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 | $0.04666 |
| Opus 5.5 | $0.00022 | $0.01866 |
| Sonnet 5.5 | $0.00011 | $0.00933 |
| Haiku 4.5 | $0.00005 | $0.00467 |
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 5d 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 — 446 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
- Python Language & Runtime:
python-programming-expert,structured-output-contracts - Agent Frameworks & Swarms:
multi-agent-orchestration,ai-llm-integration-expert,frontier-ai-models-expert - MCP & Tool Protocols:
mcp-server-architect,parallel-tool-orchestrator - Persistence & Databases:
database-orm-expert,vector-db-rag-expert - Evaluation & Testing:
autonomous-tdd-debugger,property-mutation-testing-expert,anti-slop - Lifecycle & Governance:
zero-to-prod-orchestrator,session-memory-manager
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).
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.
- 5d ago Changed · +216 lines 6491d247f151
- 6d ago Changed · +1 lines 58c35928be50
- 9d ago Changed · +67 lines 2b91c9487565
- 18d ago Changed 675d15cd99a4
- 23d ago Changed 685dcbb45c11
- 24d ago Changed a66a479c3ab8
- 26d ago Changed · -6 lines · +39 tokens per session 6dac8d81f2fe
- 28d ago Changed · +6 lines · -39 tokens per session 681bd88c0023
- 1mo ago Changed 4ad0a9826ea3
- 1mo ago 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 (114 stars, last pushed yesterday), licensed MIT. It adds 54 tokens to every session and 4,666 once invoked, about $0.0002 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 2026-09-12.
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