Tilmon Engineering

Rigorous hypothesis testing process for data analysis - formulate hypotheses before looking at data, design tests, analyze systematically, interpret with skepticism

Component skill for systematic result interpretation with intellectual honesty in DataPeeker analysis sessions

Systematic data quality remediation - detect duplicates/outliers/inconsistencies, design cleaning strategy, execute transformations, verify results (component skill for DataPeeker analysis sessions)

Use when conducting customer discovery interviews, user research, surveys, focus groups, or observational research requiring rigorous analysis - provides systematic 6-phase framework with mandatory bias prevention (reflexivity, intercoder reliability, disconfirming evidence search) and reproducible methodology; peer to hypothesis-testing for qualitative vs quantitative validation

Systematic exploratory data analysis process - discover patterns in unfamiliar data, identify meaningful insights, formulate specific questions for deeper investigation

Use when working with SQLite databases in DataPeeker analysis sessions - querying data, importing CSVs, exploring schemas, formatting output, or optimizing performance. Provides task-oriented guidance for effective SQLite CLI usage in data analysis workflows.

Component skill for systematic data profiling and exploration in DataPeeker analysis sessions

Component skill for creating compelling data-driven presentations and whitepapers using marp and pandoc with proper citations and reproducibility

Identify foreign key relationships between tables using heuristics, value overlap analysis, and referential integrity checks

Find near-duplicate records using fuzzy string matching on key text columns

Systematic process for investigating open-ended questions - decompose vague questions into specific sub-questions, map to data, investigate incrementally, synthesize findings

Systematic marketing experimentation process - discover concepts, generate hypotheses, coordinate multiple experiments, synthesize results, generate next-iteration ideas through rigorous validation cycles

Detect statistical outliers in numeric columns using MAD (Median Absolute Deviation) with 3 MAD threshold

Identify foreign key relationships using heuristics, value overlap, cardinality analysis, and referential integrity validation

Validate marketing concepts via internet research - market demand signals, similar solutions, audience needs, and validation evidence

Systematic CSV import process - discover structure, design schema, standardize formats, import to database, detect quality issues (component skill for DataPeeker analysis sessions)

Systematic comparison of segments, cohorts, or time periods - ensure fair apples-to-apples comparisons, identify meaningful differences, explain WHY differences exist

Component skill for systematic SQL query development in DataPeeker analysis sessions

Identify exact duplicate rows in a table, return groups with counts and examples

Component skill for creating effective visualizations (terminal-based and image-based) in DataPeeker analysis sessions

Analyze free text column values and propose categorical groupings with complete value-to-category mappings

Analyze raw_* table for quality issues - NULL percentages, duplicates, outliers (3 MAD), free text candidates