workany

🥈Silver

WorkAny is a desktop AI agent that automates tasks via natural language. It generates code, executes tools, and manages workspaces. Ideal for operations teams to streamline repetitive tasks and improve productivity.

1,4391610Updated Today

Intermediate30min to implementautomation

Saves ~120 min per use

Quick InstallView Source

git clone https://github.com/workany-ai/workany.git

Works with:

Claude

Overview

About This Skill

The workany Claude Code skill is a versatile desktop agent designed to automate a wide range of tasks, making it an essential tool for developers, product managers, and AI practitioners. With an intermediate implementation difficulty, users can set it up in approximately 30 minutes. This skill allows users to instruct the agent to perform tasks such as organizing files, generating website templates, and creating formatted documents based on specific criteria. By leveraging workany, teams can streamline their workflow automation processes and enhance productivity. One of the key benefits of workany is its ability to save time on repetitive tasks. For instance, users can automate the categorization and sorting of documents, which can significantly reduce manual effort. Additionally, the skill can quickly produce data tables or spreadsheets from raw data inputs, allowing teams to focus on analysis rather than data entry. This capability is particularly valuable for professionals who deal with large volumes of information and require efficient methods to manage it. The target personas for workany include developers and product managers who are looking to optimize their workflow automation. The skill is also suitable for AI practitioners who want to integrate AI agents into their daily operations. With existing use cases such as generating presentation slides tailored to specific topics, workany demonstrates its practical application across various scenarios. The skill's medium GTM relevance indicates that it can be effectively utilized in diverse environments, making it a flexible addition to any tech stack. While the time savings for workany are currently unknown, the potential for increased efficiency is clear. As an intermediate-level skill, it requires some familiarity with automation tools but offers substantial value once implemented. By integrating workany into AI-first workflows, teams can harness the power of AI automation to enhance their operational efficiency and focus on more strategic initiatives. For those looking to elevate their automation capabilities, workany represents a compelling solution.

How to Use

[{"step":"Define the task in detail. Include specific inputs, outputs, and any constraints (e.g., file formats, tools to use).","tip":"Be as specific as possible. For example, instead of 'clean data,' say 'remove rows with missing values in the 'email' column and standardize phone numbers to (XXX) XXX-XXXX format.'"},{"step":"Open WorkAny and paste the task description into the chat interface. Use the prompt template to structure your request.","tip":"If WorkAny supports voice commands, you can describe the task verbally. Ensure your microphone is clear and background noise is minimal."},{"step":"Review the generated plan. WorkAny will outline the steps it intends to take. Confirm or adjust the plan as needed.","tip":"Pay attention to the tools WorkAny plans to use (e.g., Excel, Python, PowerShell). Ensure these tools are installed and accessible on your system."},{"step":"Execute the task. WorkAny will run the automation, providing real-time updates on progress. Monitor the output for errors or warnings.","tip":"If an error occurs, WorkAny will pause and ask for clarification. Provide additional context or correct the issue immediately to avoid delays."},{"step":"Verify the results. Check the output files or logs to ensure the task was completed correctly. Provide feedback to WorkAny to improve future automations.","tip":"Save the generated scripts or automation plans for future use. WorkAny can re-run or modify these scripts with new inputs."}]

Use Cases

Automate file organization by instructing the agent to categorize and sort documents.

Generate website templates or complete web applications with simple commands.

Create and format documents or reports based on user-defined criteria.

Quickly produce data tables or spreadsheets from raw data inputs.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/workany-ai/workany

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Use WorkAny to automate the following task: [DESCRIBE_TASK_IN_DETAIL]. Provide the exact steps WorkAny should take, including any tools or applications it should interact with. For example: 'Create a Python script that extracts data from [EXCEL_FILE.xlsx], cleans it using pandas, and saves the output as [OUTPUT_FILE.csv].'

Example Output

Here’s how WorkAny would automate your task:

1. **Task Breakdown**: You’ve asked WorkAny to generate a Python script that extracts customer data from `customer_data.xlsx`, filters out inactive accounts, and exports the cleaned data to `active_customers.csv`.

2. **Step-by-Step Execution**:
   - **Step 1**: WorkAny opens `customer_data.xlsx` using Excel’s desktop app and reads the data into a pandas DataFrame.
   - **Step 2**: It identifies the 'status' column and filters rows where the value is 'active'.
   - **Step 3**: The script removes unnecessary columns (e.g., 'last_purchase_date') and renames remaining columns for clarity.
   - **Step 4**: WorkAny saves the filtered data as `active_customers.csv` in the specified output folder.
   - **Step 5**: A summary report is generated in a new tab of the Excel file, showing the number of active customers (2,456) and the columns included in the output.

3. **Output**: 
   - **Generated Script**: 
     ```python
     import pandas as pd
     
     # Read data from Excel
     df = pd.read_excel('customer_data.xlsx')
     
     # Filter active customers
     active_customers = df[df['status'] == 'active']
     
     # Clean data
     active_customers = active_customers.drop(columns=['last_purchase_date'])
     active_customers = active_customers.rename(columns={'customer_id': 'ID', 'name': 'Customer Name'})
     
     # Save to CSV
     active_customers.to_csv('active_customers.csv', index=False)
     
     # Generate summary
     summary = f"Total active customers: {len(active_customers)}"
     with pd.ExcelWriter('customer_data.xlsx', engine='openpyxl', mode='a') as writer:
         pd.DataFrame({'Summary': [summary]}).to_excel(writer, sheet_name='Summary', index=False)
     ```
   - **Files Created**: `active_customers.csv` and updated `customer_data.xlsx` with a 'Summary' tab.

4. **Verification**: WorkAny confirms the task is complete by displaying a pop-up notification: 'Task completed successfully. Files saved to [OUTPUT_PATH].'

Would you like to run this script or modify any steps?