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 kandada · Agent Tool · ★ 174
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is fastclaw safe to install? View the security audit →
FastClaw A lightweight but powerful AI Agent framework in Python. PyPI Installation (Recommended) Configure Agent After starting, visit http://localhost:8765 , click Settings in the top right corner to configure mainagent, recommended: deepseek-chat. Or edit the config file: CLI Usage Git Clone Configure Agent After starting, visit http://localhost:8765 , click Settings in the top right corner to configure mainagent, recommended: deepseek-chat. Or edit the config file: CLI Usage Design Philosophy FastClaw is built on the core concept of event-driven agent orchestration. Key design principles: Graph-based Agent: Uses a directed graph to orchestrate agent behavior, s
| Stars | 174 |
| Forks | 26 |
| Language | Python |
| Category | Agent Tool |
| License | GPL-3.0 |
| Quality Score | 69.2747241110984/100 |
| Open Issues | 2 |
| Last Updated | 2026-08-30 |
| Created | 2026-03-27 |
| Platforms | python |
| Est. Tokens | ~14k |
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fastclaw is A python-based light but strong lobster. A lightweight but powerful AI Agent framework in Python, with multi-channel support, tool calling, cron scheduling and streaming output. It is categorized as a Agent Tool with 174 GitHub stars.
fastclaw is primarily written in Python.
You can find installation instructions and usage details in the fastclaw GitHub repository at github.com/kandada/fastclaw. The project has 174 stars and 26 forks, indicating an active community.
fastclaw is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to fastclaw on Agent Skills Hub include claude-code-skill-factory, agent-skills, claude-plugins. 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: