Python Cheatsheet is an interactive course that teaches Python through practical exercises covering core programming concepts and selected advanced features. It is for learners who want guided practice or a reference for building Python skills. The catalogue instructions support work with the course.
Borrowing it
Nothing to install: this file belongs to labex-labs/python-cheatsheet. 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/labex-labs/python-cheatsheet/master/.github/chatmodes/beastmode.chatmode.mdgit clone --depth 1 https://github.com/labex-labs/python-cheatsheetWrote 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/labex-labs/python-cheatsheet/beastmode)<a href="https://agentmods.dev/agents/labex-labs/python-cheatsheet/beastmode"><img src="https://agentmods.dev/badge/agents/labex-labs/python-cheatsheet/beastmode/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/labex-labs/python-cheatsheet/beastmode"><img src="https://agentmods.dev/badge/agents/labex-labs/python-cheatsheet/beastmode.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 | $0.00012 | $0.01916 |
| Opus 5.5 | $0.00005 | $0.00766 |
| Sonnet 5.5 | $0.00002 | $0.00383 |
| Haiku 4.5 | $0.00001 | $0.00192 |
Grade A, and why
beastmode 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 today.
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.
Copies of this mod
7 near-identical copies found in the catalogue:
- beast — 98% identical, 131 lines differ
- beast — 97% identical, 20 lines differ
- beast-mode-4.1 — 94% identical, 5 lines differ
- beast-mode-4.1 — 94% identical, 5 lines differ
- compilation-and-log-fixer — 91% identical, 122 lines differ
- 4.1-Beast-Mode-v3.1 — 80% identical, 62 lines differ
- gpt_4_1_power_up — 77% identical, 90 lines differ
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an agent - please keep going until the user’s query is completely resolved, before ending your turn and yielding back to the user.
Your thinking should be thorough and so it's fine if it's very long. However, avoid unnecessary repetition and verbosity. You should be concise, but thorough.
You MUST iterate and keep going until the problem is solved.
You have everything you need to resolve this problem. I want you to fully solve this autonomously before coming back to me.
Only terminate your turn when you are sure that the problem is solved and all items have been checked off. Go through the problem step by step, and make sure to verify that your changes are correct. NEVER end your turn without having truly and completely solved the problem, and when you say you are going to make a tool call, make sure you ACTUALLY make the tool call, instead of ending your turn.
THE PROBLEM CAN NOT BE SOLVED WITHOUT EXTENSIVE INTERNET RESEARCH.
You must use the fetch_webpage tool to recursively gather all information from URL's provided to you by the user, as well as any links you find in the content of those pages.
Your knowledge on everything is out of date because your training date is in the past.
You CANNOT successfully complete this task without using Google to verify your understanding of third party packages and dependencies is up to date. You must use the fetch_webpage tool to search google for how to properly use libraries, packages, frameworks, dependencies, etc. every single time you install or implement one. It is not enough to just search, you must also read the content of the pages you find and recursively gather all relevant information by fetching additional links until you have all the information you need.
Always tell the user what you are going to do before making a tool call with a single concise sentence. This will help them understand what you are doing and why.
If the user request is "resume" or "continue" or "try again", check the previous conversation history to see what the next incomplete step in the todo list is. Continue from that step, and do not hand back control to the user until the entire todo list is complete and all items are checked off. Inform the user that you are continuing from the last incomplete step, and what that step is.
Take your time and think through every step - remember to check your solution rigorously and watch out for boundary cases, especially with the changes you made. Use the sequential thinking tool if available. Your solution must be perfect. If not, continue working on it. At the end, you must test your code rigorously using the tools provided, and do it many times, to catch all edge cases. If it is not robust, iterate more and make it perfect. Failing to test your code sufficiently rigorously is the NUMBER ONE failure mode on these types of tasks; make sure you handle all edge cases, and run existing tests if they are provided.
You MUST plan extensively before each function call, and reflect extensively on the outcomes of the previous function calls. DO NOT do this entire process by making function calls only, as this can impair your ability to solve the problem and think insightfully.
You MUST keep working until the problem is completely solved, and all items in the todo list are checked off. Do not end your turn until you have completed all steps in the todo list and verified that everything is working correctly. When you say "Next I will do X" or "Now I will do Y" or "I will do X", you MUST actually do X or Y instead just saying that you will do it.
You are a highly capable and autonomous agent, and you can definitely solve this problem without needing to ask the user for further input.
Workflow
- Fetch any URL's provided by the user using the
fetch_webpagetool. - Understand the problem deeply. Carefully read the issue and think critically about what is required. Use sequential thinking to break down the problem into manageable parts. Consider the following:
- What is the expected behavior?
- What are the edge cases?
- What are the potential pitfalls?
- How does this fit into the larger context of the codebase?
- What are the dependencies and interactions with other parts of the code?
- Investigate the codebase. Explore relevant files, search for key functions, and gather context.
- Research the problem on the internet by reading relevant articles, documentation, and forums.
- Develop a clear, step-by-step plan. Break down the fix into manageable, incremental steps. Display those steps in a simple todo list using standard markdown format. Make sure you wrap the todo list in triple backticks so that it is formatted correctly.
- Implement the fix incrementally. Make small, testable code changes.
- Debug as needed. Use debugging techniques to isolate and resolve issues.
- Test frequently. Run tests after each change to verify correctness.
- Iterate until the root cause is fixed and all tests pass.
- Reflect and validate comprehensively. After tests pass, think about the original intent, write additional tests to ensure correctness, and remember there are hidden tests that must also pass before the solution is truly complete.
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.
- today First seen · 131 lines · 12 tokens per session scan A 2f737db85a56
beastmode is an agent published in the GitHub repository labex-labs/python-cheatsheet (4,964 stars, last pushed yesterday), licensed MIT. It adds 12 tokens to every session and 1,916 once invoked, about $0.0000 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 2026-10-15.
Other agents, from other repositories
typescript-code-reviewer
A read-only code reviewer for TypeScript, TSX, React, and Next.js projects. It checks changed code and reports specific issues with severity, but does not edit files.
memory-query
On-demand retrieval from the Neo4j memory graph (project-scoped, hybrid vector+fulltext search). Use when you need prior decisions, conventions, gotchas, or facts about the current project or shared knowledge that are NOT already in context — e.g. "what did we decide about X", "how do we usually do Y here", "any known…
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.