pattern-minerAutomatically detects repeated code and command patterns in Python/Shell, generating reusable Jinja2 templates and shell automation scripts via CLI.
Install via ClawdBot CLI:
clawdbot install harrylabsj/pattern-minerGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.29.0.tgzAudited Apr 16, 2026 · audit v1.0
Generated Mar 20, 2026
Analyzes conversation logs from support tickets to identify recurring customer issues and common resolution paths. This helps in creating knowledge bases and automating responses for frequent queries, reducing resolution time and improving customer satisfaction.
Mines task files and decision logs from development teams to uncover patterns in code reviews, bug reports, and feature implementations. This enables teams to streamline processes, identify bottlenecks, and standardize best practices for increased productivity.
Applies anomaly detection to transaction data and decision records to spot unusual patterns indicative of fraudulent activity. This helps financial institutions proactively flag risks, reduce losses, and comply with regulatory requirements by monitoring outliers in real-time.
Uses clustering on patient interaction logs and treatment decisions to identify common care pathways and inefficiencies. This assists healthcare providers in optimizing resource allocation, reducing wait times, and enhancing patient outcomes through data-driven insights.
Analyzes user conversation data and purchase decisions to discover association rules between product preferences and browsing behaviors. This enables personalized recommendations and targeted marketing campaigns, boosting sales and customer engagement.
Offers the pattern-miner skill as a cloud-based service with tiered pricing based on data volume and analysis features. Revenue is generated through monthly or annual subscriptions, targeting businesses seeking scalable insights without infrastructure overhead.
Provides custom implementation and consulting services to integrate the skill into existing workflows, tailored to specific industry needs. Revenue comes from project-based fees and ongoing support contracts, ideal for enterprises requiring specialized solutions.
Offers a free version with basic pattern mining and limited insights, while charging for advanced features like anomaly detection, high-priority insights, and automated application. Revenue is driven by upgrades and add-ons for power users and larger teams.
💬 Integration Tip
Ensure Python dependencies are installed and configure data sources like conversation logs from context-preserver for seamless integration; use incremental mining to handle new data efficiently.
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.