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Converts DeepSeek protocol calls into OpenAI/Claude/Gemini-compatible APIs with a Go backend and React admin UI. Offers account pooling, protocol adapters, tool-call translation, PoW, and multiple deployment modes (Docker, Vercel, standalone).
Encodes production-grade engineering workflows (spec, plan, build, test, review, ship) as reusable "skills" so AI coding agents follow consistent development practices. Packaged as per-skill SKILL.md files and slash commands for integration with agents and CLIs. Suited for teams embedding engineering guardrails into agent-driven dev workflows.
Dramatically reduces AI agents' context usage by sandboxing large tool outputs and indexing only relevant snippets into a searchable SQLite FTS5 (BM25) knowledge base, improving session continuity and privacy. Deploys cross-platform hooks and sandbox tools to cut context size by ~98% and avoid dumping raw logs into the model's window. ([github.com](https://github.com/mksglu/context-mode/blob/main/README.md?utm_source=openai))
Provides persistent, searchable memory for coding agents (Claude Code, Cursor, Gemini CLI, etc.), automatically capturing tool usage and session facts. Combines BM25, vector embeddings and a knowledge graph for hybrid retrieval, reducing token costs and re-explaining between sessions.
Provides AI coding agents with persistent memory inside an Obsidian vault—preserving session context, decisions, and notes across sessions. Integrates hooks/commands for Claude Code, Codex CLI, and Gemini CLI and optionally uses QMD for semantic recall; aimed at developer workflows.
Turns any codebase, documentation, or knowledge base into an interactive knowledge graph you can explore, search, and ask questions about. Produces node-level summaries, guided tours, and diff impact analysis, and plugs into multiple LLM platforms (Claude Code, Codex, Copilot, Gemini CLI) for query-driven exploration.
Turns any codebase, docs, or wiki into an interactive knowledge graph for exploration, semantic search, and Q&A. Uses a Tree-sitter + multi-agent LLM pipeline to auto-generate node summaries, guided tours, and diff impact analysis; CLI and dashboard integrations.
Turns a repo's code, docs, PDFs, images, and videos into a queryable multimodal knowledge graph for AI coding assistants. Uses deterministic AST extraction for code and LLM-based semantic extraction for other assets, exporting interactive HTML, JSON, and a human-readable audit report.
Lets AI coding agents compile your documents and chat histories into a maintained Obsidian vault: it ingests sources, distills them into interconnected markdown pages, tracks deltas and provenance, and exposes query/lint/export skills across many agents.