Teaching Claude to Speak Manager, Kid, and Engineer

What it does This prompt wrapper detects your target audience from casual phrasing — “explain this to my mom” or “break this down for a 5th grader” — and recalibrates vocabulary, analogies, tone, and depth to match. It handles age groups, grade levels, job roles, and family relationships. The idea is to stop generic explanations and start audience-specific ones without manual prompt engineering each time.

The interesting bit The project includes a proper evaluation harness that runs each prompt twice — once with the skill, once without — then auto-grades outputs against audience-specific assertions using Claude itself. The README shows the skill hitting an 83.3% pass rate versus 41.6% baseline, with the biggest lift coming from non-technical audiences like managers. That meta-evaluation approach is more rigorous than most prompt-wrapper repos bother with.

Key highlights

• Detects audience intent from casual phrasing rather than requiring explicit flags
• Calibrates five dimensions: vocabulary, analogies, tone, depth, and framing
• Ships with an auto-grading evaluation suite that compares skill-on vs skill-off performance
• Evaluation results are versioned in auto-incrementing iteration directories
• Baseline performance for managers was 0%; with the skill it reached 50%

Caveats

• Tightly bound to Claude Code; useless outside that ecosystem
• No support for non-English audiences yet
• Evaluation coverage is explicitly flagged by the author as needing more test cases

Verdict Grab this if you live in Claude Code and spend half your time translating technical concepts for different rooms. Skip it if you need a universal API or prompt library that works across LLM platforms.