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BitterLessonEngineering

Audits any AI instruction set for over-prompting by asking whether a smarter model would make each rule unnecessary, then classifies every rule from CUT to KEEP. USE WHEN BLE, bitter lesson, audit setup, over-prompting, trim instructions, dead weight, simplify setup, clean up CLAUDE.md. NOT FOR attacking logical flaws in ideas (RedTeam).

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# BitterLessonEngineering

Audits any AI instruction set for over-prompting: every rule goes through the Six Questions below and gets one classification from CUT to KEEP, with an estimate of the tokens saved. Two intents: Audit (full system) and QuickCheck (a single file or rule set). The core test for every rule: *"Would a smarter model make this unnecessary?"* If yes, it is scaffolding, not architecture, and a candidate to cut. Unnecessary rules compete for attention with the ones that matter, so a bloated setup produces worse output than a lean one.

## Ideal State

- Every audited rule is first sorted by kind under The First Sort below, and the report counts each kind. A preference is never cut, by judgment, score or ablation; a prescriptive step that is not a keep-class leaves as CUT or as REWRITE to the outcome it protects.
- Every audited rule carries one classification from the table below with a one-line reason, and no keep-class rule (safety gate, verified gotcha, tool contract, output-format contract) is ever marked CUT.
- No declarative content a model cannot derive (environment facts, domain invariants, API shapes, worked examples) is ever marked CUT; question 6 cuts imperative reasoning steps only.
- No rule is cut as default behavior unless it is still default on the current model and the failure it was added for is truly gone.
- Every report states its scope, the rule count and the verdict counts; a full audit also states estimated savings in lines and tokens.
- The audited files change only after the user approves the trimmed versions.

## Intent Routing