Integration platform for AI agents with 250+ app connectors

:black_heart: Create and share beautiful images of your source code

Like htop, but for AI coding agents. Monitor Claude Code & Codex CLI sessions, tokens, context window, rate limits, and ports in real-time.

Full-stack .Net 10 Clean Architecture (Microservices, Modular Monolith, Monolith), Blazor, Angular 21, React 19, Vue 3.5, BFF with YARP, NextJs 16, Domain-Driven Design, CQRS, SOLID, Asp.Net Core Identity Custom Storage, OpenID Connect, EF Core, OpenTelemetry, SignalR, Background Services, Health Checks, Rate Limiting, Clouds (Azure, AWS, GCP), ...

Turn your PC, Mac, or Linux box into an AI server. LLM inference, chat UI, voice, agents, workflows, RAG, and image generation.

Spoon is a metaprogramming library to analyze and transform Java source code. :spoon: is made with :heart:, :beers: and :sparkles:. It parses source files to build a well-designed AST with powerful analysis and transformation API.

可能是最深度的 AI 投研报告 Skill：九章个股深研 + 九章财报深度分析，脚本化 DCF/EPV 与可复算估值

Two Claude Skills that turn agents into AI film directors, providing cinematic dramaturgy and exact prompt syntax for major video/image models.

Discounted cash flow valuation and intrinsic value analysis for public companies. Use when the brief asks for DCF, fair value, intrinsic value, price target, undervalued or overvalued analysis, or "what is this company worth?"

A self-learning skill layer for Claude Code that automatically distills, merges, updates, and prunes skills from real sessions.

A consumer-feeling dating / matchmaking dashboard — left rail navigation, ticker bar of community signals, headline KPIs, a 30-day mutual-matches bar chart, and a match-rate trend block. Editorial typography, restrained accent. Use when the brief asks for a "dating site", "matchmaking", "community dashboard", "social network dashboard", or any consumer product where the data is the story.

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.