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.
npx agentmods add instructions/haozhang615/azure-mcp-server-accelerator/pythongit clone --depth 1 https://github.com/HaoZhang615/Azure-MCP-Server-AcceleratorWhat 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 | $0.00428 | $0.00428 |
| Opus 5 | $0.00214 | $0.00214 |
| Sonnet 5 | $0.00086 | $0.00086 |
| Haiku 4.5 | $0.00043 | $0.00043 |
Grade A, and why
Azure-MCP-Server-Accelerator python.instructions.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 2d 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.
This is a copy
100% identical to Python Coding Conventions — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Coding Conventions
Python Instructions
- Write clear and concise comments for each function.
- Ensure functions have descriptive names and include type hints.
- Provide docstrings following PEP 257 conventions.
- Use the
typingmodule for type annotations (e.g.,List[str],Dict[str, int]). - Break down complex functions into smaller, more manageable functions.
General Instructions
- Always prioritize readability and clarity.
- For algorithm-related code, include explanations of the approach used.
- Write code with good maintainability practices, including comments on why certain design decisions were made.
- Handle edge cases and write clear exception handling.
- For libraries or external dependencies, mention their usage and purpose in comments.
- Use consistent naming conventions and follow language-specific best practices.
- Write concise, efficient, and idiomatic code that is also easily understandable.
Code Style and Formatting
- Follow the PEP 8 style guide for Python.
- Maintain proper indentation (use 4 spaces for each level of indentation).
- Ensure lines do not exceed 79 characters.
- Place function and class docstrings immediately after the
deforclasskeyword. - Use blank lines to separate functions, classes, and code blocks where appropriate.
Edge Cases and Testing
- Always include test cases for critical paths of the application.
- Account for common edge cases like empty inputs, invalid data types, and large datasets.
- Include comments for edge cases and the expected behavior in those cases.
- Write unit tests for functions and document them with docstrings explaining the test cases.
Example of Proper Documentation
def calculate_area(radius: float) -> float:
"""
Calculate the area of a circle given the radius.
Parameters:
radius (float): The radius of the circle.
Returns:
float: The area of the circle, calculated as π * radius^2.
"""
import math
return math.pi * radius ** 2
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.
- 2d ago First seen · 57 lines · 428 tokens per session scan A d649c2f3679b
Azure-MCP-Server-Accelerator python.instructions.md is an instructions file published in the GitHub repository HaoZhang615/Azure-MCP-Server-Accelerator (2 stars, last pushed 11mo ago), licensed MIT. It adds 428 tokens to every session, about $0.0021 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to Python Coding Conventions, differing in 8 lines, and is treated as a copy.
Other instructions, from other repositories
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Instructions for platformplatform/PlatformPlatform, covering behavioral guidelines, build, test, and format, product management tool, auto memory and source of truth.
apex-accelerator azure-artifacts.instructions.md
Template compliance rules for artifact generation.
apex-accelerator copilot-instructions.md
Instructions for jonathan-vella/apex-accelerator, covering apex - copilot instructions, azure defaults (canonical), default regions, required tags (azure policy enforced) and security baseline + avm mandate.
apex-accelerator agent-skills.instructions.md
Guidelines for creating high-quality Agent Skills for GitHub Copilot.
apex-accelerator context-optimization.instructions.md
Context window optimization rules for agent definitions, skills, and instruction files.
apex-accelerator no-interactive-shell.instructions.md
Prevents interactive shell prompts and long-output terminal replays from being injected into chat. Forbids -i flags on mv/rm/cp, read -p, and confirm prompts (incl. inside bash -c '...'). Pipe long output to files. Scoped to chat-context-loaded files; skill references/ and templates/ are exempt because they hold…