Use-case landing page

Best Python skills for teams

Team-friendly picks for collaboration-heavy running Python scripts, data workflows, and backend automation.

View leaderboardSearch all skillsCompare skills

Matched skills

30

Real skills currently mapped into this landing page cluster.

Average security

73.7

A quick trust signal across the skills currently surfaced here.

Combined installs

300

Adoption signal across the skills shown on this page.

Why this page exists

Team-oriented Python searches usually care about onboarding, collaboration, and reliability. This page targets that angle while still using the shared SkillsReview ranking and trust system underneath.

It also gives SkillsReview a scalable python cluster that can later expand into narrower long-tail pages without changing the route structure. That gives the rollout audience-specific coverage without having to fork the entire page architecture for every new modifier.

Top skills in this cluster

Ranked with live SkillsReview data and linked into detail pages that can convert search traffic into actual usage.

See full leaderboard β†’

Advanced filters

Combine security, update window, activity, and reputation filters. The current filter state stays in the query string so this category view is shareable.

#5

summarize

by openclaw

Security 85

Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for β€œtranscribe this YouTube/video”).

Category
utility
Installs
300
Stars
100
Reviews
5
Read review β†’Compare
#25

OpenClaw 11-in-1 Visual Automation Suite (Windows Only) Complete visual automation toolkit with 11 integrated modules. ### πŸ’° Price One-time purchase: **$2.99** (Lifetime access to all modules + future updates) ### πŸš€ How to Purchase 1. Pay via PayPal Invoice: πŸ”— [Click to pay $2.99](https://www.paypal.com/invoice/p/#V2RC9S8LVKJ434R9) 2. After payment, send your email to: **1215066513@qq.com** 3. I will send the full download link within 12 hours. ### πŸ–₯️ Compatibility - Windows 10 / 11 only - Not compatible with macOS / Linux ## 1. Product Basic Description ### 1.1 Core Functions Provides professional universal computer vision automation capabilities covering the full-process visual automation scenarios such as environment initialization, full-screen automatic screenshot, OCR text recognition, template matching target localization, mouse click simulation, keyboard input simulation, and complete environment initialization & cleanup mechanisms. It supports custom task combination and cyclic execution. ### 1.2 Version & Directory Description - Core Capability: Flexible invocation based on minimum executable units, supporting parameter customization, result variable inheritance, and custom skill saving. All functions can be used directly with the `call` command right after extracting the package. - Directory Structure: - `claw.json` - Skill package configuration file - `skills/all_skills.claw` - All skill unit definitions - `templates/` - Directory for template images (place your template images here for matching) - Temporary file directory `temp/` (for storing screenshots like temp/screen.png) is automatically created after executing `init_env`; temporary screenshot files can be cleaned up via `clean_temp`. - Version Info: Current version: 1.0.0; Compatible with OpenClaw >= 1.0.0 ### 1.3 Paid Attribute This automation skill system (vision-auto-tool-pro) is a paid professional toolkit. The document does not explicitly authorize commercial use of the toolkit. The paid permission only covers basic usage (non-commercial by default), and commercial use requires separate confirmation of authorization with the provider (e.g., purchasing a commercial license, signing a commercial agreement). ## 2. Complete Skill Invocation Manual ### Important Notes Ensure sufficient time is reserved for the computer to respond to each click or operation. For example, add a 2-second wait after `mouse_click` to avoid operation failure due to slow system response. ### 2.1 List of All Minimum Executable Units | Unit Name | Fixed Call Name | Function Description | Individual Call Method | |-------------------------|--------------------------|--------------------------------------------------------------------------------------|-------------------------------------------------| | Initialize Environment | `init_env` | Create directory structure, clear temporary files, check template directory | `call init_env` | | Full Screen Screenshot | `screenshot_full` | Capture entire screen and save as temp/screen.png | `call screenshot_full` | | Check Screenshot Validity | `check_screenshot_valid` | Check for black screen/freeze, wake up the interface if invalid | `call check_screenshot_valid` | | Wake Interface | `wake_window` | Solve the problems of background non-rendering and black screenshot | `call wake_window` | | OCR Recognition | `ocr_recognize` | Recognize all text on the screen and their corresponding coordinates | `call ocr_recognize` | | Template Matching | `template_match` | Use template image to match and locate icons/buttons | `call template_match category template_name` | | Unified Localization | `locate_target` | Prioritize OCR positioning; use template matching if not found, return coordinates | `call locate_target target_text OR category+template_name` | | Mouse Click | `mouse_click` | Move to the specified coordinates and perform click operation | `call mouse_click X Y [click_type, default=single_click]` | | Keyboard Input | `keyboard_input` | Input text after locating the input box | `call keyboard_input target_coords/description input_content` | | Clean Temporary Files | `clean_temp` | Delete temporary screenshots and free up storage space | `call clean_temp` | | Loop Restart | `loop_restart` | Wait 2 seconds then go back to the screenshot step and restart the process | `call loop_restart` | ### 2.2 Method for Invoking Individual Units #### Invocation Format ``` call [unit_call_name] [parameter...] ``` #### Invocation Examples - Initialize environment: `call init_env` - Template match browser icon on desktop: `call template_match desktop web` - Perform double-click at coordinates (100,200): `call mouse_click 100 200 double` ### 2.3 Combine into Custom New Tasks By writing one call instruction per line in execution order, you can combine them into a custom new task, which supports variable inheritance, looping, and permanent saving. #### Format Example (Open Browser) ``` # Task Name: Open Browser call init_env call screenshot_full call check_screenshot_valid call locate_target browser desktop Browser call mouse_click {{resultX}} {{resultY}} double call clean_temp ``` #### Combination Steps 1. **Write task name and description first** (for easier identification later) 2. **In execution order**, write one `call unit_name parameters` instruction per line 3. Coordinates can use variables `{{resultX}}`/`{{resultY}}` to inherit the output result of the previous unit 4. If cyclic execution is required, add `call loop_restart` at the end 5. **Save custom skill**: Use `save_skill skill_name instruction_list` to save the task permanently, then call it directly with `call skill_name` ### 2.4 Complete Main Flow Invocation Example ``` # General Main Flow: vision_auto_main call init_env call screenshot_full call check_screenshot_valid call ocr_recognize # If template matching is needed, add this line: call template_match category name call locate_target target_text call mouse_click {{X}} {{Y}} # If text input is needed, replace the above line with: call keyboard_input {{X}} {{Y}} input_content call clean_temp # Add this line if you need to loop: call loop_restart ``` ### Important Notes Ensure sufficient time is reserved for the computer to respond to each click or operation. > For example, add a 2-second wait after `mouse_click` to avoid operation failure due to slow system response.

by community

Security 71

OpenClaw skill indexed by SkillsReview.

Category
web
Installs
0
Stars
0
Reviews
99
Read review β†’Compare

Popular comparisons from this cluster

Internal comparison links help users evaluate adjacent options and give search engines deeper crawlable structure.

Feishu Agent Relay - Multi-Bot Collaboration vs coding-agent-commonFeishu Agent Relay - Multi-Bot Collaboration vs Coding Agent Localcoding-agent-common vs Coding Agent Local

Featured comparisons for this cluster

These curated comparison pages are the higher-intent next step after a user lands on this hub and wants a tighter decision surface.

More comparisons β†’

Agentic Coding vs Vibe Coding

A coding-cluster comparison for users picking a code-generation or developer-workflow skill and wanting a fast side-by-side decision surface.

Bailian Web Search vs DeSearch Web Search

A search-focused comparison page for users choosing between web-search-oriented skills for research, discovery, and live information gathering.

Related landing pages

This internal-link graph is the scalable part of the rollout: each new template adds more crawlable surfaces without hand-copying content.

Explore more hubs β†’

Best Coding Skills for OpenClaw

Developer-focused skills for code generation, review, debugging, and local repo workflows.

Best Data Skills for OpenClaw

Skills for SQL, analytics, data workflows, extraction, and structured information processing.

Best OpenClaw Coding Skills

A focused list for users searching for code generation, repo automation, and developer workflow skills.

Best GitHub and Git Skills

Repo automation, PR workflows, CI integrations, and version-control helpers.

Best Data and SQL Skills

For SQL, analytics, databases, CSV pipelines, and structured data workflows.

Best Documentation and Knowledge Skills

For docs, wikis, notes, knowledge bases, and documentation workflows.

Best Python Skills

Skills for running Python scripts, data workflows, and backend automation.

Best Python Automation Skills

Automation-focused picks for running Python scripts, data workflows, and backend automation.

Best Python Workflow Skills

Skills built for repeatable python workflows and multi-step execution.

Best Python Integrations

Integration-focused picks for running Python scripts, data workflows, and backend automation.

FAQ

What makes a Python skill team-friendly?

On this page, team-friendly usually means the skill maps well to shared workflows, predictable handoffs, and trust signals that matter when more than one person relies on the tool.

Why create a team-focused variant at all?

Because β€œfor teams” is a real modifier users search with, and the shared matrix makes it possible to cover that modifier without publishing thin, disconnected pages.