code-slob-cleanup
A coding agent skill that automatically identifies, refactors, and verifies Python code to remove technical debt ('code slob') introduced by
Open source Open in the app JSON README (API)
About
A coding agent skill that automatically identifies, refactors, and verifies Python code to remove technical debt ('code slob') introduced by AI agents or rapid development. Uses static analysis and Hypothesis property-based testing to safely apply and verify refactorings.
Details
- Kind
- Plugins
- Topic
- Developer tools
- Publisher
- jazz23
- Origin
- gemini
- Category
- ferramentas
- Version
- 1.0.0
- Stars
- 1
- Forks
- 1
- Last push
- 2026-04-15T16:20:02Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-30 14:13:39
- Updated
- 2026-08-30 14:13:39
- Origin id
jazz23/codeslobcleanup
README
# Overview "Code Slob" refers to the subtle technical debt, unnecessary verbosity, and over-complexity often introduced by AI coding agents or rapid development. While functional, "slob" code is harder to maintain and prone to future regressions. The **Code Slob Cleanup** project builds an automated toolchain—packaged as a coding agent skill—to identify, refactor, and rigorously verify Python code to remove this debt. Currently, the project only supports python codebases. # Installation Linux/Mac Auto Install/Uninstall/Update for Gemini/Claude/Codex: ```bash curl -LsSf https://raw.githubusercontent.com/Jazz23/CodeSlobCleanup/refs/heads/main/install.sh | sh ``` Gemini CLI: ```bash gemini extensions install https://github.com/Jazz23/CodeSlobCleanup ``` # How to use Click [here](https://jazz23.github.io/CodeSlobCleanup/) for a guide on how to use Code Slob Cleanup. # Core Objectives 1. **Automated Identification**: Detect refactoring targets using static analysis metrics (Cyclomatic Complexity, LoC, nesting depth) and semantic analysis via the agent to identify redundant logic. 2. **Safe Refactoring**: Employs your agent to perform code transformations such as function decomposition, visibility enforcement (Converting public to private), and dead code removal. 3. **Rigorous Verification**: Ensure behavioral equivalence using **Property-Based Testing**: Leveraging **Hypothesis** to verify `Original(input) == Transformed(input)` across thousands of inputs. 4. **Autonomous Self-Correction**: Integrate verification feedback directly into the refactoring loop, allowing the agent to "fix its own fixes" based on counter-examples found during testing. # Architecture The system follows an iterative loop: * **Scanner**: Traverses the codebase and finds code slob. * **Refactor Agent**: Analyzes flagged code and generates a cleaned-up version. * **Verifier**: An isolated orchestrator executes Hypothesis tests, and benchmarks performance. * **Feedback Loop**: If verification fails, the failing test cases are fed back to your agent for a retry. * **Committer**: Applies the change if verification passes and performance is not regressed. # Technologies * **Language**: Python 3.14+ * **Package Management**: `uv` (PEP 723 for standalone scripts) * **Testing**: Hypothesis (Property-based), Pytest * **Benchmarking**: Matplotlib/Numpy for performance comparison