What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of...
Copy the install, test the workflow, then decide if it earns a permanent slot.
The signal is softer here. Treat it like a pattern source unless it solves a very specific gap.
Copy the install, test the workflow, then decide if it earns a permanent slot.
Reasonable to try, but it will take more than a quick skim to get real signal.
GitHub health unknown. no security policy. 12 open issues make this testable, but not something to trust blind.
AI Agent
Universal
Model
Multiple
Fastest way to find out if 12-factor-agents belongs in your setup.
Copy the install command, run a real test, and back it out cleanly if it slows you down.
git clone https://github.com/humanlayer/12-factor-agents ~/.claude/agents/12-factor-agentsRun this first. You will know quickly if the workflow earns a permanent slot.
rm -rf ~/.claude/agents/12-factor-agentsNo messy cleanup loop. If it misses, remove it and keep moving.
Install Location
~/ └─ .claude/ ├─ commands/ ├─ agents/ │ └─ 12-factor-agents/ ← installs here └─ settings.json
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?. An open-source agent for the AI coding ecosystem.