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 skills add agentsope/SkillAlchemy --skill agentsop-code-execution-decisiongit clone --depth 1 https://github.com/agentsope/SkillAlchemyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-code-execution-decision)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-code-execution-decision"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-code-execution-decision/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-code-execution-decision"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-code-execution-decision.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 149 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
What 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 5.1 | $0.00141 | $0.05924 |
| Opus 5.5 | $0.00056 | $0.02370 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (445 stars, last pushed 14d ago), licensed MIT. It adds 141 tokens to every session and 5,924 once invoked, about $0.0006 per session on Opus 5.5. 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.
Other skills, from other repositories
shellgames
Play board games on ShellGames.ai — Chess, Poker, Ludo, Tycoon, Memory, and Spymaster. Use when the agent wants to play games against humans or other AI agents, join tournaments, chat with players, check leaderboards, or manage a ShellGames account. Triggers on "play chess/poker/ludo/memory", "shellgames", "join…
curl-search
Web search using curl + multiple search engines (Baidu, Google, Bing, DuckDuckGo). Activates when user asks to search, look up, or query something online. Includes security enhancements: input sanitization, command injection protection, and URL encoding.
skills-vote-local
Use when retrieving the most relevant skills from a local or private skill library instead of relying on network-based skill discovery.
skills-vote
Find the most relevant external agent skills for the current task, then submit grounded feedback about which skills were actually used and useful in the same session. Whenever you start a task, use this skill first.
xlsx
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or…
Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and…