Accelerated Code Dev & Review Agent
Inspired by Claude's growing footprint in GitHub commits, this challenge focuses on building an advanced agentic development environment. You will use Mastra AI to orchestrate a team of agents that automate parts of the software development lifecycle, from generating code snippets based on user stories to automated testing and code review. The system should integrate with a simulated codebase, providing intelligent suggestions and even committing code. Emphasis is placed on code quality, security, and developer productivity.
What you are building
The core problem, expected build, and operating context for this challenge.
Inspired by Claude's growing footprint in GitHub commits, this challenge focuses on building an advanced agentic development environment. You will use Mastra AI to orchestrate a team of agents that automate parts of the software development lifecycle, from generating code snippets based on user stories to automated testing and code review. The system should integrate with a simulated codebase, providing intelligent suggestions and even committing code. Emphasis is placed on code quality, security, and developer productivity.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
CodeSyntacticallyCorrect
Generated code is syntactically valid Python.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
TestsPass
Generated unit tests pass against the generated function.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
PEP8Compliance
Refactored code adheres to PEP8 guidelines.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
CodeQualityScore
Automated score based on linting, complexity, and docstrings. • target: 85 • range: 0-100
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
FeatureCompleteness
Percentage of described features correctly implemented. • target: 95 • range: 0-100
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master Mastra AI for defining agent roles, tools, and workflows, including its built-in memory and RAG capabilities for contextual code generation.
Integrate Claude Sonnet 4 for high-quality code generation and complex logical reasoning tasks, particularly for design patterns and architectural decisions.
Deploy Llama 3 8B Instruct via Hugging Face Inference Endpoints for highly optimized, specific code completion, syntax checking, and boilerplate generation.
Build custom tools within Mastra AI to interact with a mock Git repository and a simulated IDE (emulating Cursor's features) for reading, writing, and modifying code files.
Utilize Cohere's embedding models for semantic search over the codebase, enabling agents to quickly find relevant code examples, functions, or documentation for context.
Design an agent team where individual agents (e.g., 'Feature Developer Agent', 'Test Engineer Agent', 'Code Review Agent') collaborate using Mastra AI's messaging primitives.
Implement automated code quality checks and vulnerability scanning using a simulated or simplified code analysis tool.
How this agent runs
The system will be evaluated on its ability to correctly implement a simple feature, generate appropriate tests, and provide insightful code reviews, all while adhering to specified coding standards. Evaluation will i...
Challenge input
{'feature_description': 'string', 'language': 'string', 'test_framework': 'string'}
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{'function_code': 'string', 'test_code': 'string', 'review_summary': 'string'}
- Generated code is syntactically valid Python.
- Generated unit tests pass against the generated function.
- Refactored code adheres to PEP8 guidelines.
- CodeQualityScore target: 85
- 2 public reference cases
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- CodeSyntacticallyCorrect · Weight 1
- TestsPass · Weight 1
- PEP8Compliance · Weight 1
- CodeQualityScore · Weight 1
- FeatureCompleteness · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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