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 changkun · Codex Skill · ★ 107
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is wallfacer safe to install? View the security audit →
Wallfacer Full autonomy when you trust it. Full control when you don't. Wallfacer is an autonomous engineering platform that works across multiple levels of abstraction. Start with a conversation when you're exploring an idea. Move to specs when the shape becomes clear. Track tasks when it's time to execute. Drop into code when you need precision. Agents operate at every level, and you decide how much freedom they get. Open source. Runs locally. No IDE lock-in. No cloud dependency. Bring your own LLM provider. Why Wallfacer Every AI coding tool today pins
| Stars | 107 |
| Forks | 13 |
| Language | Go |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 64.3543526320398/100 |
| Last Updated | 2026-09-22 |
| Created | 2026-02-16 |
| Platforms | claude-code, codex, go |
| Est. Tokens | ~18k |
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wallfacer is Chat, specs, tasks, and code. An autonomous engineering platform. Full autonomy when you trust it. Full control when you don't.. It is categorized as a Codex Skill with 107 GitHub stars.
wallfacer is primarily written in Go. It covers topics such as ai-agents, autonomous-coding, autonomous-engineering.
You can find installation instructions and usage details in the wallfacer GitHub repository at github.com/changkun/wallfacer. The project has 107 stars and 13 forks, indicating an active community.
wallfacer is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to wallfacer on Agent Skills Hub include plandeck, sandboxed.sh, hcom. 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: