wallfacer: Chat, specs, tasks, and code

by changkun · Codex Skill · ★ 113

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 63/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-agentsautonomous-codingautonomous-engineeringclaude-codecodexcoding-agentsdeveloper-toolsgolangkanbanllm

Quick Facts

Stars113
Forks14
LanguageGo
CategoryCodex Skill
LicenseMIT
Quality Score63/100
Last Updated2026-10-31
Created2026-02-16
Platformsclaude-code, codex, go
Est. Tokens~18k

About wallfacer

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.

Compatible Skills

These tools work well together with wallfacer for enhanced workflows:

  • pilot — semantic(0.29)+complementary+same_lang+similar_pop+shared_platform (55%)
  • tingly-box — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (53%)

wallfacer alternative? Top 6 similar tools

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  • turbo-flow by marcuspat · ⭐ 185

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

View on GitHub → Browse Codex Skill tools