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    Autonomous Agent Builder Skill

    1

    Generates structured Python or TypeScript autonomous agent projects with tool loops, memory, and safety gates.

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    Autonomous Agent Builder Skill

    Autonomous Agent Builder Skill

    Example session with this skill installed

    Build a Python-based research agent that uses a search tool to find news, summarizes findings, and saves them to a local JSON file. It needs a 5-iteration limit and human approval before saving.

    • Read your context and instructions
    • Compiled the autonomous agent builder

    I have architected a Python research agent using a tool-loop architecture with explicit state management and human-in-the-loop checkpoints.

    Research Agent Project Scaffold

    Project Structure

    research_agent/
├── src/
│   ├── agent/
│   │   └── researcher.py    # Main orchestration loop
│   ├── tools/
│   │   ├── search.py        # Web search adapter
│   │   └── file_ops.py      # Local JSON storage tool
│   ├── memory/
│   │   └── state.py         # Task state and iteration tracking
│   └── config/
│       └── settings.py      # Pydantic-based configuration
├── tests/
│   └── test_researcher.py   # Mock-based loop tests
├── .env.example
├── pyproject.toml
└── README.md

    Core Implementation (researcher.py)

    from typing import List, Dict
from src.tools.search import news_search
from src.tools.file_ops import save_json

class ResearchAgent:
    def __init__(self, max_iterations=5):
        self.max_iterations = max_iterations
        self.state = {"iterations": 0, "results": []}

    def run(self, query: str):
        while self.state["iterations"] < self.max_iterations:
            self.state["iterations"] += 1
            raw_data = news_search(query)
            summary = self.summarize(raw_data)
            self.state["results"].append(summary)
            
            # Human-in-the-loop Checkpoint
            if self.request_approval(summary):
                save_json(self.state["results"])
                break
        return "Task completed."

    def request_approval(self, summary: str) -> bool:
        print(f"Proposed Summary: {summary}")
        choice = input("Approve save? (y/n): ")
        return choice.lower() == 'y'

    Next steps

    • Populate the news_search tool adapter with your preferred API (e.g., Serper or Tavily).
    • Install dependencies using pip install . after updating pyproject.toml.
    • Run pytest tests/ to verify the iteration limit and approval logic via mocks.

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

    About this skill

    This skill solves the problem of high-friction agent development by converting raw product requirements into runnable, structured codebases. Most developers struggle to move from a single prompt to a maintainable system with tool loops, memory persistence, and error handling. This skill automates the creation of that scaffold in Python or TypeScript.

    What it does

    • Architects Agent Loops by designing single-agent or planner/executor flows based on task complexity.
    • Generates Typed Tool Systems including input/output schemas, validation, and side-effect classifications.
    • Configures Memory Architectures for conversation history, task state, and knowledge retrieval.
    • Enforces Safety & Reliability with loop limits, human-in-the-loop checkpoints, and budget controls.
    • Produces Full Test Suites covering planning logic, tool failures, and state persistence.

    How it works

    1. Requirements Discovery: Provide your agent's purpose, tools, and interaction mode.
    2. Architecture Selection: The skill selects a workflow (e.g., state machine vs. tool loop) and defines data flows.
    3. Project Scaffolding: It generates the folder structure, dependency files, and environment templates.
    4. Code Implementation: It writes the orchestration logic, tool adapters, and memory interfaces.

    Frameworks & tools

    Supports Python and TypeScript projects. Generates configuration for environment variables, pyproject.toml or package.json, and standard testing frameworks like pytest or jest.

    Why this beats prompting it yourself

    Generic prompts yield single-file scripts that break when complexity grows. This skill generates a modular, enterprise-grade directory structure with explicit interfaces for models and tools, ensuring the agent remains maintainable and testable.

    Use cases

    • Building a research agent that requires durable task state and tool usage.
    • Scaffolding a coding assistant with human-in-the-loop approval gates.
    • Creating a customer support agent with tenant-isolated conversation memory.
    • Designing a multi-stage workflow agent for data extraction and validation.

    Known limitations

    Does not provide live API keys or credentials. Generated code requires a local environment and model provider access to run. Logic is a starting implementation that must be verified in production.

    Requires local environment for execution. No real credentials or API keys provided. Logic is a scaffold, not a production-ready deployment.

    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 any payment.

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    Trust & safety

    Security scan

    Security scan passed 8 days ago

    Software
    Python

    Permissions

    No special permissions declared or detected

    Listed8 days ago

    What's inside

    Frequently Asked Questions

    This skill generates a complete, production-ready codebase including directory structures, dependency management files, tool schemas, and orchestration logic, whereas standard prompts typically only yield single-file scripts that lack modularity.

    Yes, you can choose between a Python (pyproject.toml) or TypeScript (package.json) environment, and the skill will generate the appropriate typed interfaces and testing frameworks for your chosen language.

    The skill allows you to define 'Safety Gates' which include hard-coded loop limits, budget constraints, and human-in-the-loop checkpoints to prevent autonomous agents from running indefinitely or incurring high costs.

    The output is a complete scaffold that includes your selected memory architecture (like conversation history or task state), tool adapters, and a full test suite, though you will need to provide your own API keys to run the finished project.

    While it generates the necessary adapters and interfaces for LLMs, it does not include live API credentials or model hosting; you must provide your own environment variables for providers like OpenAI or Anthropic.

    New in Agents & Orchestration