pydantic
Build Reliable Pydantic v2 Models
Unvalidated Python data can cause runtime failures and inconsistent API behavior. This skill guides Pydantic v2 model design, validation, configuration, and serialization.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "pydantic" from https://skillstore.io/skills/bossjones-pydantic.md and its manifest at https://skillstore.io/api/skills/bossjones-pydantic/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "pydantic". Create a customer signup model with a positive identifier, validated email, optional signup date, and active status.
Expected outcome:
- A Customer model uses constrained fields and a validated email type.
- The signup date accepts a compatible date-time value or remains empty.
- Invalid identifiers and email addresses produce clear validation errors.
Using "pydantic". Protect credentials when serializing a user model.
Expected outcome:
The design stores the password as a secret value, excludes it from exported data, and documents the chosen serialization options.
Using "pydantic". Validate a list of integer identifiers without creating a full model.
Expected outcome:
A TypeAdapter validates the list, converts compatible string values, and reports invalid elements with their locations.
Security Audit
SafeAll 29 external-command alerts are false positives caused by Markdown fences and inline examples; SKILL.md contains no command execution mechanism. The reconnaissance alert is a false positive on a validation error message. No prompt injection, exfiltration intent, or other semantic security risk was found.
Risk Factors
⚙️ External commands (29)
Share & cite this report
Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.
Copy report link
https://skillstore.io/skills/bossjones-pydantic/audits/8?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
[](https://skillstore.io/skills/bossjones-pydantic?utm_source=security_passport_badge)HTML badge
<a href="https://skillstore.io/skills/bossjones-pydantic?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/bossjones-pydantic/security.svg" alt="Skillstore security assessment" loading="lazy"></a>Embed card
<iframe src="https://skillstore.io/embed/skills/bossjones-pydantic.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
bossjones. (2026). pydantic security audit report (audit version 8) [Author version unspecified]. Skillstore. https://skillstore.io/skills/bossjones-pydantic/audits/8BibTeX citation
@techreport{bossjones-bossjones-pydantic-2026,
author = {bossjones},
title = {pydantic security audit report (audit version 8)},
institution = {Skillstore},
year = {2026},
number = {8},
url = {https://skillstore.io/skills/bossjones-pydantic/audits/8},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "pydantic security audit report (audit version 8)"
version: "unspecified"
type: report
authors:
- name: "bossjones"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/bossjones-pydantic/audits/8"
identifiers:
- type: other
value: "skillstore:bossjones-pydantic:audit:8"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Validate API payloads
Define request and response models with constraints, aliases, nested objects, and controlled serialization.
Normalize pipeline records
Validate dictionaries, coerce compatible values, and report structured errors before downstream processing.
Modernize validation models
Apply Pydantic v2 configuration, validators, serializers, and TypeAdapter patterns to an existing Python library.
Try These Prompts
Create a Pydantic v2 model for [entity] with these fields: [fields]. Include sensible defaults, constraints, and one valid input example.
Improve this Pydantic v2 model for [requirements]. Add field constraints, custom validators, clear errors, and handling for extra fields.
Design serialization for [model]. Exclude sensitive fields, apply aliases, format [special values], and explain the selected model_dump options.
Design a Pydantic v2 validation layer for [workflow]. Use nested models, strict configuration, reusable validators, TypeAdapter, and explicit serialization rules.
Best Practices
- Use specific field types and constraints so invalid data fails at the model boundary.
- Choose ConfigDict behavior explicitly for coercion, extra fields, assignment, and aliases.
- Test valid inputs, invalid inputs, error locations, and serialized output for every important model.
Avoid
- Do not rely on broad primitive types when the domain has stricter limits.
- Do not expose secret or internal fields through default serialization.
- Do not place unrelated side effects inside validators or serializers.