skill-assessmentEvaluate OpenClaw skills with lightweight static analysis across documentation completeness, code quality, configuration friendliness, and maintenance signal...
Install via ClawdBot CLI:
clawdbot install harrylabsj/skill-assessmentGrade Limited — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Generated Mar 21, 2026
A platform hosting multiple AI skills uses this tool to automatically evaluate submissions for quality before listing them. It ensures only well-documented, maintainable skills are available to users, reducing support overhead and enhancing trust.
An organization adopting OpenClaw skills for internal workflows employs this tool to assess custom or third-party skills for security and maintainability. It helps identify risks and compliance gaps before deployment in production environments.
Individual developers or teams creating OpenClaw skills use this tool during development to iteratively check documentation and code quality. It serves as a lightweight linter to catch issues early and improve release readiness.
Training programs teaching AI skill development incorporate this tool to help students evaluate their projects against best practices. It provides objective feedback on areas like documentation and configuration for learning purposes.
Offer this assessment tool as a cloud service where users upload skill packages for automated analysis. Charge based on the number of assessments or through subscription tiers, targeting developers and enterprises needing regular evaluations.
Provide professional services using this tool to audit and improve OpenClaw skills for clients. Bundle it with custom development or training to help organizations enhance their skill portfolios and reduce technical debt.
Partner with AI development platforms or marketplaces to embed this tool into their CI/CD pipelines. Monetize through licensing fees or revenue sharing, ensuring skills meet quality standards before publication.
💬 Integration Tip
Integrate this tool into CI/CD pipelines to automatically assess skills on each commit, ensuring continuous quality checks without manual intervention.
Scored Apr 19, 2026
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
Humanize AI-generated text to bypass detection. This humanizer rewrites ChatGPT, Claude, and GPT content to sound natural and pass AI detectors like GPTZero,...
Humanize AI-generated text by detecting and removing patterns typical of LLM output. Rewrites text to sound natural, specific, and human. Uses 24 pattern detectors, 500+ AI vocabulary terms across 3 tiers, and statistical analysis (burstiness, type-token ratio, readability) for comprehensive detection. Use when asked to humanize text, de-AI writing, make content sound more natural/human, review writing for AI patterns, score text for AI detection, or improve AI-generated drafts. Covers content, language, style, communication, and filler categories.
去除文本中的 AI 生成痕迹。适用于编辑或审阅文本,使其听起来更自然、更像人类书写。 基于维基百科的"AI 写作特征"综合指南。检测并修复以下模式:夸大的象征意义、 宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、 AI 词汇、否定式排比、过多的连接性短语。
Collaborative thinking partner for exploring complex problems through questioning
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.