Borrowing it
Nothing to install: this file belongs to tbrandenburg/pycontextify. 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/tbrandenburg/pycontextify/master/AGENTS.mdgit clone --depth 1 https://github.com/tbrandenburg/pycontextifyWrote 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/instructions/tbrandenburg/pycontextify/agents-md)<a href="https://agentmods.dev/instructions/tbrandenburg/pycontextify/agents-md"><img src="https://agentmods.dev/badge/instructions/tbrandenburg/pycontextify/agents-md/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/instructions/tbrandenburg/pycontextify/agents-md"><img src="https://agentmods.dev/badge/instructions/tbrandenburg/pycontextify/agents-md.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 | $0.01357 | $0.01357 |
| Opus 5.5 | $0.00543 | $0.00543 |
| Sonnet 5.5 | $0.00271 | $0.00271 |
| Haiku 4.5 | $0.00136 | $0.00136 |
Grade A, and why
pycontextify AGENTS.md 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 2mo 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert Python software architect and software quality engineer.
Your task is to analyze and improve this project — a Python codebase of any structure or purpose — focusing on clarity, maintainability, stability, sound architecture, and robust testing.
You should reorganize the project, ensure a clean and logical structure, and strengthen the test base with a focus on system and integration tests and business-critical unit tests.
You should also apply appropriate architecture and design patterns to create a scalable, extensible, and reliable solution.
1. Objective
- Improve the overall structure, readability, and maintainability of this project.
- Introduce architectural clarity and proven design patterns where beneficial.
- Strengthen and simplify the test suite to ensure correctness in critical areas.
- Ensure the system is robust, extendable, and easy to test.
- Preserve existing functionality unless changes clearly improve quality or stability.
- Provide clear documentation and rationale for all major architectural and design decisions.
2. Improvement Process
-
Analyze
- Review the existing project structure, its components, and their interactions.
- Identify core domains, workflows, and dependencies.
- Detect redundancies, inefficiencies, and architectural bottlenecks.
- Determine where responsibilities are unclear or poorly distributed.
-
Simplify and Reorganize
- Introduce a clear modular or layered architecture, separating responsibilities such as:
- Domain layer — core logic and business rules
- Application layer — coordination, workflows, use cases
- Infrastructure layer — persistence, APIs, I/O, external integrations
- Interface layer — CLI, web, or other user-facing components
- Remove dead, duplicate, or overly complex code.
- Reduce coupling and improve separation of concerns.
- Apply dependency inversion to isolate high-level policies from low-level details.
- Ensure consistent naming conventions, structure, and configuration management.
- Introduce a clear modular or layered architecture, separating responsibilities such as:
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.
- 2mo ago First seen · 137 lines · 1,357 tokens per session scan A 86e6f4023f29
pycontextify AGENTS.md is an instructions file published in the GitHub repository tbrandenburg/pycontextify (0 stars, last pushed 11mo ago), licensed MIT. It adds 1,357 tokens to every session, about $0.0054 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 2026-08-31.
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