Research Exploration Sandbox

What This Does

This playbook creates a structured sandbox for experimental work. Instead of scattering test scripts and experiments across your codebase, all exploratory work goes into explorations/ first. You can work fast without full rigor, then graduate successful experiments to production or archive failures with documentation.

Prerequisites

• Claude Code installed and configured
• A project where you do experimental/research work explorations/ ├── [active-project]/ │ ├── README.md # Goal, hypothesis, status │ ├── code/ # Iterations (_v1, v2, v3) │ ├── data/ # Test data, inputs │ └── output/ # Results, generated files └── ARCHIVE/ ├── completed[name]/ # Graduated to production └── abandoned[name]/ # Documented why stopped

## Starting an Exploration

Before coding, create the structure:

mkdir -p explorations/[project-name]/{code,data,output}

Create `explorations/[project-name]/README.md`:
```markdown
# Exploration: [Name]

## Goal
[What are you trying to learn or build?]

## Hypothesis
[What do you expect to happen?]

## Status
[ ] In progress / [x] Completed / [ ] Abandoned

## Notes
[Running notes as you work]

## Outcome
[What did you learn? Did hypothesis hold?]

Fast-Track vs Plan-First

Question	Answer	Workflow
"Will this ship?"	YES	Plan-First (80/100 quality)
"Am I testing an idea?"	YES	Fast-Track (60/100 quality)
"Does this improve the project?"	NO	Don't build it

Fast-Track Rules (for explorations only)

• Skip formal planning
• 60/100 quality threshold (vs 80 for production)
• No PR required
• Document as you go, not after
• Can abandon anytime with brief note

Research Value Check (2 minutes)

Before starting ANY exploration:

1. "Does this improve the paper/product/analysis?"

   • Yes → Explore
   • Maybe → Explore (quick test)
   • No → Skip

2. "Is this the smallest experiment that could validate the idea?"

   • If no, scope down first

Lifecycle

Research value check (2 min)
        ↓
Create explorations/[project]/ (5 min)
        ↓
Code without overhead (60/100 quality)
        ↓
Decision point (1-2 hours):
├── Graduate → Move to src/, upgrade to 80/100
├── Keep exploring → Stay in explorations/
└── Abandon → Move to ARCHIVE/ with explanation

Graduation Process

When exploration succeeds:

1. Document what worked in the README
2. Refactor code to production standards (80/100)
3. Move to appropriate production location
4. Update imports, tests, documentation
5. Move exploration folder to ARCHIVE/completed_[name]/

Abandonment Process

When exploration fails or isn't worth continuing:

1. Document WHY in the README (so future-you doesn't retry)
2. Note any partial learnings
3. Move to ARCHIVE/abandoned_[name]/
4. No shame — failed experiments are valuable data

Kill Switch

At any point, you can stop:

• Archive with one-paragraph explanation
• No sunk cost guilt
• Move on to next idea

## Step-by-Step Setup

### Step 1: Create the explorations structure

```bash
mkdir -p explorations/ARCHIVE

Step 2: Add to your CLAUDE.md

Add the exploration protocol to your project's CLAUDE.md.

Step 3: Start your first exploration

mkdir -p explorations/test-new-api/{code,data,output}

Create the README:

# Exploration: Test New API

## Goal
Evaluate whether the new Stripe API v3 simplifies our checkout flow

## Hypothesis
v3 should reduce checkout code by 30% and eliminate the webhook complexity

## Status
[ ] In progress

## Notes
- Starting exploration 2024-01-15

Example Usage

User:

"I want to experiment with using DuckDB instead of SQLite for analytics queries"

Claude:

Starting exploration for DuckDB evaluation.

Created structure:

explorations/duckdb-analytics/
├── README.md
├── code/
├── data/
└── output/

README.md:

# Exploration: DuckDB for Analytics

## Goal
Evaluate if DuckDB improves analytics query performance over SQLite

## Hypothesis
DuckDB's columnar storage should make aggregate queries 5-10x faster

## Status
[ ] In progress

## Test Plan
1. Import sample data (1M rows)
2. Run benchmark queries in both DBs
3. Compare query times
4. Assess integration complexity

Ready to start coding. Should I begin with the data import script?

Later - Graduation:

"The exploration succeeded. DuckDB is 8x faster. Graduate to production."

Claude:

Graduating DuckDB exploration:

1. Refactoring explorations/duckdb-analytics/code/queries.py to production standards
2. Moving to src/analytics/duckdb_queries.py
3. Adding tests in tests/analytics/
4. Updating README with integration notes
5. Moving exploration to explorations/ARCHIVE/completed_duckdb-analytics/

Done. DuckDB is now production-ready in src/analytics/.

Tips

• Default to explorations/: When unsure if something will ship, start in explorations. Easier to graduate than to extract.
• Version your iterations: Use _v1, _v2, _v3 suffixes. Don't delete old versions until exploration completes.
• Document as you go: "I'll document later" means never. Quick notes now > detailed docs never.
• Abandon freely: Knowing what doesn't work is valuable. Archive with notes and move on.

Troubleshooting

Problem: Explorations never get graduated

Solution: Set time limits. "If not graduated in 2 weeks, either complete or archive."

Problem: explorations/ folder is cluttered

Solution: Archive aggressively. Anything older than a month should be graduated or archived.

Problem: Exploration code quality is too low even for experiments

Solution: Even fast-track code should run. 60/100 means "works but not polished," not "broken."