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 Ikalus1988 · MCP Server · ★ 501
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is MisakaNet safe to install? View the security audit →
English | 日本語 MisakaNet Git-backed failure-memory for AI coding agents. Zero dependencies. Zero server. Zero database. Paste an error → search 290 lessons → get a fix path. mcp-name: io.github.Ikalus1988/misakanet --
| Stars | 501 |
| Forks | 204 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 64.3345722823544/100 |
| Open Issues | 92 |
| Last Updated | 2026-09-22 |
| Created | 2026-04-29 |
| Platforms | mcp, python |
| Est. Tokens | ~19k |
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MisakaNet is 📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. | https://misakanet.org. It is categorized as a MCP Server with 501 GitHub stars.
MisakaNet is primarily written in Python. It covers topics such as action, agents, ai-agents.
You can find installation instructions and usage details in the MisakaNet GitHub repository at github.com/Ikalus1988/MisakaNet. The project has 501 stars and 204 forks, indicating an active community.
MisakaNet is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to MisakaNet on Agent Skills Hub include mcp, pg-aiguide, npcpy. 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: