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 ag2ai/ag2-skills --skill ag2-structured-outputgit clone --depth 1 https://github.com/ag2ai/ag2-skillsWrote 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/ag2ai/ag2-skills/ag2-structured-output)<a href="https://agentmods.dev/skills/ag2ai/ag2-skills/ag2-structured-output"><img src="https://agentmods.dev/badge/skills/ag2ai/ag2-skills/ag2-structured-output/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/ag2ai/ag2-skills/ag2-structured-output"><img src="https://agentmods.dev/badge/skills/ag2ai/ag2-skills/ag2-structured-output.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00117 | $0.01657 |
| Opus 5 | $0.00059 | $0.00829 |
| Sonnet 5 | $0.00023 | $0.00331 |
| Haiku 4.5 | $0.00012 | $0.00166 |
Grade A, and why
ag2-structured-output 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 10d 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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Structured output
When to use
- The user wants a Pydantic model, dataclass, dict, primitive, or union back — not a string.
- They're doing classification, extraction, scoring, normalisation, or anything where downstream code parses the reply.
- They want automatic retry on validation failure.
60-second recipe
from pydantic import BaseModel, Field
from typing import Annotated
from ag2 import Agent
from ag2.config import OpenAIConfig
class TicketTriage(BaseModel):
category: Annotated[str, Field(description="e.g. billing, bug, account_access")]
urgency: Annotated[str, Field(description="low, medium, or high")]
summary_one_line: Annotated[str, Field(description="Max 120 characters", max_length=120)]
agent = Agent(
"triage",
prompt="You triage support messages. Be conservative with urgency.",
config=OpenAIConfig(model="gpt-4o-mini"),
response_schema=TicketTriage,
)
reply = await agent.ask("I was charged twice and can't export reports. Quarter close blocked.")
triage = await reply.content() # → typed TicketTriage
print(triage.category, triage.urgency)
reply.body is still the raw model text; await reply.content() runs validation and returns the parsed value. If validation fails, content() raises (e.g. pydantic.ValidationError).
Schema types you can pass
| Type | What you get |
|---|---|
Primitive (int, float, bool) |
Bare value, framework wraps in {"data": ...} for the API |
dataclass |
Instance of the dataclass |
Pydantic BaseModel |
Instance of the model |
Union (int | str, (int, str)) |
One of the alternatives |
dict[K, V], TypedDict |
Validated dict |
ResponseSchema(...) |
Same as above, with explicit name / description for the provider |
@response_schema callable |
Custom validation/parsing logic |
PromptedSchema(inner) |
Schema injected into the system prompt for providers without native structured output |
ResponseSchema — name your payload
What ships with it
1 file 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.
- 10d ago First seen · 177 lines · 117 tokens per session scan A fd3f62ee4159
ag2-structured-output is a skill published in the GitHub repository ag2ai/ag2-skills (10 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 117 tokens to every session and 1,657 once invoked, about $0.0006 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 2026-08-31.
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