Who it is forA developer who wants evidence that the judge's decisions are right before trusting it with live context.
The problemHiding the wrong block could cost time, and it is hard to judge filter quality without seeing the decisions.
The outcomeA week of logged decisions and savings estimates that show whether active mode is safe to enable.
What you need first
- Python 3.10+
- uv installed
- Claude Code 2.1.260 or newer with function hooks enabled (early access)
- A Jev key from TypeSafe, or an Anthropic API key for the adapter judge
- Git to clone the repo
Steps
- Add WINNOW_MODE=shadow to ~/.winnow/env, or to the env block of ~/.claude/settings.json.
- Work in Claude Code as normal for about a week so the hook judges real tool results.
- Run
winnow statsto see how many results would have been rewritten and how many characters would have been saved. - Run
winnow recallwith a key from ~/.winnow/decisions.jsonl to see exactly what would have been hidden. - If the decisions look right, change WINNOW_MODE to active.
Prompt for your agent
In the winnow repo at <repo path>, set WINNOW_MODE=shadow, then run `winnow stats` after a week of Claude Code use. Report the number of results that would have been rewritten and the characters that would have been saved. Verify that the decision log in ~/.winnow/decisions.jsonl contains per-block probabilities.
Caveat. Shadow mode still judges every result and sends the tool output to the judge, so data leaves your machine even though nothing is rewritten.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for GhalebDweikat/winnow and "Test Winnow in shadow mode before it changes results"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
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