Pydantic AI is a typed Python SDK for building AI agents that can use different language models and interfaces, including voice, image generation, and embeddings. Developers use it for applications ranging from structured data extraction to long-running multi-agent work. The catalogue entries provide workflows and configuration for its coding-agent and harness features.
Borrowing it
Nothing to install: this file belongs to pydantic/pydantic-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/pydantic/pydantic-ai/main/.agents/skills/add-new-model/SKILL.mdgit clone --depth 1 https://github.com/pydantic/pydantic-aiWrote 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/pydantic/pydantic-ai/add-new-model)<a href="https://agentmods.dev/skills/pydantic/pydantic-ai/add-new-model"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/add-new-model/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/pydantic/pydantic-ai/add-new-model"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/add-new-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 8 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 34 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 202 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Rogue Agent · line 227 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
- medium Data Exfiltration · line 27 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 28 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 29 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 198 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 31 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00097 | $0.07679 |
| Opus 5 | $0.00048 | $0.03839 |
| Sonnet 5 | $0.00019 | $0.01536 |
| Haiku 4.5 | $0.00010 | $0.00768 |
Grade A, and why
add-new-model scanned grade A with 1 finding 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 yesterday.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| OpenAI | `curl -s https://api.openai.com/v1/models -H "Authorization: Bearer $OPENAI_API_KEY"` | How it starts
The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add New Model
Wire a newly-released provider model into pydantic-ai. Optimized for the common case (mirror an existing sibling); flags the cases where it's not a mirror and needs deeper work.
Reference docs (read once before scoping)
agent_docs/pydantic-ai-slim.md— the Ownership section, pluspydantic_ai_slim/pydantic_ai/native_tools/AGENTS.md, for the user-visible surface this model needs to land on.pydantic_ai_slim/pydantic_ai/profiles/AGENTS.md,providers/AGENTS.md,models/AGENTS.md, andpydantic_ai_slim/pydantic_ai/AGENTS.md(the capability-flag andProvider.model_profile()rules), plus the Design Rules section ofagent_docs/pydantic-ai-slim.md. These tell you where capability facts belong (profile vs. provider vs. model class) when the new id has non-mirror behavior.
Inputs
User invokes with provider + model id (e.g. openai gpt-5.6). If missing, ask via AskUserQuestion.
Image generation models
Image-only models use a separate public surface from conversational models. If the model is consumed by ImageGenerator, update KnownImageGenerationModelName in pydantic_ai_slim/pydantic_ai/images/__init__.py, the relevant direct provider adapter, and its tests; do not also change conversational KnownModelName, profiles, gateway aliases, ImageGenerationTool, or models/<provider>.py unless that surface is explicitly supported and in scope. Add only the public model IDs the project intends to support, and do not infer or automatically add dated snapshots.
Keep common, provider-agnostic controls in images/settings.py, but import provider-specific setting types from the official SDK. Put model-specific size and aspect-ratio validation or mapping in the private images/_<provider>_geometry.py helper, and update the public support matrix in docs/image-generation.md. Verify geometry against official documentation; if the provider does not publish exact output shapes, probe every documented aspect-ratio and resolution combination for every supported model and record the evidence. Prefer deterministic table tests for the full matrix, adding one representative VCR cassette only when the new model or wire behavior needs integration coverage rather than recording every image combination.
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.
- yesterday Changed · +12 lines · +17 tokens per session 8ed118581374
- 6d ago Changed · +30 lines 3fd7182fe8d2
- 10d ago First seen · 207 lines · 80 tokens per session scan A ecbacf6bb008
add-new-model is a skill published in the GitHub repository pydantic/pydantic-ai (19,824 stars, last pushed today), licensed MIT. It adds 97 tokens to every session and 7,679 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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