failproofai

About failproofai

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agent-failure agent-harness agent-observability agent-reliability agent-tracing ai-agent claude-code codex enforcement evals

Quick Facts

Stars	1,398
Forks	297
Language	MDX
Category	Codex Skill
Quality Score	64.0027628923063/100
Open Issues	37
Last Updated	2026-08-21
Created	2026-04-06
Platforms	claude-code, codex
Est. Tokens	~17k

Compatible Skills

These tools work well together with failproofai for enhanced workflows:

failproofai — semantic(1.00)+rare_topics+same_lang+similar_pop+shared_platform (70%)
octogent — semantic(0.19)+complementary+rare_topics+same_lang+similar_pop+shared_platform (61%)
pi-mcp-adapter — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (59%)
claw-orchestrator — semantic(0.21)+complementary+same_lang+similar_pop+shared_platform (57%)
code-container — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (57%)

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Frequently Asked Questions

What is failproofai?

failproofai is Observability and enforcement for AI agent harnesses. Capture every run and runtime reliability with policy enforcement. 40 built-in policies, a local dashboard, no account required with a generous f. It is categorized as a Codex Skill with 1.4k GitHub stars.

What programming language is failproofai written in?

failproofai is primarily written in MDX. It covers topics such as agent-failure, agent-harness, agent-observability.

How do I install or use failproofai?

You can find installation instructions and usage details in the failproofai GitHub repository at github.com/FailproofAI/failproofai. The project has 1.4k stars and 297 forks, indicating an active community.

View on GitHub → Browse Codex Skill tools