Individual skill

agentsop-code-execution-decision

Decision rubric for when an LM agent should write-and-run code (Program-of-Thought / code interpreter) versus reason in natural language: classify each step as deterministic- computable (emit + execute code, feed the result back) vs judgment (stay in prose). Use when designing or debugging an agent step that does arithmetic/parsing/data transforms, when prose reasoning hallucinates a computation (under-coding), or when a sandbox round- trip is wasted on a judgment task (over-coding). Search keywords: code interpreter, agent does math wrong, calculator hallucination, when to run code vs reason, program of thought, PoT, tool vs reasoning.

skills/agentsop-code-execution-decision/SKILL.md

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# Code-Execution Decision — emit-code-vs-reason

> **One-liner**: LMs are unreliable calculators but reliable coders. When the answer needs
> determinism and precision — arithmetic, exact data manipulation, deterministic transforms —
> emit code and run it. When the answer needs judgment, taste, or open-ended synthesis, reason
> in natural language. The cost of getting this gate wrong is silent: prose arithmetic
> *hallucinates a plausible-looking wrong number*, and over-coding a judgment task burns a
> sandbox round-trip for nothing.

This is an **enhancement overlay**. DSPy already gives you `dspy.ProgramOfThought` (PoT) —
the *mechanism* for write-then-execute. What it does not give you is the *decision rubric* for
when to reach for it. That rubric is this skill. Cross-link the sibling
[[agentsop-output-format-by-model]] (which decides *how* code-shaped content should be serialized) and
[[agentsop-test-fix-loop]] (which closes the execute → error → retry loop).