Humanizer

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,

Near Best Practices

Comprehensive NEAR best practices guide with 100+ terms covering wallet security, smart contracts, and DeFi safety.

MemPalace Memory System for OpenClaw

MemPalace memory system for OpenClaw/XClaw/WorkBuddy. Archive AI conversations to local long-term storage with semantic search. Commands: /mem-arc (archive),...

memory-index

Structured long-term memory management engine with multi-role isolation, layered indexing, and strict context retrieval protocols to prevent token waste and...

Legal Compliance

Generate legal documents and ensure compliance for startups. Use this skill when the user mentions: terms of service, privacy policy, GDPR, LGPD, cookie cons...

Crypto Terms Translator

A plain-language crypto glossary that translates technical terms into everyday meaning, context, and examples. Use when the user encounters confusing crypto...

Breadth Chart Analyst

This skill should be used when analyzing market breadth charts, specifically the S&P 500 Breadth Index (200-Day MA based) and the US Stock Market Uptrend Stock Ratio charts. Use this skill when the user provides breadth chart images for analysis, requests market breadth assessment, positioning strategy recommendations, or wants to understand medium-term strategic and short-term tactical market outlook based on breadth indicators. All analysis and output are conducted in English.

Dividend Growth Pullback Screener

Use this skill to find high-quality dividend growth stocks (12%+ annual dividend growth, 1.5%+ yield) that are experiencing temporary pullbacks, identified by RSI oversold conditions (RSI ≤40). This skill combines fundamental dividend analysis with technical timing indicators to identify buying opportunities in strong dividend growers during short-term weakness.

usewhisper

Official Whisper Context skill for OpenClaw. Cuts context tokens via delta compression + caching, and adds long-term memory across sessions.

Shared Memory

Cross-host durable memory — same `ling-mem` daemon and store in Claude Code, Codex, and OpenClaw. Three-tier model (core + long-term + episodic staging) of w...

Memoria Memory System

Manages AI assistant long-term memory with layered storage for facts, events, skills, context, and fast indexing, including backup and integrity tools.

Ecloud Long Term Memory

基于移动云记忆服务的长期记忆系统，支持语义搜索和云端存储。 用于处理所有与记忆相关的操作，包括存储用户姓名、年龄、职业、偏好等个人信息，以及搜索、列出历史记忆。当用户说出“我名字叫...”、“我今年...”、“我喜欢...”或询问“我有哪些记忆”时，必须使用此技能。

LLMCOM Prompt Compression

Compress prompts for Claude Code and Cursor CLI. Reduces tokens by removing filler words and abbreviating terms. Self-contained, no external dependencies req...

Multi-Dimensional Trading System

Provides structured multi-timeframe technical analysis using EMA channels and Fibonacci levels for short-term trading decisions in A-shares, DSE, and crypto.

Indian Stock Fundamental Analyser

AI-powered fundamental analysis for Indian stocks (NSE/BSE) designed for long-term investors seeking data-driven investment insights.

Strategical Alignment

Strategical alignment analysis — detect where short-term optimization (profit, efficiency, convenience) is building long-term fragility, dead ends, or collap...

Term Sheet Negotiation Coach

Coach a founder through reading and negotiating a venture term sheet — every economic and control term, line by line. Covers valuation (pre-money / post-mone...

a very short history of #datascience

_The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one--computer science. The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term “Data Science” and its use, attempts to define it, and related terms._