Spec-driven development workflow for AI coding agents: architecture-first planning, task decomposition, GitHub Issue/PR tracking, Deep Discuss, and adaptive control for Claude Code, Codex, Cursor, and other Markdown-capable agents.

Loom is an AI harness, that can be used with local or cloud LLMs, for complex tasks. It decomposes work, drives execution through a verification harness, and keeps models on track with structured state instead of history. It can route between thinking and acting models, verifies outputs, and exposes an APP/TUI/API/CLI/MCP for both humans & agents.

基于 DeepSeek 大语言模型的 AI 编程助手 Agent —— 自然语言驱动的终端编码助手。支持自动读写文件、代码搜索、命令执行、Git 管理，复杂任务智能拆解与多 Agent 并行执行。

The issue tracker that lives in your repo. Built for AI agents. Works for humans.

Agentic AI refers to AI systems capable of autonomous decision-making, planning, and executing tasks based on goals—acting like intelligent agents. These systems combine LLMs with tools, memory, and feedback loops to complete complex workflows with minimal human input.

Turn requirements into executable Linear issues: with explicit dependencies, design boundaries, and interactive validation.

Instantly decompose Azure DevOps work items into hierarchical trees and streamline agile planning in a single action! 🚀

Krako 2.0 – Energy-efficient, triadic multi-tier inference infrastructure enabling adaptive routing across heterogeneous edge–cloud nodes.

Multi-Agent AI legal assistant for solo lawyers practicing Chinese law. Model-agnostic, plug-and-play. 为执业中国法律的独立律师打造的多 Agent AI 助手 — 模型无关、即插即用。

Make any LLM 10x smarter. Recursive DAG decomposition + temporal knowledge graph = free models that match expensive ones. 30/30 tasks passing at $0.0006 total cost.

Autonomous coding agent harness built on Claude Code SDK — implements Anthropic's long-running agent pattern: scan → plan → code → validate → git-commit in a loop. Turn requirements into working code with one command.

AI-Driven Project Decomposition and Implementation

Description: A structured engineering workflow skill for complex AI-assisted coding, coursework, data analysis, and research projects.

Cursor skills and harness-oriented workflows for project intake, problem framing, and AI-ready task decomposition.

Skill-based Reasoning Intelligence — AI orchestration beyond the complexity ceiling

SRAO Stage 2-3: Agent capability graph construction. Decompose SRM into atomic task DAGs, map to 6 agent categories, output Agent Cards and capability graphs.

[TVC 2026] TDNet (Degradation-aware Comprehensive Task Decomposition for Joint Rain and Haze Removal)

🏛️ TipAI Architect — 三方AI答辩式方案架构师。用户提需求→裁判调研设计→正方vs反方辩论→裁决→拆解为原子开发任务

Intelligent travel planning with multi-agent AI orchestration using n8n

Automatically decompose complex prompts into orchestrated, executable workflows for Claude Code. Research-backed prompt chaining and task decomposition.