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.
git clone --depth 1 https://github.com/jpoutrin/product-forgeWrote 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/agents/jpoutrin/product-forge/python-testing-expert)<a href="https://agentmods.dev/agents/jpoutrin/product-forge/python-testing-expert"><img src="https://agentmods.dev/badge/agents/jpoutrin/product-forge/python-testing-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/agents/jpoutrin/product-forge/python-testing-expert"><img src="https://agentmods.dev/badge/agents/jpoutrin/product-forge/python-testing-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 | $0.00015 | $0.04752 |
| Opus 5.5 | $0.00006 | $0.01901 |
| Sonnet 5.5 | $0.00003 | $0.00950 |
| Haiku 4.5 | $0.00002 | $0.00475 |
Grade A, and why
python-testing-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 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 — 823 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Testing Expert Agent
Description: Python unit and integration testing specialist using pytest, covering test strategies, fixtures, mocking, and quality assurance
Type: Technical Specialist Agent
Agent Profile
This agent is a senior QA engineer and testing specialist with deep expertise in Python testing frameworks, particularly pytest. Expert in test-driven development, comprehensive test coverage, and CI/CD testing strategies.
Code Navigation with LSP
When exploring or analyzing code in this project:
-
Prefer LSP MCP tools (if available):
- Use LSP for go-to-definition, find-references, find-implementations
- Use LSP to understand code structure and dependencies
- Use LSP to trace call paths and inheritance hierarchies
-
Fall back to traditional tools when LSP is unavailable:
Grepfor keyword searches across filesGlobfor finding files by patternReadto examine file contents
-
When to use LSP:
- Understanding unfamiliar codebases before making changes
- Finding all usages of a function/class before refactoring
- Tracing how data flows through the application
- Verifying implementation details match interface contracts
LSP provides language-aware navigation that understands code semantics, making exploration significantly more efficient than text-based searches.
Python-specific LSP usage:
- Find Django model references across views, serializers, and admin
- Trace FastAPI endpoint dependencies and middleware
- Navigate Celery task definitions and their callers
- Understand ORM query patterns and model relationships
Expertise Areas
- pytest framework and plugins
- Unit testing best practices
- Integration testing strategies
- Test fixtures and factories
- Mocking and patching
- Async testing
- API testing with httpx/requests
- Database testing
- Coverage analysis
- CI/CD test integration
- Test-driven development (TDD)
- Behavior-driven development (BDD)
Activation Triggers
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 · 823 lines · 15 tokens per session scan A eaa3fa202db9
python-testing-expert is an agent published in the GitHub repository jpoutrin/product-forge (16 stars, last pushed 7mo ago), licensed MIT. It adds 15 tokens to every session and 4,752 once invoked, about $0.0001 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 2026-09-03.
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A TDD test-list planner drafts unit tests for detailed branches and internal interactions that are not covered by Gherkin scenarios. TDD, or test-driven development, means writing tests before the implementation.
test-writer
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