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.
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.
Provides portable agent 'skills' that steer code-generating agents toward higher-quality UI: stronger layout, typography, spacing and image-reference boards. Ships adjustable dials for design variance, motion and density and image→code pipelines for agent-led frontends.
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.
Generates production-ready App Store and Google Play screenshots from app metadata and style preferences using AI. Scaffolds a Next.js project, composes ad-style slides with localized/RTL support, and exports PNGs at all required Apple and Google resolutions.
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.
Runs multiple AI agents in parallel inside a single macOS browser, giving each agent an isolated Space that can use your real logins without touching your tabs; controllable via an ego-browser JavaScript skill to perform web automation with fewer tokens and faster task completion.
Provides a single MCP endpoint that lets AI coding agents search AWS docs, run sandboxed Python scripts, and make authenticated AWS API calls with enterprise guardrails like IAM condition keys, CloudWatch metrics, and CloudTrail auditing.