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 josephgoksu · MCP Server · ★ 87
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is TaskWing safe to install? View the security audit →
TaskWing The local-first knowledge layer for AI development. Website · Tutorial · Vision · Install Your AI tools start every session from zero -- and every session, your code context flows through someone else's cloud. TaskWing takes the opposite approach. One command extracts your architecture into a local knowledge base on your machine. No cloud. No account. Every AI session after that just knows -- without your knowledge base leaving your infrastructure. Install bash brew install josephgoks
| Stars | 87 |
| Forks | 5 |
| Language | Go |
| Category | MCP Server |
| Quality Score | 64.6950502145659/100 |
| Open Issues | 2 |
| Last Updated | 2026-05-12 |
| Created | 2025-06-03 |
| Platforms | claude-code, cli, codex, gemini, go, mcp |
| Est. Tokens | ~365k |
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TaskWing is Local-first AI knowledge layer. Extract architecture, query from any AI tool via MCP. Private by architecture.. It is categorized as a MCP Server with 87 GitHub stars.
TaskWing is primarily written in Go. It covers topics such as ai, architecture, claude.
You can find installation instructions and usage details in the TaskWing GitHub repository at github.com/josephgoksu/TaskWing. The project has 87 stars and 5 forks, indicating an active community.
The top alternatives to TaskWing on Agent Skills Hub include claude-code-open, mcpproxy-go, daymon. 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: