ↓
evaluator:
  - Validate Gate 1
  - Generate checklist
  - Approve/Conditional/Reject

Memory MCP Integration

# Store baseline specification
memory-store --key "sop/pipeline-d/baseline-bert/specification" \
             --value "$(cat baseline-bert-specification.md)" \
             --layer long_term

# Store validation results
memory-store --key "sop/pipeline-d/baseline-bert/gate-1" \
             --value "$(cat gate-1-validation-checklist.md)" \
             --layer long_term

---

Related Skills

• method-development - Develop novel methods after baseline validation
• holistic-evaluation - Run HELM + CheckList evaluations (Pipeline E)
• gate-validation - Quality Gate approval workflow
• reproducibility-audit - Test reproducibility packages
• literature-synthesis - PRISMA systematic reviews

---

Resources

Official Standards

• ACM Artifact Evaluation
• ML Reproducibility Checklist
• TIER Protocol

Tools

• Docker - Reproducible environments
• DVC - Data version control
• Weights & Biases - Experiment tracking
• MLflow - ML lifecycle management

Deep Research SOP Documentation

• Pipeline D specification: docs/deep-research-sop-gap-analysis.md
• Quality Gates overview: CLAUDE.md
• Agent definitions: agents/research/
• Command specifications: .claude/commands/research/

---

Created: 2025-11-01 Version: 1.0.0 Category: Deep Research SOP Pipeline: D (Method Development) Quality Gate: 1 (Baseline Validation) Estimated Time: 8-12 hours (first baseline), 4-6 hours (subsequent)