empathic-expressions

"Understand intent, generate correct code, teach gently."

MOOLLM K-Lines

| K-Line | Why Related | |--------|-------------| | moollm/ | Core MOOLLM principle | | empathic-templates/ | Uses expressions in templates | | postel/ | The law underlying generous interpretation | | sniffable-python/ | Empathic interpretation of code | | speed-of-light/ | Work in vectors, delay tokenization | | yaml-jazz/ | Comments carry meaning here too | | coherence-engine/ | LLM understands intent |

The big-tent skill for interpreting user intent across ALL programming languages.

Quick Overview

What it does:

• Interprets fuzzy, approximate, vernacular code
• Generates correct, idiomatic output
• Teaches the proper form as a gift (not a correction)
• Supports code-switching between languages
• Asks for clarification when truly ambiguous

Languages supported:

• Empathic SQL
• Empathic Python
• Empathic JavaScript
• Empathic Bash
• Empathic YAML
• Any language the LLM understands

The Philosophy

Traditional parsers are strict: syntax correct or rejected.

Empathic Expressions is generous: understands what you MEANT, generates what you NEED.

This is what LLMs are great at. Lean into it.

Example

User writes:

get users who signed up this month but haven't verified email

LLM generates:

SELECT * FROM users 
WHERE created_at >= DATE_TRUNC('month', CURRENT_DATE)
  AND email_verified = FALSE;

LLM teaches:

I interpreted "this month" as the start of the current month,
and "haven't verified" as email_verified = FALSE.

Files

| File | Purpose | |------|---------| | SKILL.md | Full specification | | CARD.yml | Machine-readable interface |