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 roedyrustam/vibes-plug --skill pydantic-ai-expertgit clone --depth 1 https://github.com/roedyrustam/vibes-plugWrote 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/roedyrustam/vibes-plug/pydantic-ai-expert)<a href="https://agentmods.dev/skills/roedyrustam/vibes-plug/pydantic-ai-expert"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/pydantic-ai-expert/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/roedyrustam/vibes-plug/pydantic-ai-expert"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/pydantic-ai-expert.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 | $0.00054 | $0.01899 |
| Opus 5 | $0.00027 | $0.00949 |
| Sonnet 5 | $0.00011 | $0.00380 |
| Haiku 4.5 | $0.00005 | $0.00190 |
Grade A, and why
pydantic-ai-expert 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 today.
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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pydantic AI Expert (Type-Safe Python Agent Engineering)
English
Orchestration & Integration
Connects and orchestrates with domain skills like python-programming-expert, multi-agent-orchestration, ai-llm-integration-expert, ai-evals-benchmark-expert, and database-orm-expert to engineer type-safe, resilient backend AI agents.
Description
Production-grade guide for building enterprise AI agents using Pydantic AI (from the creators of Pydantic). Unlike untyped or fragile agent libraries, Pydantic AI provides strict static typing (Agent[DepsType, ReturnType]), first-class dependency injection (RunContext[Deps]), automatic tool parameter validation via Pydantic v2 schemas, model-agnostic provider switching (Anthropic, OpenAI, Gemini, Groq, Ollama), dynamic system prompts, and deterministic structured outputs.
Swarm Synergy: Within the Backend & APIs Swarm, this skill serves as the Lead Python Agent Engineer. It executes critical business operations, runs data pipelines, and integrates with SQL databases with compile-time type safety in Phase 4.
Trigger Conditions
- Engineering Python-based autonomous agents requiring strict type safety and schema validation.
- Implementing dependency injection (passing database connections, API clients, or user sessions) into agent tools.
- Extracting guaranteed structured outputs from frontier reasoning models without JSON parsing errors.
- Designing multi-agent delegation or agent handoffs in Python microservices.
- Running unit tests and deterministic evals on agent logic using mock models (
TestModel).
Pydantic AI Architecture
┌─────────────────────────────────────────────────────────────┐
│ AGENT DEFINITION │
│ Agent[DatabaseDeps, AuditReport]( │
│ model='anthropic:claude-3-7-sonnet-latest', │
│ result_type=AuditReport, │
│ ) │
└──────────────────────────────┬──────────────────────────────┘
│
┌───────────────────────┴───────────────────────┐
▼ ▼
┌──────────────────────────────┐ ┌──────────────────────────────┐
│ DEPENDENCY INJECTION │ │ TOOL EXECUTION LOOP │
│ RunContext[DatabaseDeps] │ │ @agent.tool │
│ • Async DB connection pool │ │ • Auto Pydantic validation │
│ • Tenant & session auth │ │ • Structured error return │
└──────────────────────────────┘ └──────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ DETERMINISTIC STRUCTURED RESULT │
│ result.data -> Pydantic BaseModel instance (Validated) │
└─────────────────────────────────────────────────────────────┘
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.
- today First seen · 162 lines · 54 tokens per session scan A b0955ae36308
pydantic-ai-expert is a skill published in the GitHub repository roedyrustam/vibes-plug (53 stars, last pushed today), licensed MIT. It adds 54 tokens to every session and 1,899 once invoked, about $0.0003 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 2026-09-12.
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