BitterPillEngineering

Audits AI instruction files to cut redundant, contradictory, or vague rules and estimate token savings.

Updated Sep 3, 2026
One-click install
npx skills add https://github.com/ruban-s/DevOS --skill bitterpillengineering-ruban-s
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: BitterPillEngineering
Source: https://github.com/ruban-s/DevOS/tree/main/skills/BitterPillEngineering
Command: npx skills add https://github.com/ruban-s/DevOS --skill bitterpillengineering-ruban-s

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? AI instruction setups (CLAUDE.md, settings.json load lists, system prompts) accumulate bloated rules over time — duplicates, contradictions, one-off incident patches, and vague directives that dilute model attention and degrade output quality. This Skill systematically identifies which rules earn their tokens and which are dead weight. ## Core Features & Use Cases - Five Questions scoring: Evaluates each rule against five checks (already default behavior, contradictory, duplicate, one-off patch, vague) plus a procedure-vs-outcome screen for methodology bloat. - Verdict system: Assigns each rule a verdict of CUT, RESOLVE, MERGE, EVALUATE, SHARPEN, MOVE, or KEEP, with estimated line and token savings. - Two workflows: Audit performs a full pass over every force-loaded file mapped in settings.json; QuickCheck gives a rapid verdict on a single file or pasted rule block. - Use Case: After months of appending guardrails to a CLAUDE.md, run the Audit workflow to find rules that merely echo built-in model behavior, then produce cleaned copies with the dead weight removed. ## Quick Start Ask the agent to run a BitterPillEngineering audit on your setup to score every instruction rule and report which ones to cut, merge, or keep.

Frequently Asked Questions about BitterPillEngineering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reduce bloat in my CLAUDE.md or AI instruction files?▼

Run the Audit workflow, which reads every force-loaded file listed in settings.json and scores each rule against five checks: whether it echoes default model behavior, contradicts another rule, duplicates one, patches a single past incident, or is too vague to follow. Each rule gets a verdict with estimated token savings.

How to check a single instruction file for redundant rules?▼

Use the QuickCheck workflow with a file path or pasted rule block. It runs the same five-question scoring on just that target and returns a short report listing rules to keep, cut, or sharpen with reasons.

What kinds of AI prompt rules should be kept versus cut?▼

Keep concrete, actionable, non-default rules such as check harnesses, tool contracts, output format requirements, and verified gotchas. Cut rules that restate built-in model behavior, choreograph reasoning steps, or use vague directives like personality sliders.

When should I not use this instruction-trimming approach?▼

Do not use it to attack logical flaws in ideas or arguments — that is a red-teaming task. Also verify before deleting incident-patch rules, since some suppress repeat failures, and confirm a rule truly echoes current model defaults since built-in behavior shifts between model releases.

Does the audit modify my instruction files automatically?▼

No. The Audit workflow produces a grouped report with verdicts and estimated savings first, and only produces cleaned copies of the files after operator approval. It also supports an advisory mechanical pre-scan via a lint tool to surface candidates cheaply.