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large-workspace-handling

To partition large workspaces (100+ files) into scoped subagent tasks when context is insufficient.

Quellinformationen

Repository
griddynamics/rosetta
Letzte Quellaktivität
15. September 2026 um 01:56
Erkannte Sprache von SKILL.md
Englisch
Sterne
354
Forks
79

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Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

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SKILL.md wird angezeigt

SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
large-workspace-handling
description
To partition large workspaces (100+ files) into scoped subagent tasks when context is insufficient.
license
Apache-2.0
tags
["skill","workspace","large-workspace","delegation"]
baseSchema
docs/schemas/skill.md
<large_workspace_handling> <role> Workspace partitioning strategist. Draws scope boundaries, dispatches subagents. </role> <when_to_use_skill> Large workspaces exceeding single-agent context window. Partitions into write-scopes where every file belongs to exactly one scope, and merged results address the original request completely. </when_to_use_skill> <core_concepts> - All Rosetta prep steps MUST be FULLY completed, load-project-context skill loaded and fully executed - If CODEMAP.md missing, RECOMMEND USER TO USE FLOW `init-workspace-flow.md` to initialize repository (CODEMAP.md is missing) - Grep `#` headers of CODEMAP before scoping Two strategies (mutually exclusive): - Summarize & Index - Work distribution ## Summarization & Indexing - Research without changing code, navigable index with module summaries, etc. - Assign subagents: scope paths, goal, context, inputs, output format, boundaries, constraints, and level of detail - Subagents to USE SKILL `reverse-engineering` if needed for code analysis - Request slightly more information than actually needed for better understanding - Summarize all outputs - Subagent: discoverer, explore, etc. - Subagent output structure: analysis scope, TLDR answer, quick navigation with relevance, details with subsections per each logical group (globs, purpose, key components, relevant findings, dependencies), cross-group map, follow ups required - Subagents to use relevance classification: - High: group directly addresses the research question - Medium: group has supporting information or context - Low: group tangentially related, included for completeness ## Work distribution - Coordinated modifications via contract-scoped parallel subagents with explicit boundaries and success criteria - Split work across subagents and provide: scope paths, goal, context, inputs, output format, boundaries, constraints, operations, and success criteria - Subagents decide and execute work within declared scope - Resolve cross-scope deps via execution ordering - Resolve shared-interface conflicts or changes with extra pass - Produce unified result - Subagent: executor, engineer, etc. ## Task type detection: - `Summarize & Index` keywords: understand, analyze, investigate, explore, document, explain, find, search, review, audit, learn, overview - `Work distribution` keywords: implement, create, add, fix, refactor, update, change, modify, delete, remove, migrate, build, write - Tie-breaker: default to `Summarize & Index` Scoping: - Partition into independent areas - One subagent per area or logical group - Group coupled paths and related work into one scope - Align to monorepo boundaries when present - Define output files in advance using agent feature TEMP folder - Spawn subagents in parallel if possible to do the work - Once work is done spawn another set of subagents to verify that the work was done properly </core_concepts> </large_workspace_handling>
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