Tilmon Engineering

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

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

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

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

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

Component skill for systematic result interpretation with intellectual honesty in 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

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

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

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

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

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

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

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.

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

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

Component skill for systematic SQL query development in DataPeeker analysis sessions

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

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

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

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