actual-self-improvementCapture durable lessons from debugging, user corrections, missing capabilities, and repeated workflow friction so future sessions avoid the same mistakes. Us...
Install via ClawdBot CLI:
clawdbot install tristanmanchester/actual-self-improvementGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Generated Mar 20, 2026
A new developer joins a project and repeatedly encounters non-obvious failures with the build system or dependencies. The team uses this skill to log errors and learnings about project-specific conventions, such as using pnpm instead of npm, ensuring the developer avoids repeated mistakes and accelerates onboarding.
During deployment or CI/CD pipeline setup, engineers face recurring issues like platform mismatches or tool failures. This skill helps capture durable errors and feature requests, such as Docker build problems on specific hardware, enabling proactive solutions and reducing downtime in future sessions.
Data scientists work on shared projects with unique data processing conventions or missing capabilities. They use this skill to log learnings about workflow friction, like specific library versions or data format requirements, promoting reusable knowledge across team members and preventing repeated errors.
Support agents diagnose non-obvious customer issues and discover reusable workarounds. This skill allows logging corrections and feature requests into a shared workspace, building a durable knowledge base that improves response accuracy and reduces resolution time for recurring problems.
Maintainers handle user-reported bugs and missing features across different environments. By using this skill to log errors and learnings, they can track patterns, promote solutions into project documentation, and extract insights for new tooling or skill development.
Offer this skill as part of a subscription-based platform for development teams, integrating with existing tools like IDEs and project management software. Revenue is generated through monthly or annual licenses per user, with tiers based on advanced features like analytics and cross-project sharing.
Provide tailored implementations and training for organizations adopting this skill, helping them integrate it into specific workflows or industries. Revenue comes from one-time project fees and ongoing support contracts, leveraging expertise in debugging and process optimization.
Distribute the core skill for free to attract individual users and small teams, while monetizing advanced capabilities such as AI-powered search, automated promotion of learnings, and enterprise-grade security. Revenue is driven by upgrades to premium plans and add-ons for large-scale deployments.
💬 Integration Tip
Ensure the workspace root is correctly identified before logging learnings to avoid writing to the wrong directory, and always search for duplicates to maintain a clean, reusable knowledge base.
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。
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.
Collaborative thinking partner for exploring complex problems through questioning