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 magnus919/agent-skills --skill pydanticaigit clone --depth 1 https://github.com/magnus919/agent-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/magnus919/agent-skills/pydanticai)<a href="https://agentmods.dev/skills/magnus919/agent-skills/pydanticai"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/pydanticai/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/magnus919/agent-skills/pydanticai"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/pydanticai.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00093 | $0.03685 |
| Opus 5 | $0.00046 | $0.01843 |
| Sonnet 5 | $0.00019 | $0.00737 |
| Haiku 4.5 | $0.00009 | $0.00368 |
Grade A, and why
pydanticai 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 8d 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 — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PydanticAI & PydanticGraph Expert Skill
PydanticAI is a Python agent framework for building production-grade GenAI applications, built by the team behind Pydantic. PydanticGraph is its companion graph/state-machine library.
Install:
pip install pydantic-ai # Full install (all providers)
pip install "pydantic-ai-slim[openai]" # Minimal install + your provider
Quick Reference
from pydantic_ai import Agent
# Basic agent — one line
agent = Agent('openai:gpt-5.2', instructions='Be concise.')
# Run it
result = agent.run_sync('What is the capital of France?')
print(result.output)
When to Load Which Reference
| Topic | Load When | File |
|---|---|---|
| Agent creation & lifecycle | You need to create, configure, or run an agent — define tools, deps, output types, run methods, streaming | references/core-agents.md |
| Capabilities & hooks | You need built-in capabilities (Thinking, WebSearch, MCP, etc.), on-demand loading, lifecycle hooks, or custom capabilities | references/capabilities-hooks.md |
| PydanticGraph | You need a state machine, graph-based control flow, parallel execution, BaseNode subclasses, or GraphBuilder with joins/decisions | references/graph.md |
| Models, output & streaming | You need multi-model setups, FallbackModel, streaming output, output functions, or structured output with validation | references/models-output.md |
| Multi-agent patterns & integrations | You need agent delegation, programmatic hand-off, MCP servers, durable execution, or UI adapters | references/patterns.md |
| Testing & evaluation | You need TestModel, FunctionModel, pytest patterns, overrides, or Pydantic Evals for systematic eval | references/testing-evals.md |
| Full worked examples | You want complete runnable examples — bank support agent, email feedback graph, multi-agent flight booking | references/examples.md |
| Framework boundaries | You need to compare PydanticAI vs LangGraph for a project, or want to combine them | references/hybrid-pydanticai-langgraph.md — also load skill_view(name='langgraph') |
| API surface reference | You need to find the right import path, class name, or method signature quickly | references/api-reference.md |
What ships with it
11 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.
- evals/evals.json 2.8 KB
- README.md 1.7 KB
- references/api-reference.md 6.6 KB
- references/capabilities-hooks.md 11 KB
- references/core-agents.md 11 KB
- references/examples.md 11 KB
- references/graph.md 9.2 KB
- references/hybrid-pydanticai-langgraph.md 7.1 KB
- references/models-output.md 8.2 KB
- references/patterns.md 7.3 KB
- references/testing-evals.md 4.7 KB
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.
- 8d ago First seen · 248 lines · 93 tokens per session scan A dd79884cfe92
pydanticai is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed yesterday), licensed MIT. It adds 93 tokens to every session and 3,685 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 2026-09-03.
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