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12,437 skills indexed with the new KISS metadata standard.
Generate high-quality synthetic datasets using statistical samplers and Claude's native LLM capabilities. Use when users ask to create synthetic data, generate datasets, create fake/mock data, generate test data, training data, or any data generation task. Supports CSV, JSON, JSONL, Parquet output. Adapted from NVIDIA NeMo DataDesigner (Apache 2.0).
Use this skill when the user needs to analyze, clean, or prepare datasets. Helps with listing columns, detecting data types (text, categorical, ordinal, numeric), identifying data quality issues, and cleaning values that don't fit expected patterns. Invoke when users mention data cleaning, data quality, column analysis, type detection, or preparing datasets.
Memory preservation for Claude Code sessions. Use when approaching token limits, needing to /reset or /compact, switching between complex tasks, or preserving critical session state before context loss. Creates comprehensive memory dumps at /tmp/total-recall containing current state, decisions, artifacts, and next steps for seamless context restoration.
2. 算法 链表翻转 异或取数组中的不同数
Interface with Zotero's MCP server to search and retrieve bibliographic data using advanced semantic search and multi-strategy approaches. Designed for output as a plain markdown formatted outline, suitable for pasting into Logseq. Also offers side-by-side translation of Chinese titles and abstracts for improved English language search within Logseq. Context-aware - uses agents in Claude Code, batched searches in Claude Desktop.
Domain registration and management via Namecheap API.
Use this skill when users want to generate images using OpenAI's image generation API (DALL-E or gpt-image-1), or extract text from images using OCR. Invoke when users request AI-generated images, artwork, logos, illustrations, visual content from text prompts, or need to extract text/data from images.
This repository contains the Minnesota Conciliation Court Case Agent, a FastAPI
This file provides guidance for agents working within this repository, which is structured as a "Self-Correcting Coding Agent Team." This team of specialized AI agents collaborates to develop, review, test, and debug code in an iterative and self-improving workflow.
=== foundation rules ===
- `frontend/`: Vite + React app. Entry in `src/main.jsx`, UI in `src/App.jsx`, styles in `src/styles.css`, config defaults in `src/config.js`, API helpers in `src/api/client.js`, and token helpers expected in `src/lib/token.js` (imported by `App.jsx`).
Web utility to import Statement of Work (SOW) documents into ScopeStack. Users upload Word/PDF files, AI extracts services/subservices, users review/edit mappings, then export JSON for ScopeStack API.
DOCUMENT METADATA
> **For**: GitHub Copilot, OpenAI Codex, and other AI coding assistants
Overview
This file provides guidance to agents when working with code in this repository.
**For AI Coding Agents** — Entry point for working with this codebase.
- `apps/cwe_ingestion/`: CWE downloader, parser, embedders (Gemini/local), and Postgres stores.
AGENTS.md file
This repository is designed to be maintained by AI coding agents.
- `app.py`: Flask web UI for tagging, manual video review, and NFO metadata plan/write/history workflows.
- 本文件适用于整个仓库，更新任意代码或文档时请遵循下列约定。
This repo is a local Hyperlane registry plus ops configs for Celestia Mocha and Eden testnets. It tracks chain metadata and core addresses, warp route deployments, relayer and faucet configs, and Solidity tooling for HypNativeMinter.
This appears to be a transcript of a presentation on AI trends, specifically focusing on agentic workflows, visual AI, and large language models. The speaker discusses several key points, including: