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eml-lab

Compile ordinary formulas (exp(x+y), x**y, ln(x*y), sin(x)+cos(x), sqrt(x*y), x/y, asin(x), atan(x), log10(x)) into EML trees, look up calculator-primitive witnesses (exp, ln, add, mult, sub, pow, neg, inv, div, pi, i, sin, cos, tan, sqrt, asin, acos, atan, log10), inspect arbitrary EML trees, or run one-shot compile-render to emit tree + diagram + audit + summary from a sympy expression. Use when a user wants to lower a sympy-parseable expression into the EML IR, ask "how many tokens does the mult witness take?", visualize a tree as Graphviz/Mermaid, convert between nested / RPN / JSON forms, read shape stats (K, depth, leaf histogram), or produce a shareable artifact bundle in one command. Every named elementary primitive has a stored tree; the only `needs_tree` entry is `apex` (the closure proof itself, not a callable primitive).

Resumen

Compile ordinary formulas (exp(x+y), x**y, ln(x*y), sin(x)+cos(x), sqrt(x*y), x/y, asin(x), atan(x), log10(x)) into EML trees, look up calculator-primitive witnesses (exp, ln, add, mult, sub, pow, neg, inv, div, pi, i, sin, cos, tan, sqrt, asin, acos, atan, log10), inspect arbitrary EML trees, or run one-shot compile-render to emit tree + diagram + audit + summary from a sympy expression. Use when a user wants to lower a sympy-parseable expression into the EML IR, ask "how many tokens does the mult witness take?", visualize a tree as Graphviz/Mermaid, convert between nested / RPN / JSON forms, read shape stats (K, depth, leaf histogram), or produce a shareable artifact bundle in one command. Every named elementary primitive has a stored tree; the only `needs_tree` entry is `apex` (the closure proof itself, not a callable primitive).

Comando de instalación
npx skills add https://github.com/yaniv-golan/eml-skill --skill eml-lab

Copia y pega este comando en Claude Code para instalar la habilidad

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Actualizado19 de abril de 2026, 14:12
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