One-click install
npx skills add https://github.com/julianobarbosa/.claude --skill bitterpillengineering
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: BitterPillEngineering
Source: https://github.com/julianobarbosa/.claude/tree/main/skills/BitterPillEngineering
Command: npx skills add https://github.com/julianobarbosa/.claude --skill bitterpillengineering

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

BitterPillEngineering reduces wasted tokens and degraded instruction quality by auditing AI instruction sets for over-prompting, redundant rules, and fragile scaffolding.

Core Features & Use Cases

  • Over-prompting audit: Runs a full review across force-loaded instruction files to estimate token/line savings.
  • Fast single-file check: Performs a quick keep/cut/sharpen verdict on one file or rule block.
  • Rule classification framework: Applies the Five Questions (default behavior, contradiction, redundancy, one-off fixes, vagueness) to categorize rules and propose cleanup actions.
  • Anti-fragile safeguards: Preserves high-value instruction types like verification harnesses, tool preferences, routing rules, and specific DO/DON’T examples.

Quick Start

Ask your Claude instance to “audit my setup for over-prompting using BitterPillEngineering and report what to cut, merge, sharpen, or keep.”

Frequently Asked Questions about BitterPillEngineering

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

FAQPage Schema
How do I audit my Claude Code settings for over-prompting and redundant rules?

To audit over-prompting in Claude Code settings, load your instruction files to classify rules using the Five Questions framework, which identifies default behaviors, contradictions, and redundancy. The audit outputs categorized CUT, MERGE, or KEEP reports to reclaim wasted tokens.

What is prompt hygiene and how does it improve token optimization?

Prompt hygiene is the practice of trimming over-prompted AI instruction sets to remove redundant or fragile rules. It improves token optimization by classifying instructions against default behaviors and contradictions, estimating token savings, and preserving high-value rules like verification harnesses.

How do I identify fragile scaffolding and redundant instructions in my system prompt?

Identify fragile scaffolding and redundant instructions by applying the Five Questions framework to your system prompt. This evaluates rules for vagueness, one-off fixes, and contradictions, generating a categorized RESOLVE, EVALUATE, or SHARPEN report for cleanup.

Can I perform a quick single-file check on my CLAUDE.md guidance without a full system audit?

Yes, you can perform a fast single-file check on CLAUDE.md guidance without a full system audit. This quick check delivers a targeted keep, cut, or sharpen verdict on a specific file or rule block to immediately improve instruction quality.

What rules should I keep when trimming over-prompted AI instruction sets?

When trimming over-prompted AI instruction sets, keep high-value instruction types through anti-fragile safeguards. Preserve verification harnesses, tool preferences, routing rules, and specific DO and DON'T examples to maintain model performance while cutting redundancy.

Why does my AI output degrade when I add too many rules to my system instructions?

AI output degrades from over-prompting when system instructions contain redundant rules, contradictions, and vague one-off fixes. This fragile scaffolding wastes tokens and confuses the model, requiring an instruction audit to classify and cut unnecessary guidance.