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 37/100. no security policy. 27 open issues plus stale maintenance signal push this into high-risk adoption territory.
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.
Source: GitHub repository
Source check: August 30, 2026
Upstream commit: September 21, 2025
Repository state: Not marked archived
Honeystax upvotes are community interest signals, not star ratings. GitHub stars and repository health are source measurements; editorial risk and trial-cost notes are Honeystax analysis.