Execute map and reduce phases to aggregate batch processing results.
npx skills add https://github.com/ElliotJLT/Claude-Skill-Potions --skill map-reduce
Please help me install this Agent Skill. Skill: map-reduce Source: https://github.com/ElliotJLT/Claude-Skill-Potions/tree/main/skills/map-reduce Command: npx skills add https://github.com/ElliotJLT/Claude-Skill-Potions --skill map-reduce
This Skill provides a structured pattern for processing large collections of items by splitting work into a Map phase and then reducing results, enabling scalable batch operations.
Define a Map-Reduce Job: map-reduce job name. Input: specify the collection of items. Map function: the per-item operation. Reduce function: the aggregation/summary operation. Then execute to obtain the final results.
High-intent search queries and answers about installing and using this skill.
Map-reduce splits batch processing into two phases: a map phase applies an operation to each item in parallel, then a reduce phase aggregates results into a final summary. This pattern scales codebase-wide analyses, bulk transformations, and large-scale data processing tasks efficiently.
Yes. Map-reduce orchestrates codebase analysis by mapping per-file operations—counting lines, functions, complexity—across all files in parallel, then reducing those results into a unified codebase report with aggregated metrics.
Define four elements: a job name, the input collection of items, a map function specifying the per-item operation, and a reduce function that aggregates results. Execute the job to obtain final processed results.
Map-reduce is suited for large-scale data processing where splitting work into independent per-item operations and then aggregating results improves performance. It handles automation and parallel processing across many items more effectively than sequential approaches.
Your reduce function should combine per-item results into a final summary matching your analysis goal. For codebase statistics, aggregate counts and metrics; for bulk transformations, consolidate outputs. The strategy depends on your end-state reporting or data needs.
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