No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by yoloshii · MCP Server · ★ 61
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is gigaxity-deep-research safe to install? View the security audit →
Gigaxity Deep Research — Open-source deep research MCP server for Claude Code, Codex, Cursor, Hermes, and any MCP-capable agent Open-source deep research MCP server for Claude Code, Hermes, Cursor, and any MCP-compatible agent. Qwen3-30B-A3B-Thinking via OpenRouter plus multi-source web synthesis with citations. Gigaxity Deep Research is a multi-source synthesis pipeline — six MCP tools (two primitives / plus four deep-research tools ///) with a matching FastAPI REST surface, fronting parallel multi-source search, RRF fusion, citation binding, and contradiction detection. The synthesis stage runs against any OpenAI-compatible chat-completions model; the recommended default is Alibaba's Qwen3-30B-A3B-Thinking, a reasoning-tuned 30B-A3B MoE model, but DeepSeek-R1, Qwen-QwQ, Llama 3.x, and hosted-aggregator endpoints (OpenRouter and the like) all work — pick any model your endpoint serves. The search layer pulls from a "Triple Stack" of complementary MCPs (Ref, Exa, Jina) alongside SearXNG, Tavily, and LinkUp connectors. A bundled companion — the MCP shim around [GPT Researcher](https://g
| Stars | 61 |
| Forks | 9 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 72.4468956877336/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-30 |
| Created | 2026-05-04 |
| Platforms | browser, claude-code, codex, mcp, python |
| Est. Tokens | ~19k |
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gigaxity-deep-research is Open-source deep research MCP. Qwen3-30B-A3B-Thinking via OpenRouter, cited web synthesis for Claude Code, Codex, Cursor, Hermes and any MCP-compatible agent.. It is categorized as a MCP Server with 61 GitHub stars.
gigaxity-deep-research is primarily written in Python. It covers topics such as agentic-ai, agentic-search, ai-agents.
You can find installation instructions and usage details in the gigaxity-deep-research GitHub repository at github.com/yoloshii/gigaxity-deep-research. The project has 61 stars and 9 forks, indicating an active community.
gigaxity-deep-research is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to gigaxity-deep-research on Agent Skills Hub include af-deep-research, xc-mcp, google-analytics-mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: