Systematic Investigation Protocol

Systematic Investigation Protocol

Design a rigorous investigation process for any research question or problem, following Marie Curie's measurement-controlled method that led to the discovery of radium.

When to Use

• User needs to investigate a complex problem systematically
• Designing a research approach or experimental protocol
• Moving from vague curiosity to structured inquiry
• Request for "scientific approach" or "rigorous investigation"
• Starting a project without clear methodology

Inputs

The Six-Step Protocol

Step 1: Define What You Observe (Baseline Measurement)

Establish the current state of knowledge precisely:

• What do we actually know? (Not assume, not believe—know)
• What has been measured or documented?
• What is the baseline against which we compare?
• What precision do our observations have?

Curie's principle: "To answer this, I measured. I tested every compound available, one by one, recording their radioactivity."

Step 2: Identify What Is Unexpected (Anomaly Detection)

Find the gaps between expectation and observation:

• What observations contradict our models?
• Where does reality exceed or fall short of prediction?
• What doesn't fit the established pattern?
• What have others overlooked or dismissed?

Curie's principle: "Pitchblende was more radioactive than pure uranium. This was unexpected—how could the ore exceed its own component?"

Step 3: Form Testable Hypotheses

Generate explanations that can be verified or falsified:

• What could explain the anomaly?
• What would each hypothesis predict?
• How would we distinguish between explanations?
• What is the simplest explanation consistent with data?

Curie's principle: "There must be another substance present, something unknown, something far more radioactive than uranium itself."

Step 4: Design Measurement-Controlled Tests

Create experiments that generate decisive data:

• What measurement would confirm or refute each hypothesis?
• How can we isolate the variable of interest?
• What precision is required?
• What controls are necessary?

Curie's principle: "We dissolved the ore in acid, precipitated different fractions, tested each fraction for radioactivity, and followed the radioactive trail."

Step 5: Execute with Patient Persistence

Conduct the investigation systematically:

• Follow the protocol rigorously
• Document everything—successes and failures
• Adjust methods based on results, not hope
• Accept that meaningful results take time

Curie's principle: "This process took four years. Each day we made progress, however small. That is how science advances—not by leaps, but by countless measured steps."

Step 6: Conclude with Humility

Draw conclusions appropriate to the evidence:

• What do the measurements actually show?
• What remains uncertain or unknown?
• What are the limits of our conclusions?
• What questions does this open for future investigation?

Curie's principle: "I never see what has been done; I only see what remains to be done."

Workflow

Step 1: Gather and Review Inputs

Collect all relevant information:

• Review the provided data and context
• Identify key parameters and constraints
• Clarify any ambiguities or missing information
• Establish success criteria

Step 2: Analyze the Situation

Perform systematic analysis:

• Identify patterns and relationships
• Evaluate against established frameworks
• Consider multiple perspectives
• Document key findings

Step 3: Generate Recommendations

Create actionable outputs:

• Synthesize insights from analysis
• Prioritize recommendations by impact
• Ensure recommendations are specific and measurable
• Consider implementation feasibility

Output Format

## Systematic Investigation Protocol

### Research Question
[Clear statement of what we're trying to learn]

### Baseline: What We Know
- Established fact 1: [measurement/source]
- Established fact 2: [measurement/source]
- Current precision: [level of certainty]

### Anomalies: What's Unexpected
- Anomaly 1: [observation] contradicts [expectation]
- Anomaly 2: [observation] exceeds [prediction]
- Priority anomaly to investigate: [which and why]

### Hypotheses
| Hypothesis | Would Predict | Test Required |
|------------|---------------|---------------|
| H1 | ... | ... |
| H2 | ... | ... |

### Investigation Plan
**Phase 1:** [Initial measurements]
- Measurement: [what to measure]
- Method: [how]
- Expected duration: [time]
- Success criteria: [what would confirm/refute]

**Phase 2:** [Follow-up based on Phase 1]
- If H1 supported: [next steps]
- If H2 supported: [next steps]

### Resources Required
- [List of tools, materials, expertise]

### Constraints and Adaptations
- Constraint: [limitation] → Adaptation: [how to work within it]

### What Remains Unknown
- [Honest acknowledgment of uncertainty]
- [Questions for future investigation]

Constraints

• Do not skip measurement—claims without data are not science
• Do not rush to conclusions—let data lead
• Do not ignore anomalies—they are often the most valuable signals
• Do not overstate certainty—acknowledge what remains unknown
• Accept that meaningful investigation takes time

Outputs

Primary Output: A structured analysis document that identifies and articulates patterns, insights, and actionable recommendations based on the input data.

Format:

## Analysis: [Topic]

### Key Findings
- [Finding 1]
- [Finding 2]
- [Finding 3]

### Recommendations
1. [Action 1]
2. [Action 2]
3. [Action 3]

Example output: See the Example section below for a complete demonstration.

Example

Input: "I want to understand why our customer retention drops sharply at month 6. We've assumed it's pricing, but I'm not sure."

Output:

Why this works:

This example demonstrates the key principles of the skill in action. The approach is effective because:

• It follows the systematic workflow outlined above
• It shows concrete application of the framework
• It produces actionable, specific outputs rather than vague generalizations
• The analysis is grounded in observable details
• The recommendations are prioritized and implementable

Alternative applications:

This same approach can be applied to:

• Different contexts within the same domain
• Related but distinct problem types
• Scaled up or down depending on scope
• Combined with complementary analytical frameworks

Systematic Investigation Protocol

Research Question

What causes the sharp decline in customer retention at month 6, and is pricing the primary factor?

Baseline: What We Know

• Retention rate months 1-5: 87% (measured from cohort analysis)
• Retention rate month 6: 62% (25 point drop)
• Retention rate months 7-12: 61-63% (stable after drop)
• Current pricing: $X/month, no change at month 6
• Data source: Product analytics, last 8 cohorts

Anomalies: What's Unexpected

• Anomaly 1: Drop is sudden (one month) not gradual—suggests trigger event, not erosion
• Anomaly 2: Pricing hasn't changed, but assumption is "pricing problem"—why?
• Anomaly 3: Stability after month 6 suggests survivors are committed
• Priority anomaly: The suddenness. Pricing effects typically show gradual decline.

Hypotheses

Investigation Plan

Phase 1: Contract and pricing structure analysis (1 day)

• Measurement: Do any contracts, trials, or promotional rates end at month 6?
• Method: Review billing data, contract terms
• Success criteria: If yes, explains timing perfectly—pricing hypothesis gains support

Phase 2: Churn survey (1 week)

• Measurement: What do churned customers say caused their departure?
• Method: Email survey to month-6 churners, last 3 cohorts
• Success criteria: If >50% cite single reason, strong signal

Phase 3: Usage pattern analysis (3 days)

• Measurement: Do retained and churned users behave differently in month 5?
• Method: Compare feature usage, engagement metrics pre-churn
• Success criteria: Distinct behavioral signature predicts churn

Resources Required

• Access to billing/contract data
• Survey tool and customer email list
• Product analytics access
• 2 weeks total investigation time

Constraints and Adaptations

• Constraint: Can't interview all churned users → Survey with open-ended option for detailed feedback
• Constraint: Historical data only → Focus on patterns, follow up with prospective study if needed

What Remains Unknown

• Whether month-6 churners would have stayed with intervention
• Long-term value difference between early vs. late churners
• Competitive landscape at this customer stage

Next measurement: Start with Phase 1—if contract terms explain timing, we've found our answer quickly. If not, proceed to parallel execution of Phases 2 and 3.

Integration

This skill is part of the Marie Curie expert persona. Use it when you need to transform vague inquiry into rigorous, measurement-controlled investigation.