WheelSpotter is a cost-controlled scout that identifies mature, reusable software solutions before you waste time building from scratch.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install wheelspotter
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install wheelspotter using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is WheelSpotter?
WheelSpotter is an advanced discovery agent within the Openclaw Skills ecosystem designed to act as a technical scout for developers. Its primary mission is to identify existing libraries, frameworks, and APIs that can be directly integrated into a project, thereby preventing the common pitfall of reinventing the wheel. Unlike basic search engines, it evaluates the maturity and activity of a project to ensure that the suggested solutions are not just available, but production-ready.
This skill focuses on actionable output, providing specific installation commands and integration advice for each recommendation. By using complexity-aware filtering, WheelSpotter ensures that the effort spent searching is always proportional to the complexity of the problem, maintaining a high level of efficiency for any developer using Openclaw Skills.
WheelSpotter Use Cases
- Searching for mature libraries to handle specific technical tasks like PDF parsing or image processing.
- Identifying external APIs and services that can replace complex internal modules.
- Consulting on tech stack selections by comparing popular tools within a specific ecosystem.
- Preventing duplicate development by spotting existing wheels before implementation begins.
- Finding standalone CLI tools or Docker containers to accelerate devops workflows.
How WheelSpotter Works
- Assesses the complexity of the user's requirement (L1 to L3) to set appropriate token and time budgets.
- Classifies the user's intent to determine whether they need a library, a service, a standalone tool, or a reference template.
- Selects the most relevant platforms for search, such as GitHub, PyPI, or npm, based on the identified intent.
- Executes parallel API calls to gather potential candidates while adhering to strict cost and time caps.
- Applies hard filtering logic to remove deprecated repositories or those that do not meet minimum activity thresholds.
- Utilizes an LLM to perform a multi-dimensional evaluation of the top candidates, scoring them on semantic match and integration feasibility.
- Outputs a structured report featuring the best matches, specific action commands, and a cost-benefit summary.
WheelSpotter Setup
To get started with this skill, ensure you have Python 3.8+ installed. You can install the necessary dependencies via pip:
pip install requests pydantic
To use the standalone search functionality, run the provided script with your specific query:
python scripts/search.py --query "python excel read write" --complexity L2 --intent library
For users of Openclaw Skills who require higher search limits, configuring a GitHub Token is highly recommended to increase API rate limits.
WheelSpotter Data Schema & Taxonomy
WheelSpotter uses a structured data schema to ensure that every recommendation is easy to evaluate and implement. Results are organized into the following categories:
| Attribute | Description |
|---|---|
| status | Indicates if suitable solutions were found, not found, or if clarification is needed. |
| recommendations | A list of objects containing the solution name, source platform, and URL. |
| action | The exact CLI command (e.g., pip install) required to acquire the tool. |
| match_score | A calculated value representing how closely the tool fits the user requirement. |
| cost | A summary of resource consumption, including tokens and estimated time saved. |
WheelSpotter Advanced Features
- Three-Tier Budget System that dynamically scales resource usage based on the complexity of the development task.
- Intent-Driven Platform Selection which optimizes search results by targeting the right ecosystems (e.g., Crates.io for Rust, PyPI for Python).
- Early Termination Logic that stops the search immediately if a high-quality match is found or if the task is simple enough to build manually.
- Closed-Loop Delivery providing specific action commands and integration advice rather than just links.
- Integration with Openclaw Skills triggers to automatically suggest searches when a developer mentions building a common utility.
SKILL.md
Loading
Related Openclaw Skills
Lightwing Control
Lightwing Control provides comprehensive AI-driven management for smart desk lamps, featuring lighting adjustments, automated voice broadcasting, and real-time state monitoring.
Telegram News Digest (Lite)
An autonomous AI agent for monitoring and summarizing public Telegram channels without requiring API keys or authentication.
Berlin AI Skill
A comprehensive analytical framework for understanding Berlin's evolution from a divided city to Europe's leading tech and creative powerhouse.
BYD Knowledge Agent
A specialized AI skill providing deep technical and business intelligence on BYD's evolution from a battery maker to the world's leading EV manufacturer.
Use Maskrun
A security-focused utility that masks sensitive environment variables and secrets in command-line output to prevent accidental exposure in logs and transcripts.
Generate Real Estate Ad Creative Brief
A specialized AI agent skill designed to automate the planning of visuals and marketing hooks for high-converting real estate ad campaigns.
Featured*
