— · Apache-2.0
MCP server for JADX-AI Plugin
— · Apache-2.0
A MCP (Model Context Protocol) server for interacting with dbt.
v1.0.33 · MIT
GodPrompt MCP server + portable Agent Skill for coding agents — TDD, debugging, verification gates, task routing, and progressive disclosure.
— · MIT
Runtime evidence that helps agents trace, profile, and burn down hotspots in application and native code, GPU kernels, and inference stacks.
— · no license
Persistent project-scoped memory for Claude Code. Auto-loads on session start, structured by schema, hot/cold layered.
— · no license
A persistent, Git-versioned map of your whole codebase and database schema that coding agents read before they touch anything. Local-first MCP server + CLI in Go: a governed repository index of code knowledge that gives Claude Code, Codex, Cursor, and opencode long-term context, memory, and code intelligence.
— · MIT
The missing tool to sift your Claude Code sessions: regex search across all record types, recover files, extract images, inspect subagent topologies, match between plan files and sessions.
— · no license
Claude Code plugin that offloads large outputs to filesystem and retrieves when required.
— · MIT
GSD Pro — The most powerful AI coding workflow for Claude Code. Free alternative to Cursor Composer, Copilot Workspace, Devin & Bolt. Multi-model routing, rollback/recovery, adaptive context. Fork of 30K★ original. npx get-shit-done-cc
v1.0.9 · MIT
Semantic Intelligence for Large-Scale Engineering. Context+ is an MCP server designed for developers who demand 99% accuracy. By combining RAG, Tree-sitter AST, Spectral Clustering, and Obsidian-style linking, Context+ turns a massive codebase into a searchable, hierarchical feature graph.
v2.9.0 · MIT
Vibe Check is a tool that provides mentor-like feedback to AI Agents, preventing tunnel-vision, over-engineering and reasoning lock-in for complex and long-horizon agent workflows. KISS your over-eager AI Agents goodbye! Effective for: Coding, Ambiguous Tasks, High-Risk tasks
— · MIT
Context engineering for AI agents. ~80% fewer tokens. Fix tool overload. Skills and memory with in-process BM25 and semantic retrieval. Progressive Disclosure. No vector DB.
— · MIT
Your entire engineering context, deeply understood