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-structured-output-pickernpx skills add agentsope/SkillAlchemy --skill agentsop-structured-output-pickergit 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-structured-output-picker)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-structured-output-picker"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-structured-output-picker.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00105 | $0.05428 |
| Opus 5 | $0.00053 | $0.02714 |
| Sonnet 5 | $0.00021 | $0.01086 |
| Haiku 4.5 | $0.00011 | $0.00543 |
Grade A, and why
agentsop-structured-output-picker 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 6d 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 — 386 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Structured-Output-Picker — 在解码处约束，还是在校验处约束？
One-liner: Three local libraries (Outlines, Instructor, Guidance) plus provider-native structured outputs all "make the model emit valid structure", but they enforce at different points and fail differently. Pick by how costly a malformed output is and whether you control the decoder. Then pick the failure stance — Assert (hard-fail + retry) vs Suggest (soft nudge, degrade gracefully) — borrowed from DSPy's constraint primitives.
This is an ENHANCE overlay. The four enforcement mechanisms each have a working local skill; what no single one provides is the cross-library *which-one
- how-to-handle-failure* decision. That gap is hit every time an LM output is
consumed by code. For what shape the content should take (code vs JSON vs
prose), descend first to
[[agentsop-output-format-by-model]]— this skill assumes the shape is already chosen and asks only how to enforce it.
1. 何时激活 (When to activate)
Activate after you have decided the output shape (via
[[agentsop-output-format-by-model]]) and the answer was "a typed/validated object", and
before you write the parsing code.
| Trigger | Signal |
|---|---|
| LM output feeds a parser | json.loads(resp) / Model.model_validate(...) is in the next line of code |
| You picked a typed shape | format-by-model said "JSON / Pydantic / typed field" — now: who enforces it? |
| Repeated parse failures | JSONDecodeError, ValidationError, truncated/extra-prose responses in logs |
| Library is already installed | outlines, instructor, or guidance is in the env and you must choose between them |
| An enum / regex / range must hold | output must be one of N labels, a valid date, a bounded int |
| You must decide failure stance | "if the model returns garbage, do I crash, retry, or accept-and-flag?" |
Anti-triggers (skip this skill):
- The content is code / multi-step reasoning / long prose — go back to
[[agentsop-output-format-by-model]]; enforcing a JSON grammar on code is the headline anti-pattern there (Aider 61%→20%). Enforcement strength is the wrong question when the shape is wrong. - No code consumes the output yet (one-shot exploration).
- The shape is one token / one number — any parser works; no library needed.
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.
- 6d ago First seen · 386 lines · 105 tokens per session scan A dc3c3913fd51
agentsop-structured-output-picker is a skill published in the GitHub repository agentsope/SkillAlchemy (361 stars, last pushed 3d ago), licensed MIT. It adds 105 tokens to every session and 5,428 once invoked, about $0.0005 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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