This repository is a reference architecture and plugin collection for deploying AI agents in financial services using Anthropic's Claude platform. It defines 10+ named agents (e.g., Pitch Agent, Earnings Reviewer, KYC Screener) and 100+ skills that guide Claude to produce institutional-grade financial documents: Excel valuation models (DCF, LBO, comps), PowerPoint pitch decks, Word research reports, and HTML earnings previews. The architecture follows a dual-delivery model: agents can be installed as Claude Cowork plugins via marketplace registration, or deployed headlessly via the Managed Agents API (agent.yaml manifests in managed-agent-cookbooks). Skills are authored in vertical plugins organized by domain (investment banking, equity research, private equity, wealth management) and synced into agent bundles. The repository also includes a Microsoft 365 add-in installer (claude-for-msft-365-install) for deploying Claude into Excel, Word, PowerPoint, and Outlook. Data flows rely heavily on MCP servers (LSEG, S&P Global, Daloopa, FactSet) and SEC EDGAR for financial data, with strict citation and formula-over-hardcode conventions. The repo is primarily prompt engineering and configuration rather than application runtime code, but it includes several reusable Python scripts for skill scaffolding, document validation, and cross-reference linting.
This repository is an open-source terminal-based AI coding assistant built in Rust, clean-room reimplementing Anthropic's Claude Code. It provides an interactive TUI (ratatui) for multi-provider LLM conversation (Anthropic, OpenAI, Google, and 30+ OpenAI-compatible) with a rich tool ecosystem for file editing, bash execution, MCP integration, code analysis, web fetching, and agent orchestration. The architecture is modular: a provider abstraction layer (`LlmProvider` trait), plugin system, slash commands, session management, and an Agent Client Protocol (ACP) for external IDE integration. The codebase is organized into 12+ workspace crates with clear separation of concerns, but many modules are still in active refactoring (issue #232) with stubs, TODOs, and incomplete feature gates (36 experimental features). The permission system, tool budget enforcement, and streaming decoders demonstrate strong engineering, while dual feature flag systems and global mutable state introduce maintenance risk. Overall, it is a feature-rich, well-architected application with some rough edges from ongoing migration.
EvoNexus is an AI-powered business operating system with a dashboard for managing various applications and services. It has features for managing systems, knowledge base, and user authentication, and uses Docker for containerization.
This repository is a comprehensive, structured library of over 50 marketing 'skills' designed for AI coding agents (Claude Code, Codex, Cursor, etc.). Each skill is a self-contained directory with a SKILL.md (frontmatter + workflow), a references/ folder with deep-dive documentation and benchmarks, and evals/evals.json for behavioral testing. The skills cover the full marketing lifecycle: copywriting, SEO, paid ads (Meta, Google, LinkedIn), email, social, CRO, pricing, offers, attribution, market research, and more. Additionally, the repository includes 40+ zero-dependency Node.js CLI scripts that act as API clients for marketing tools like Ahrefs, Semrush, GA4, Meta Ads, Mailchimp, etc., each following a consistent pattern with dry-run support and environment variable authentication. Several integration guides and a central tool registry (REGISTRY.md) complete the ecosystem. While primarily a content library, the CLI scripts and validation/sync tooling demonstrate strong engineering patterns. The skills are designed to be consumed by AI agents to execute complex, multi-step marketing tasks, with cross-skill references and a shared context document (product-marketing.md) enabling orchestration.
This repository contains demonstration scripts and documentation for the GPT_API_free service, a forwarding proxy that provides free access to various large language models including GPT, DeepSeek, Claude, Gemini, and others. The actual forwarding server is not included; instead, the repository offers Python examples using the OpenAI library and standard HTTP requests to illustrate how to interact with the service's API endpoints for chat completions, image generation/editing, audio transcription, speech synthesis, and file analysis. The code is minimal and educational, focusing on replacing the base URL and authentication header to route requests through the proxy. The codebase is primarily a collection of usage examples tightly coupled to the external service's address and API key format. There is no production logic, no tests, and no independent reusable components. The demos follow standard patterns for calling OpenAI-compatible APIs and serve as a quick-start guide for developers integrating the service. As such, the repository does not represent a standalone application but rather a documentation supplement for the GPT_API_free project.
This repository is a lightweight, reference implementation of several standard LLM benchmarks (MMLU, GPQA, MATH, HumanEval, DROP, MGSM, SimpleQA, BrowseComp, HealthBench) developed by OpenAI. It provides a unified evaluation framework based on an `Eval` base class and `SamplerBase` that supports OpenAI Chat Completions API, Anthropic Claude API, OpenAI Responses API, and reasoning models (o1, o3, o4-mini). The main entry point is `simple_evals.py` which accepts command-line arguments to select models and evals, runs them, and outputs HTML reports and JSON metrics. The code is cleanly factored into per-benchmark modules, a shared utility module (`common.py`), and sampler adapters. The framework is designed for reproducibility of OpenAI's published benchmark numbers, with all data loaded from public Azure blob storage URLs. The repository is deprecated as of July 2025 and will not receive updates for new models or benchmarks, though it will continue to host reference implementations for HealthBench, BrowseComp, and SimpleQA.
This repository is Visual Studio Code Void, a deprecated fork of VS Code focused on AI-enhanced desktop editing. While the project is no longer accepting contributions per its README, it contains the core VS Code architecture with additional AI-oriented features in the `src/vs/workbench/contrib/void/` directory. The application provides a code editor experience with syntax highlighting, IntelliSense, debugging, and extension support, built on Electron for desktop with web fallback capabilities. It features a modular architecture with core editor (Monaco), workbench/UI framework, platform services, and extension system, along with a Rust-based CLI for code and tunnel management.
This repository is primarily a large collection of GPT prompt templates (markdown files) designed for use with AI chat platforms like FlowGPT or gptcall.net. Each prompt file defines a specific role or task for the AI, such as a resume editor, coding tutor, fitness planner, or creative writing assistant. The repository also includes an Android application (OpenClaw and Codex) referenced in the README, along with Node.js scripts (update.mjs, db.mjs) for managing the prompt data. The prompts are structured with placeholders for user input and often request specific output formats, enabling users to import them into chat interfaces to obtain specialized AI behavior without crafting prompts from scratch. While the prompt library is extensive, it contains significant duplication, minimal-effort entries, and some policy-risking content (e.g., jailbreaks, NSFW role-play), which affects overall quality and reuse potential.
This repository is a CLI tool (@webdevtoday/claude-agents) that enhances Claude Code with specialized AI agents for software development tasks. It provides 16 domain-specific agents (code-reviewer, test-runner, debugger, devops-engineer, etc.) that can be executed independently or chained together in workflows. The system integrates with Claude Code through hooks, commands, and MCP tools, featuring a memory system for agent coordination, voice notifications for task completion, and context-aware adaptation to project structures (particularly context-forge projects). The architecture follows a modular command pattern with core utilities for configuration, TTS, hooks, and agent management. The project has been explicitly archived as of June 2026 because Claude Code now provides native subagents, skills, hooks, and plugins functionality that replicates this tool's features. While the code demonstrates solid engineering patterns in several areas, it is no longer maintained and has been superseded by native capabilities in the platform it was designed to enhance.