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    Python FileTreeArchitect — Static AST Monolith Decomposer

    1

    Decomposes oversized Python monoliths into dependency-aware, modular domain directory architectures.

    $19.99

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    Python FileTreeArchitect — Static AST Monolith Decomposer

    Python FileTreeArchitect — Static AST Monolith Decomposer

    Example session with this skill installed

    Monolith project payload input target requiring architectural splitting

    import re

    BOT_TOKEN = "secret-token"
    PRIMARY_HOST = "https://api.com"

    class User:
    def __init__(self, uid): self.uid = uid

    def is_valid_email(email): return bool(re.match(r"[^@]+@[^@]+.[^@]+", email))

    def db_connect(): return "sqlite:///:memory:"
def handle_start(user_id): return f"Hello {user_id}"

    2000+ lines of identical continuous execution tracking structures omitted for sample clarity...

    • Read your context and instructions
    • Compiled the python filetreearchitect
    {
  "audit_status": "MONOLITH_DETECTED",
  "inventory": {
    "functions": 6,
    "classes": 1,
    "imports": 1,
    "side_effects": "global initialization found"
  },
  "target_tree": {
    "bot": {
      "utils": ["validators.py"],
      "models": ["user.py"],
      "config.py": "settings layer",
      "main.py": "entry point"
    }
  },
  "extraction_order": [
    {
      "step": 1,
      "target_file": "bot/utils/validators.py",

      "action": "Extract pure utility functions",
  "items_to_move": ["is_valid_email"],
  "agent_prompt": "File: bot/utils/validators.py\nMove: is_valid_email\nImports: re\nInstruction: Extract target functions, ensure application remains runnable."

        }
  ],
  "metadata": {
    "version": "1.0.0",
    "timestamp": "2026-10-15T16:00:00Z",
    "monolith_line_count": 2147,
    "framework_detected": "python-standard"
  }
}

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    About this skill

    Stop tolerating structural code degradation, severe context degradation, and unhandled regression loops caused by oversized Python application monoliths. Python FileTreeArchitect is a static AST-driven architectural engine that deep-scans single-file codebases to programmatically generate isolated, dependency-aware modular blueprints and safely distribute refactoring workloads to AI coding agents.

    Large-scale files exceeding 1000 lines of execution logic inevitably trigger severe LLM hallucinations, resulting in dropped functions, duplicated helper strings, and fractured module scopes. This engine acts as a strict, read-only architectural orchestrator that maps full application internal graphs within local sandboxed memory blocks.

    What it does

    • AST Dependency Ledger Ingestion: Traverses oversized Python files utilizing Abstract Syntax Tree (AST) call-graphs to index functions, class scopes, external package statements, and module-level side effects.
    • Domain-Driven Directory Isolation: Groups chaotic legacy structures into strict, formalized Python module patterns (config.py, db.py, models, services, handlers) driven exclusively by clean architectural contracts.
    • Sequenced Extraction Serialization: Generates a low-risk, step-by-step modular migration roadmap engineered to keep the application fully testable and runnable at every single stage of execution.
    • Context-Isolated Agent Handoffs: Synthesizes dedicated, highly-focused micro-prompts for each targeted module, enabling local AI workflows to execute refactoring iterations in small, hallucination-free context segments.
    • Machine-Readable Telemetry: Outputs a strict, schema-validated JSON compilation contract optimized for local Git operations, architectural gates, or enterprise CI/CD verification pipelines.

    Detected Structural Components

    1. PURE_UTILITIES: Independent, side-effect-free helper functions containing zero internal domain dependencies, isolated for priority extraction stages.
    2. CONFIGURATION_METADATA: Environment variable access hooks and global app initialization variables isolated to build an independent configuration layer.
    3. DOMAIN_ENTITIES: System classes, schemas, and processing methods mapped to initialize modular database layer models.
    4. OPERATIONAL_SERVICES: Business logic functions that consume model endpoints and call active database session factories.
    5. BOUNDARY_HANDLERS: External protocol layer interfaces, routers, and bot callbacks mapped as high-level entry points.

    When to use

    • Modularizing oversized single-file API frameworks or messaging systems (FastAPI, aiogram, Django, Flask) exceeding 1000 lines of source code.
    • Restructuring legacy Python bot codebases before scaling teams or introducing automated coding workflows.
    • Establishing pre-refactoring guardrails inside Cursor or Claude Code to eliminate contextual memory drift during complex engineering sprints.

    When NOT to use

    • Codebases outside the native Python runtime ecosystem (e.g., Node.js, JavaScript, TypeScript, Go).
    • Processing lightweight script components, local text-parsing utilities, or tools under 500 total lines of code volume.
    • Performing active runtime code mutations or direct remote repository modifications (this engine runs strictly in a passive, read-only orientation).

    Under the Hood & Security

    • 100% Local & Air-Gapped: Operates entirely inside offline memory buffers (file_system_write: false). Absolute zero external cloud queries ensure proprietary architecture layers, sensitive endpoints, and corporate logic strings never leak.
    • Contract-Tested Stability: Backed by a rigorous 12-point testing harness validating schema compliance boundaries, edge cases, and absolute zero-hallucination execution.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse All Claude Skills.

    Static validation constraints only. The engine optimizes, groups, and maps existing architectural layouts inside memory buffers without executing application packages, running dynamic integration checks, or directly generating refactored source files. Designed exclusively for high-density codebases; files containing fewer than 500 lines of functional Python logic are automatically bypassed by the parser logic. Focuses entirely on clean domain-based physical splitting without modifying or re-architecting the core business logic loops.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before buying a skill.

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    Software
    Python

    Permissions

    Read Files

    Allowed Hosts

    docs.python.org

    File Scopes

    **/*.py
    **/*.js

    Read-only sandbox access to Python application files and script scopes. Evaluates repository layouts entirely offline within localized memory allocations, with absolute zero permission for shell routing, outbound telemetry, or directory mutations.

    Listed1 month ago
    Updated29 days ago

    What's inside

    Frequently Asked Questions

    The engine systematically mitigates the LLM context degradation limit. When a Python file expands, automated models incur token tracking loss, causing dropped code layers and code repetition loops. The skill maps the internal AST dependency graph to formulate an isolated, step-by-step module migration sequence.

    Fully compatible with Claude Code, Cursor, GitHub Copilot, and open-source agent execution environments supporting standard Markdown parsing. The package delivers context-isolated micro-prompts designed to keep automated local sessions confined to a single Python file schema at a time.

    The deployment package delivers the core deterministic AST decomposition engine, rigid domain-driven structural validation matrices, and a contract-tested JSON telemetry output template optimized for immediate local git integration and AI repository orchestration.

    No. The engine generates a highly prioritized, incremental migration sequence that preserves runtime application stability at every checkpoint. By isolating pure, dependency-free utility components first and locking the primary application entry point for the absolute final step, code execution paths remain unbroken.

    Exclusively engineered and optimized for Python runtime architectures (Python 3.10+ up to 3.14). The internal AST parsing modules are specifically tuned to evaluate and split complex application repositories built on aiogram, FastAPI, Django, and Flask.

    Yes. All enterprise subscriptions include continuous lifecycle updates targeting the core decomposition engine. As local AI token boundaries evolve and new native Python specifications are released, the underlying AST parsing logic and context prompt schemas are programmatically optimized to maintain complete compatibility.