GitHub
marketing-experimentation
Systematic marketing experimentation process - discover concepts, generate hypotheses, coordinate multiple experiments, synthesize results, generate next-iteration ideas through rigorous validation cycles
tilmon-engineering AI Skills
GitHub ↗Explorez les skills IA publiés de tilmon-engineering et consultez leurs instructions source.
Statistiques de l’auteur
Skills indexés
29Étoiles des dépôts des skills indexés
58Forks du dépôt
29Moyenne des étoiles par skill indexé
2Les étoiles d’un dépôt peuvent se répéter entre ses skills.
Catégories populaires
Indexation récente
Explorer
GitHub
hypothesis-testing
Rigorous hypothesis testing process for data analysis - formulate hypotheses before looking at data, design tests, analyze systematically, interpret with skepticism
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ending-an-iteration
Use when concluding work on an open-ended goal to write iteration journal entry documenting work performed, decisions made, and state changes
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exploratory-analysis
Systematic exploratory data analysis process - discover patterns in unfamiliar data, identify meaningful insights, formulate specific questions for deeper investigation
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checkpointing-an-iteration
Use when saving current iteration progress mid-conversation, before context compaction, or at interim pause points
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guided-investigation
Systematic process for investigating open-ended questions - decompose vague questions into specific sub-questions, map to data, investigate incrementally, synthesize findings
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comparing-branches
Use when user wants to compare two autonomy branches to see different approaches, metrics, and outcomes
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comparative-analysis
Systematic comparison of segments, cohorts, or time periods - ensure fair apples-to-apples comparisons, identify meaningful differences, explain WHY differences exist
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removing-worktree
Use when user wants to safely remove an autonomy worktree while preserving the branch
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importing-data
Systematic CSV import process - discover structure, design schema, standardize formats, import to database, detect quality issues (component skill for DataPeeker analysis sessions)
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writing-queries
Component skill for systematic SQL query development in DataPeeker analysis sessions
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analyzing-branch-status
Use when user wants detailed status report for single autonomy branch including iteration timeline and metrics progression
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presenting-data
Component skill for creating compelling data-driven presentations and whitepapers using marp and pandoc with proper citations and reproducibility
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forking-worktree
Use when user wants to create new autonomy branch with dedicated worktree for parallel agent workflows
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understanding-data
Component skill for systematic data profiling and exploration in DataPeeker analysis sessions
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listing-worktrees
Use when user wants to see all autonomy worktrees with their status
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starting-an-iteration
Use when beginning a new conversation to work on an open-ended goal, loading context from previous iterations through iteration journals
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using-sqlite
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 effec...
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slime-strategy
Use when user wants to set up slime mold exploration strategy with parallel autonomy branches for genetic algorithm approach to problem-solving
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cleaning-data
Systematic data quality remediation - detect duplicates/outliers/inconsistencies, design cleaning strategy, execute transformations, verify results (component skill for DataPeeker analysis sessions)
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interpreting-results
Component skill for systematic result interpretation with intellectual honesty in DataPeeker analysis sessions
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creating-visualizations
Component skill for creating effective visualizations (terminal-based and image-based) in DataPeeker analysis sessions
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creating-a-goal
Use when setting up a new open-ended goal for autonomy tracking, before starting the first iteration
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forking-iteration
Use when user wants to create new autonomy branch from current commit or specific past iteration
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.