productize

productize is a command for Claude Code, Cursor from AlexFischman/mcp-skill-creator-agency. It costs 0 tokens per session (1,419 once invoked), scanned A, original, MIT.

A command for turning an AI agent into a reusable template that can be adapted for different clients. It uses onboarding fields so client-specific values can be supplied without rewriting the whole agent.

In plain words
What is it for?
Use it to inspect an existing agency, identify values that should be customizable, and add an onboarding form while preserving the current setup.
Why use it?
It removes repeated manual edits when the same agent needs different names, business details, models, output formats, or other settings.

Command for Claude CodeCursor

Written for Cursor and Claude Code: installed under .cursor/, but also a Claude Code command (commands/*.md).

Good fit Use it to inspect an existing agency, identify values that should be customizable, and add an onboarding form while preserving the current setup.

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Install with agentmods
npx agentmods add commands/alexfischman/mcp-skill-creator-agency/productize
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo
git clone --depth 1 https://github.com/AlexFischman/mcp-skill-creator-agency

Made for: Claude Code, Cursor.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for productize

README.md
[![agentmods](https://agentmods.dev/badge/commands/alexfischman/mcp-skill-creator-agency/productize/github.svg)](https://agentmods.dev/commands/alexfischman/mcp-skill-creator-agency/productize)
Your own site
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Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

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Your own site · 80×15
<a href="https://agentmods.dev/commands/alexfischman/mcp-skill-creator-agency/productize"><img src="https://agentmods.dev/badge/commands/alexfischman/mcp-skill-creator-agency/productize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,419 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.01419
Opus 5 $0.00000 $0.00709
Sonnet 5 $0.00000 $0.00284
Haiku 4.5 $0.00000 $0.00142

Measured 11d ago against content hash 9439fbf618ff, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

productize scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.cursor/commands/productize.md · 169 lines

How it starts

The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent Productization Task

Your task is to productize an AI agent in the current repository by turning it into a reusable template. Essentially, the user wants you to ensure that he can easily customize this agent for different clients.

Step-by-Step Process

  1. Fetch the documentation for how to create onboarding forms for AI agents with our framework: https://agency-swarm.ai/platform/marketplace/onboarding Make sure to fetch the entire page.
  2. Explore the current repository to understand the structure of the current agency to productize. Focus primarily on insturctions files of each agent and the shared_instructions.md file.
  3. Ask the user what he would like to customize accross different clients for this agent. For example, agent name, business overview, model, output format, additional notes, etc.
  4. Based on the user's answers, create a new OnboardingTool for an agent, as described in documentation. Example is provided below.
  5. Important: When converting the agent to use an OnboardingTool, make sure to preserve existing values from the repository.
    • For each value (for example, business overview or server addresses) that you want to turn into a customizable field, do NOT overwrite or delete the user’s current data.
    • Instead, create a field for this value in the OnboardingTool, and store the existing value as the default in onboarding_config.py.
    • Place code that loads these defaults inside the if __name__ == "__main__" block of your OnboardingTool script.
    • This approach keeps the user’s current settings safe while making them configurable for future onboarding.
  6. Run the onboarding_tool.py file to generate the onboarding_config.py file.
  7. Import the onboarding_config.py file to customize the agent, as described in documentation.

Example OnboardingTool

from agency_swarm.tools import BaseTool
from pydantic import Field
from typing import Optional, Literal
import os
import json

class OnboardingTool(BaseTool):
    """
    Customizes the agent based on business requirements and preferences.
    Add any fields you want the user to configure during onboarding.
    """

    # Basic text field example
    company_name: str = Field(
        "Acme Corp",
        description="Your company name"
    )

    # Textarea field example (for longer text inputs)
    company_overview: str = Field(
        "A company that does amazing things.",
        description="Brief overview of your company or product",
        json_schema_extra={"ui:widget": "textarea"},
    )

    model: Literal["gpt-5", "gpt-4.1"] = Field(
        "gpt-4.1",
        description="Select the model to use: gpt-5 or gpt-4.1"
    )

    # File upload field example
    knowledge_files: list[str] = Field(
        [],
        description="Upload documentation files for the agent to reference.",
        json_schema_extra={
            "x-file-upload-path": "./agent_name/files",  # Replace with your agent folder. FIles will be automatically uploaded to this path.
        },
    )

    # Add more fields as needed:
    # - Use str for text inputs
    # - Use Optional[str] for optional inputs
    # - Use list[str] for file uploads
    # - Add json_schema_extra={"ui:widget": "textarea"} for multi-line text
    # - Add json_schema_extra={"ui:placeholder": "placeholder"} for field placeholder text
    # - Add json_schema_extra={"x-file-upload-path": "./path"} for file uploads

    def run(self):
        """Saves configuration to onboarding_config.py"""
        # Get the directory where this tool is located
        tool_dir = os.path.dirname(os.path.abspath(__file__))
        config_path = os.path.join(tool_dir, "onboarding_config.py")

        # Convert tool fields to dictionary
        config = self.model_dump()

        # Convert to Python code format
        json_str = json.dumps(config, indent=4)
        json_str = json_str.replace(': null', ': None').replace(': true', ': True').replace(': false', ': False')
        python_code = f"# Auto-generated configuration\n\nconfig = {json_str}\n"

        # Write to file
        with open(config_path, "w", encoding="utf-8") as f:
            f.write(python_code)

        return f"Configuration saved to {config_path}"

# Test the tool
if __name__ == "__main__":
    tool = OnboardingTool(
        company_name="Test Company",
        company_overview="This is a test.",
        support_email="[email protected]"
    )
    print(tool.run())

Read the full file on GitHub · 169 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 169 lines · 0 tokens per session scan A 9439fbf618ff

Subscribe to this mod's changes

productize is a command published in the GitHub repository AlexFischman/mcp-skill-creator-agency (2 stars, last pushed 9mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,419 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.