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 Lap-Platform · MCP Server · ★ 344
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is LAP safe to install? View the security audit →
Lean API Platform Agent-Native API specs. Verified, compressed, ready to install. <img src="h
| Stars | 344 |
| Forks | 31 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 68.6156241087546/100 |
| Open Issues | 8 |
| Last Updated | 2026-08-17 |
| Created | 2026-02-08 |
| Platforms | claude-code, cli, mcp, python |
| Est. Tokens | ~17k |
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LAP is Your agents are guessing at APIs. Give them the actual Agent-Native spec. 1500+ API's Ready To-Use skills, Compile any API spec into a lean, agent-native format. 10× smaller. OpenAPI, GraphQL, AsyncA. It is categorized as a MCP Server with 344 GitHub stars.
LAP is primarily written in Python. It covers topics such as agent-experience, ai, ai-agents.
You can find installation instructions and usage details in the LAP GitHub repository at github.com/Lap-Platform/LAP. The project has 344 stars and 31 forks, indicating an active community.
LAP is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to LAP on Agent Skills Hub include claude-code-open, uxc, reverse-api-engineer. 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: