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/.claude/skills/testing-skill/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/testing-skill)<a href="https://agentmods.dev/skills/pydantic/pydantic-ai/testing-skill"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/testing-skill/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/testing-skill"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/testing-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 7 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 4 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 13 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 28 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 33 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 40 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 84 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 87 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.
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 | $0.00044 | $0.00854 |
| Opus 5.5 | $0.00018 | $0.00342 |
| Sonnet 5.5 | $0.00009 | $0.00171 |
| Haiku 4.5 | $0.00004 | $0.00085 |
Grade A, and why
testing-skill 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 1mo 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pytest VCR Workflow
Use this skill when recording or re-recording VCR cassettes for tests, or when debugging cassette contents.
Prerequisites
- Verify
.envexists:test -f .env && echo 'ok' || echo 'missing' - Missing API keys will cause clear test errors at runtime
Important flags
--record-mode=rewrite: Record cassettes (works for both new and existing)--lf: Run only the last failed tests-vv: Verbose output--tb=line: Short traceback output-k="": Run tests matching the given substring expression
Recording Cassettes
Step 1: Record cassettes
source .env && uv run pytest path/to/test.py::test_function_name -v --tb=line --record-mode=rewrite
Multiple tests can be specified:
source .env && uv run pytest path/to/test.py::test_one path/to/test.py::test_two -v --tb=line --record-mode=rewrite
Step 2: Verify recordings
Run the same tests WITHOUT --record-mode to verify cassettes play back correctly:
source .env && uv run pytest path/to/test.py::test_function_name -vv --tb=line
Step 3: Review snapshots
If tests use snapshot() assertions:
- The test run in Step 2 auto-fills snapshot content
- Review the generated snapshot files to ensure they match expected output
- You only review - don't manually write snapshot contents
- Snapshots capture what the test actually produced, additional to explicit assertions
Parsing Cassettes
Parse VCR cassette YAML files to inspect request/response bodies without dealing with raw YAML.
Usage
uv run python .claude/skills/testing-skill/parse_cassette.py <cassette_path> [--interaction N]
Examples
# Parse all interactions in a cassette
uv run python .claude/skills/testing-skill/parse_cassette.py tests/models/cassettes/test_foo/test_bar.yaml
# Parse only interaction 1 (0-indexed)
uv run python .claude/skills/testing-skill/parse_cassette.py tests/models/cassettes/test_foo/test_bar.yaml --interaction 1
What ships with it
1 file 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.
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.
- 1mo ago First seen · 98 lines · 44 tokens per session scan A 4f0663ca5bc5
testing-skill is a skill published in the GitHub repository pydantic/pydantic-ai (20,771 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 854 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 2026-08-30.
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systematic-debugging
Systematic approach to diagnosing and fixing errors.
verification-strategy
Thorough verification of completed work before declaring done.
test-generator
Generate pytest test cases for Python functions and classes.