发现 数据库 相关 Skills，比较来源仓库和用途，查看适合你工作流的技能说明。
Systematic marketing experimentation process - discover concepts, generate hypotheses, coordinate multiple experiments, synthesize results, generate next-iteration ideas through rigorous validation cycles
Rigorous hypothesis testing process for data analysis - formulate hypotheses before looking at data, design tests, analyze systematically, interpret with skepticism
Use when concluding work on an open-ended goal to write iteration journal entry documenting work performed, decisions made, and state changes
Systematic exploratory data analysis process - discover patterns in unfamiliar data, identify meaningful insights, formulate specific questions for deeper investigation
Use when saving current iteration progress mid-conversation, before context compaction, or at interim pause points
Systematic process for investigating open-ended questions - decompose vague questions into specific sub-questions, map to data, investigate incrementally, synthesize findings
Use when user wants to compare two autonomy branches to see different approaches, metrics, and outcomes
Systematic comparison of segments, cohorts, or time periods - ensure fair apples-to-apples comparisons, identify meaningful differences, explain WHY differences exist
Use when user wants to safely remove an autonomy worktree while preserving the branch
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 SQL query development in DataPeeker analysis sessions
Use when user wants detailed status report for single autonomy branch including iteration timeline and metrics progression
Component skill for creating compelling data-driven presentations and whitepapers using marp and pandoc with proper citations and reproducibility
Use when user wants to create new autonomy branch with dedicated worktree for parallel agent workflows
Component skill for systematic data profiling and exploration in DataPeeker analysis sessions
Use when user wants to see all autonomy worktrees with their status
Use when beginning a new conversation to work on an open-ended goal, loading context from previous iterations through iteration journals
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...
Use when user wants to set up slime mold exploration strategy with parallel autonomy branches for genetic algorithm approach to problem-solving
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 result interpretation with intellectual honesty in DataPeeker analysis sessions
Component skill for creating effective visualizations (terminal-based and image-based) in DataPeeker analysis sessions
Use when setting up a new open-ended goal for autonomy tracking, before starting the first iteration
Use when user wants to create new autonomy branch from current commit or specific past iteration
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。