Provides reusable “skill” instruction bundles that teach AI coding tools how to author, query, and operate Microsoft Fabric workloads via REST APIs, T-SQL, KQL and notebooks. Includes Copilot CLI/Claude/Cursor integrations, workload-focused bundles, and optional MCP configurations for live data access.
Optimizes websites for AI-first search by providing GEO-focused SEO audits: citation-readiness scoring, AI-crawler access checks, schema generation, platform-specific recommendations, and client-ready PDF reports — delivered as a Claude Code skill with CLI commands.
Local-first session analytics for AI coding agents: discover, search, and track token usage and estimated costs across Claude Code, Codex, Forge and 20+ other agents. Single binary / desktop app that runs locally (no cloud accounts) with fast, SQLite-backed queries and optional PostgreSQL/DuckDB sync.
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))
Indexes codebases into a persistent, queryable knowledge graph for AI coding agents, enabling full-repo indexing in minutes and sub-millisecond structural queries. Bundles 158 vendored tree-sitter grammars, a Hybrid LSP resolver, built-in embeddings, and 14 MCP tools for search, trace, and architecture analysis.
One-command installer that gives AI agents the ability to read and search the web and social platforms (web pages, Twitter/X, Reddit, YouTube, GitHub, Bilibili, XiaohongShu) by installing and wiring upstream CLIs and MCP connectors while keeping credentials local.
Provides a deployable personal AI assistant that runs locally or in the cloud, supports multi-channel chat, extensible Skills/Plugins, and local-model runtimes. Key features include three-layer memory, kernel-level sandboxing and tool/file guards, and bundled QwenPaw-Flash local models for zero-API deployments.
Runs local AI models on Apple Silicon as an OpenAI‑compatible server, emphasizing low latency, prompt caching, and reliable tool-calling. Optimized for M1–M4 Macs with multimodal support and drop‑in compatibility for IDEs and agent frameworks.
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 a structured library of 754 cybersecurity skills (agentskills.io format) mapped to MITRE ATT&CK, NIST CSF, MITRE ATLAS, D3FEND and NIST AI RMF — so AI agents can load practitioner workflows and decision logic across 20+ platforms.
Builds a local structural knowledge graph of a codebase so AI coding assistants read only the minimal, relevant code during reviews and daily tasks—reducing tokens used while providing blast-radius impact analysis, incremental updates, and MCP integrations.
Audits and reduces token waste in LLM sessions by compressing verbose outputs, checkpointing before compaction, and restoring lost context. Runs fully locally with zero telemetry and provides a live token dashboard plus plugins for Claude Code, OpenClaw and Codex.