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 bgauryy · MCP Server · ★ 838
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is octocode-mcp safe to install? View the security audit →
Octocode: Research Driven Development for AI Research like a Senior Staff Engineer.In every codebase, in seconds. Stop guessing. Octocode researches code locally and externally: your own workspace (ripgrep + LSP-level go-to-definition, references, call hierarchy) and the world's (GitHub repos, PRs, npm/PyPI packages), turning it into verifiable evidence your AI can search, read, and trace. Use it as an MCP server inside your AI assistant, or as a terminal CLI. octocode.ai · MCP Server · CLI · Skills Two ways to run Octocode Quick scripted lookups, CI, piping to othe
| Stars | 838 |
| Forks | 71 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 68.6458741050153/100 |
| Open Issues | 4 |
| Last Updated | 2026-05-18 |
| Created | 2025-06-05 |
| Platforms | claude-code, mcp, node |
| Est. Tokens | ~8581k |
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octocode-mcp is MCP server for semantic code research and context generation on real-time using LLM patterns | Search naturally across public & private repos based on your permissions | Transform any accessible codeb. It is categorized as a MCP Server with 838 GitHub stars.
octocode-mcp is primarily written in TypeScript. It covers topics such as agent, ai, ai-agents.
You can find installation instructions and usage details in the octocode-mcp GitHub repository at github.com/bgauryy/octocode-mcp. The project has 838 stars and 71 forks, indicating an active community.
octocode-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to octocode-mcp on Agent Skills Hub include octocode, OpenContext, atlassian-mcp-server. 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: