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    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+22 more

    Lean Context

    1

    Cut Claude Code and Cursor token costs by 30-50% with AST-aware context compression, skeleton extraction, and budget guards.

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    lean-context

    lean-context

    Example session with this skill installed

    I need to understand the structure of the core engine in this repo, but the files are huge. Help me map it out without hitting context limits.

    • Read your context and instructions
    • Compiled the lean-context

    I have scanned the repository and generated a compressed map of the core engine. I filtered out all build artifacts and pruned Python files to their signatures to stay within your token budget.

    Repository Context Summary

    File PathOriginal (Tokens)Compressed (Tokens)% Saved
    engine/core.py1,84042077%
    engine/utils/math.py95018081%
    engine/handlers/events.py1,20031074%
    Total3,99091077%
    
    # engine/core.py (Compressed AST View)
    
    class ExecutionEngine:
        """Main orchestrator for task execution."""
        
        def __init__(self, config: dict): ...
        
        def register_handler(self, name: str, handler: Callable):
            """Registers a new event handler to the engine."""
            ...
    
        def run_sync(self, task_id: str) -> Result:
            """Executes a task synchronously and returns the Result object."""
            ...
    
    class Result:
        def __init__(self, status: str, data: Any): ...
    
    .
    └── engine/
        ├── __init__.py
        ├── core.py
        ├── handlers/
        │   ├── __init__.py
        │   └── events.py
        └── utils/
            ├── __init__.py
            └── math.py
    

    Next steps

    1. Would you like me to expand the full body of a specific method in core.py?
    2. I can perform a lean-context diff if you have pending changes to these files.
    3. Type show me the structure at any time to refresh this map.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    The problem Large Python codebases quickly exhaust LLM context windows. Manually stripping boilerplate to save tokens is slow and error-prone.

    What it does

    • AST-aware file summaries (signatures only)
    • Low-token directory tree skeletons
    • Git diff summarization for code review
    • 3,000-token HITL budget per cycle
    • Zero external dependencies (Python 3.8+ stdlib only)

    Why it beats prompting yourself Generic prompts miss edge cases: large files, nested directories, build artifacts. lean-context ships dedicated scripts with budget enforcement.

    Use cases

    • Inspect or refactor a 2,000-line file without blowing context
    • Map repo structure before deep analysis
    • Review PR diffs without dumping full files
    • Stay under token budget on long sessions

    Limitations v1.0 is Python only. TypeScript support arrives in v1.1.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Trust & safety

    Security scanned

    Verified clean 11 days ago

    • Passed all security checks, Safe to install

    Requires

    Python

    Listed1 month ago
    Updated11 days ago

    What's inside

    Frequently Asked Questions