from trending-skills42
Orchestrate multi-step AI coding workflows as Graphviz DOT graphs with human approval gates, multi-model routing, and cloud sandboxes.
Fabro provides a CLI and runtime to define and run AI-driven workflows described as Graphviz DOT graphs. Workflows can include branching, loops, human-in-the-loop approval gates, model routing via stylesheet rules, sandboxed execution in cloud VMs, and automated retrospectives. It turns LLM turns into coordinated pipelines so agents execute complex tasks while humans intervene only when necessary.
Use Fabro when you need structured, repeatable AI workflows that combine planning, implementation, review, and testing stages; when you want human approval gates for safety; or when you need to run agent steps in isolated sandboxes for reproducibility and security. It's ideal for multi-model orchestration, automated test/fix loops, and long-running developer workflows.
Likely compatible with agent runtimes and CLI-driven LLM integrations such as Claude Code, Codex, and other LLM CLIs that can run shell installers or accept scripted prompts.
Documentation-only skill for Fabro, an AI coding workflow orchestrator using Graphviz DOT graphs. Well-structured SKILL.md with comprehensive examples covering multi-model routing, human approval gates, loops, parallel execution, cloud sandboxes, and REST API. No bundled scripts to test. Main security concern is the curl|bash install pattern. The skill reads as promotional documentation rather than a actionable agent skill — it teaches the tool but doesn't provide scripts or automation that an agent can directly execute.
This is essentially a documentation/readme skill, not an operational skill with scripts. It describes Fabro comprehensively but provides no automation layer. The curl|bash install is the only notable security finding. Well-written content but limited utility as an agent skill without scripts.
