— · Apache-2.0
Control what your AI can see. LeanCTX (Lean Context) is the context intelligence layer for AI agents — one local Rust binary that decides what they read, remembers what they learn, guards what they touch, and proves what they save. 60–90% fewer tokens as the receipt. 76 MCP tools, 30+ agents, local-first.
— · Apache-2.0
Code intelligence for agents: find the code that matters and keep your context window and tokens lean.
— · Apache-2.0
Cross-platform toolkit to enhance Claude Code with multi-LLM consensus, 8 specialist agents, semantic knowledge search, and one-command install.
v0.4.2 · MIT
DeepSeek Flow — Markdown-first visual workflow plugin for DeepSeek Harness with executable Boolean gate semantics, two-way canvas sync, and AI review/optimization.
v1.5.0 · MIT
AI code reviews grounded in 12 classic engineering books — decay risk diagnostics with book citations, severity labels, and 6 analysis modes including full-sweep auto-fix
v2.0.0 · MIT
Install engineering discipline into any AI coding assistant. Composable skills for design, implementation, review, and team standards. Better process, not just better prompts.
v1.5.0 · MIT
AI code reviews grounded in 12 classic engineering books — decay risk diagnostics with book citations, severity labels, and 6 analysis modes including full-sweep auto-fix
v0.5.0 · MIT
Purge multi-vendor AI watermarks: clean Unicode text, apply statistical rewrite hooks, and clear C2PA plus metadata from PNG, JPEG, SVG, PDF, DOCX, HTML, and MD.
— · MIT
🤫 Token-lean sessions at the harness level. An easy to grasp output style, output-shrinking hooks, and log compression cut both input and output tokens.
v5.4.2 · MIT
HEDGEHOG codes Cleaner, Faster and with Fewer Tokens. Hedgehog's AI-driven development builds a task dependency graph from your spec-driven, BMAD-METHOD plan, so Claude Code, Cursor & Gemini CLI stay locked to it. A CLI-enforced state machine for agentic coding. Now builds DeepSeek DSH Plugins. DeepSeek Harness、DSH 插件、AI 编程、BMAD 方法
v1.36.160 · MIT
The AI-coding operations layer that makes "done" require evidence — persistent memory, evidence-gated completion checks, and clean handoffs for any AI agent (Claude Code, Codex, Cursor). State lives as plain files in your repo. CLI + MCP, 0 runtime depend
v1.33.0 · MIT
Claude Code plugin for structured, AI-driven software development. Orchestrates complex workflows -- from issue assessment to staged implementation to review and merge -- giving developers the speed of full automation with the control of human-in-the-loop checkpoints.