Repeatable procedures your AI agent runs with judgment. Every run writes a trace. The agent gets better over time.
An agent is a folder of markdown files that an AI runtime follows. It has steps, a setup command, and a runs/ folder where every execution writes its trace. The agent reads past runs before it starts a new one, so it makes better decisions each time.
This is not a prompt chain or a static runbook. The steps give the agent room for judgment: they say what to accomplish and when the step is done, not which API call to make. The result is a procedure that works across different systems because the agent adapts it.
Every agent declares two kinds of inputs:
The first run starts from the procedure alone. It syncs data, operates, and writes what happened into runs/1/. The second run reads that trace before it begins. If the first run discovered a retry pattern, the second run knows about it and applies it.
Playbooks, decision logs, and checklists accumulate inside the module. The agent consults them before every step. Nothing is retrained, no model is fine-tuned. The context is just files, and the agent reads them.
Every agent follows the same structure. A manifest, numbered steps, a place for runs, and optional commands and functions.
Manifest with metadata, description, parameters, and connection requirements.
Numbered markdown files the agent follows, each with a clear objective and done condition.
Traces from completed runs. The agent reads past runs to make better decisions.
Setup interview and user-invokable actions. The setup command personalizes the agent.
Functions, playbooks, checklists, templates. Add whatever folders the agent needs. The structure is yours.
One command. The agent lands in your instance's modules/ folder, ready for the setup interview.
Every agent is a plain folder of markdown files. Download it from the agent page and drop it into any instance. No lock-in, no runtime dependency.
Explore browser actions with AI, then crystallize them into reusable Python scripts. Recipes self-heal: when a script fails, the agent falls back to live exploration and updates the recipe.
A release agent that collects changes, drafts a changelog, bumps the version, publishes, and optionally announces. Each release is a searchable run; the agent learns your changelog style and known blockers over time.
Research keywords, discover content opportunities, and build a prioritized list of content ideas. Each run explores a seed topic and produces actionable suggestions, not finished articles.