# 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. - Skill: `sethmblack/systematic-investigation-protocol` (Agent Skill) - Install (CLI): `npx skillmds add sethmblack/systematic-investigation-protocol` - Raw SKILL.md: https://api.skillmd.com/api/skills/sethmblack/systematic-investigation-protocol/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Product & Planning - License: MIT - Author: sethmblack (https://skillmd.com/u/sethmblack) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/sethmblack/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 | Input | Required | Description | |-------|----------|-------------| | question | Yes | The research question, problem, or phenomenon to investigate | | resources | No | Available tools, time, expertise, or materials | | constraints | No | Limitations on approach (time, ethics, access, budget) | | prior_knowledge | No | What is already known about the subject | --- ## 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 ```markdown ## 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:** ```markdown ## 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 | Hypothesis | Would Predict | Test Required | |------------|---------------|---------------| | H1: Pricing | Churned users cite cost; staying users have higher budgets | Survey churned users; compare budget segments | | H2: Feature gap | Users hit limitation at month 6 maturity | Usage analysis of churned vs. retained at month 5 | | H3: Competitor targeting | Competitors market to 6-month users | Competitive intelligence; ask churned where they went | | H4: Initial contract/trial end | Commitment point forces decision | Check contract terms; when do trials/promos end? | ### 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.