By kalpeshgamit
Automatically route and optimize your codebase context for LLM interactions by running a Python script that determines which tools or workflows to activate based on session and prompt.
Analyzes the blast radius of a file change — finds all dependents, transitive imports, affected tests, and risk score. Best before modifying shared files like types, utilities, or core modules.
Packs and compresses the codebase into an AI-friendly context file. Best for new features, code reviews, or full codebase context with minimal tokens.
Scans the codebase for leaked secrets, API keys, passwords, and credentials. Best before committing code or reviewing security posture. 180 patterns across 15 categories.
Checks token counts per file and plans context budget. Shows which files consume the most tokens and how to optimize context window usage.
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