Content-aware output compression for AI coding assistants. 36 specialized processors cut CLI output tokens by 60-99% (git, pytest, npm, terraform, kubectl, docker, and more) without losing errors, diffs, or stack traces.

Graph memory for AI agents — decisions, context, and session history that survive across every conversation. Works with any LLM.

Measure token savings per AI coding agent, optimize context, and share a live local knowledge graph across 16 CLI clients.

Bilingual floating DeepSeek Harness pet with live context, lifetime token ledger, trends, opt-in completion sound, and editable Prompt Optimizer enhancement.

Restore prior Claude Code AND Codex sessions with zero LLM calls. Cross-tool session handoff, cache-expiry prevention, real cost dashboard. One plugin, both hosts.

Cost tracking for DeepSeek Harness: session and model costs, token usage, budgets, provider balances, and coding-plan quotas. Bilingual English/Chinese UI.

Token-saving proxy for Claude Code: renders bulky context (system prompt, tool docs, old history) as dense PNGs to cut input tokens. Runs on Node and Cloudflare Workers.

Claude Code plugin that tracks token usage, identifies wasted context, and saves 30-50% on API costs. Heatmaps, ROI reports, budget alerts, efficiency scores, git-aware suggestions — all local, zero config.

DSH 个人配置:Token 用量统计 / 成本估算 / 自定义指令 / 外观全局字号 / 桌面宠物(含会话状态概览),纯本地不联网。Personal center for DSH: usage stats, cost estimation, custom instructions, global font size & desktop pet with session overview — all local & offline.

Login gate for DeepSeek Harness (dsh) web instances: password or shared-token sign-in, optional TOTP two-factor, session cookies, rate limiting, self-service password change and a user-management CLI

Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse, with 35.24% token savings and DeepSeek Harness support.

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.

High-performance code-intelligence engine for AI agents and IDE, supports 257 languages, multi repositories, based on graph, with access via CLI, MCP Server, and API. AI coding agents teammate - expose only needed information, cutting token usage up to 50x. 100% local. Discord: https://discord.gg/39MFHu3J5d

ANOLISA (Agentic Nexus Operating Layer & Interface System Architecture) | Agentic OS with runtime, security, observability, and Tokenless response compression for lower token usage and cost.

PostMCP core engine - OpenAPI parser, Token Diet, JIT router, and MCP protocol server

Local dashboard auditing Claude Code's per-turn token context (skills, agents, commands, CLAUDE.md, MEMORY.md, hooks, MCP) with toggle-based disable and session analytics.

Multi-account orchestration and secure token storage for OAuth-based MCP servers

Context compression for AI coding agents: compresses tool output before it enters the model, dedups repeats to 13-token refs. Claude Code, Cursor, Codex, Kiro, Zed, any MCP client. Rust, zero LLM calls.

The best DeepSeek Harness plugin for context insight and management, with context dashboard / browser / sidebar and context command, for context statistics, composition, breakdown, evolution details, understanding how the context is made of, and how it evolves. 一站式 DeepSeek Harness 上下文可视化插件，Context 面板及浏览器和侧边栏与 Context 命令，透视上下文组成、演进、压缩、剪枝等事件与动作。

Context-as-image compression proxy for LLMs: renders bulky context (system prompt, tool docs, history) as dense PNG pages with exact per-provider billing math (Anthropic/OpenAI/Gemini). Node and Cloudflare Workers. Part of the OmniRoute family.

Claude Code usage governor: compact professional output, context slimming, tool-output filtering, telemetry, and drift guardrails.

You say it. AutoCode ships it. 48 skills. Code to deployment in one session. iLang v5.0 judgment + secret-safe deploys. Free forever.

Usage billing dashboard plugin for DeepSeek Harness: sidebar cost metrics, real usage aggregation from session logs, current multi-provider pricing catalog

Cut context bloat in your AI-agent stack: find and safely prune unused skills, MCP servers and subagents from real transcript evidence