TryCrucible — Prove your AI engineering skills

Scenario

You are given 40 broken Python functions with various bug categories (logic errors, off-by-one, type errors, edge cases). Build an agent that processes each function, identifies the root cause, produces a corrected version, and writes a pytest test that validates the fix. The agent itself may be implemented in Python, TypeScript, Go, or any other language.

Expected output

Fixed code passes all assertions, generated tests are syntactically valid and catch the original bug

Dataset

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Dataset files

Scoring rubric

Clarity5%

Agent output is structured and easy to inspect per function

LLM usage20%

Prompts guide the model to reason before coding, not just guess fixes

Robustness10%

Handles ambiguous bugs, functions with multiple issues, and unsolvable edge cases

Correctness25%

Fixed functions pass all ground truth test cases, generated tests catch the original bug

Architecture20%

Agent loop is well-structured, reasoning step is explicit before the fix

Language-free evaluation

Build your solution in any language or framework — Python, TypeScript, Go, Rust, Java, C#, or anything else. The dataset artifacts may be in one language; your implementation does not need to match. TryCrucible evaluates the behaviour of your system, the quality of your AI workflow, your verification strategy, and the reproducibility of your submission — not your language choice.

Submission requirements

A public GitHub repository link
A Dockerfile in the repo root — any language or framework; the evaluator builds and runs your container
Your solution reads test_inputs.json from the working directory and writes results.json — standard I/O contract across all challenges
A decisions.md — 3–5 sentences on the key architectural and AI-workflow choices you made
The system must be fully reproducible — we clone, build, and run it against real test inputs

Evaluation contract

When you submit, the evaluator runs these steps in order:

1Clone your public GitHub repository
2Build your container from the Dockerfile in the repo root
3Mount test_inputs.json into the working directory
4Run your solution in a network-isolated sandbox (5 min limit, 512 MB RAM)
5Read results.json from the working directory
6Score correctness against hidden ground truth, then score architecture, AI workflow, robustness, and clarity

// I/O contract — same schema for every challenge

Input (provided by evaluator)

// test_inputs.json
[
  { "id": "t1", "input": { ... } },
  { "id": "t2", "input": { ... } }
]

Output (written by your solution)

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Build a coding agent that fixes broken Python functions