vcscout

Solid

Fast codebase scouting using shell search and optional parallel research agents. Use for file discovery, task context gathering, and quick scoped searches across directories.

AI & Automation 852 stars 197 forks Updated 1 weeks ago MIT

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# Scout Fast, token-efficient codebase scouting using parallel agents to find files needed for tasks. ## Arguments - Default: Scout using local shell search plus optional parallel `research-agent` delegation (`./references/internal-scouting.md`) - `ext`: Scout using external Gemini/OpenCode CLI tools in parallel (`./references/external-scouting.md`) ## When to Use - Beginning work on feature spanning multiple directories - User mentions needing to "find", "locate", or "search for" files - Starting debugging session requiring file relationships understanding - User asks about project structure or where functionality lives - Before changes that might affect multiple codebase parts ## Quick Start 1. Analyze user prompt to identify search targets 2. Use a wide range of Grep and Glob patterns to find relevant files and estimate scale of the codebase 3. Use local shell search first, then optionally spawn parallel `research-agent` workers with divided directories when the search space is large 4. Collect results into concise report ## Configuration Read from `.claude/.vc.json` (falls back to legacy `.claude/.ck.json` if present): - `gemini.model` - Gemini model (default: `gemini-3-flash-preview`) ## Workflow ### 1. Analyze Task - Parse user prompt for search targets - Identify key directories, patterns, file types, lines of code - Determine optimal SCALE value of subagents to spawn ### 2. Divide and Conquer - Split codebase into logical segments per agent - Assign each ag...

Details

Author
withkynam
Repository
withkynam/vibecode-pro-max-kit
Created
2 weeks ago
Last Updated
1 weeks ago
Language
JavaScript
License
MIT

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