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 IstiN · Agent Tool · ★ 75
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is flutter_agent_harness safe to install? View the security audit →
flutteragentharness Cross-platform AI agent harness for Dart and Flutter — streaming provider adapters, an agent loop with native tool calling, JSONL session persistence, context compaction, and a pi-like terminal coding agent (). Architecture ported from pi-mono ( + ), with a pure-Dart core that runs on the VM, Flutter desktop/mobile, and web. fa1.dev — the project website: a live in-browser demo of the full agent (sandboxed shell, git, interpreters — your key stays in page memory), the iOS public beta on TestFlight, the macOS app, and the CLI installer below. Design contract -
| Stars | 75 |
| Forks | 7 |
| Language | Dart |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 69.9714368113718/100 |
| Open Issues | 67 |
| Last Updated | 2026-10-13 |
| Created | 2026-07-13 |
| Est. Tokens | ~19k |
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flutter_agent_harness is Fa - Factory Agent. Cross-platform AI agent harness for Dart and Flutter — streaming providers, agent loop with tools, session persistence, compaction; backend agent mode: fa behind product Go backend. It is categorized as a Agent Tool with 75 GitHub stars.
flutter_agent_harness is primarily written in Dart. It covers topics such as ai, ai-agent, ai-agents.
You can find installation instructions and usage details in the flutter_agent_harness GitHub repository at github.com/IstiN/flutter_agent_harness. The project has 75 stars and 7 forks, indicating an active community.
flutter_agent_harness is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to flutter_agent_harness on Agent Skills Hub include AndroidHarness, javis-os, har. 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: