Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/agentsope/skillalchemy/agentsop-code-execution-decisionnpx skills add agentsope/SkillAlchemy --skill agentsop-code-execution-decisiongit clone --depth 1 https://github.com/agentsope/SkillAlchemyWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00141 | $0.05924 |
| Opus 5 | $0.00071 | $0.02962 |
| Sonnet 5 | $0.00028 | $0.01185 |
| Haiku 4.5 | $0.00014 | $0.00592 |
Grade A, and why
agentsop-code-execution-decision scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 395 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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).
1. 何时激活 (When to activate)
Activate this skill before committing a step to a reasoning strategy whenever the task has a verifiable, deterministic core — or whenever you catch an agent doing arithmetic in prose.
| Trigger | Signal |
|---|---|
| Arithmetic / math | "compute the compound interest", "what's 17.5% of $4,392.18", multi-step word problems, unit conversions, date deltas |
| Precise data manipulation | "sort these 240 rows by the third column", "dedupe and count", "join these two lists on id", "parse this CSV and sum column B" |
| Deterministic transforms | regex extraction, string reformatting, base conversion, hashing, sorting, set operations |
| Symbolic / combinatorial | "how many distinct permutations", "solve this system of equations", calendar/scheduling math |
| You see a model doing math in prose | "Let me add: 1,204 + 8,991 + ... = 10,195" — almost always worth a code check |
| Choosing a DSPy module | deciding between ChainOfThought and ProgramOfThought for a signature [dspy.ai/learn/programming/modules/] |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 395 lines · 141 tokens per session scan A 84a7f25f9f82
agentsop-code-execution-decision is a skill published in the GitHub repository agentsope/SkillAlchemy (342 stars, last pushed 8d ago), licensed MIT. It adds 141 tokens to every session and 5,924 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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