Detect Drift In Designs and Code

When running in Codex or ChatGPT Work, first read Codex integration. In Claude Code or Cowork, read ${CLAUDE_PLUGIN_ROOT}/docs/claude.md first.

When the user asks to detect drift across all designs or a set of READMEs, the goal is to identify all instances where the codebase has diverged or failed to implement the specifications described by those designs or READMEs.

1. Check Input: Verify a directory containing design documents or READMEs is provided. If not provided, ask the user to specify the target directory.

2. List Documents: List all files in the design directory.

3. Analyze: For EACH design document:

   • Contextualize: Read the design document. Identify the intent, proposed changes, and specific filenames/symbols mentioned.
   • Locate Source: Find the corresponding source code files in the source tree. If paths aren't explicit, use code search or grep to locate the relevant classes, functions, or API endpoints mentioned in the doc. Prompt the user to add missing links to the document to make future detection cheaper.
   • Verify: Read the current state of those source files.
   • Detect Drift: Compare the design vs. reality. Look specifically for:
     • Missing Features: Logic described in the doc but absent in the code.
     • Incomplete Models: Dataclasses/Classes/Structs/Protos missing fields specified in the design.
     • Logic Shortcuts: Hardcoded values where dynamic logic was planned.
     • Architectural Divergence: Implementation using different patterns/libraries than proposed.
   • Filter: Report ONLY the differences. Do not describe what is working or what matches. If the code matches the design perfectly, output "No drift detected."

4. Report: Produce a single report listing all discrepancies found.