a recursive self-improving harness designed to help your agents (and future iterations of those agents) succeed on any task
autocontext is a self-improving harness for AI agents that runs a goal through evaluation, preserves useful lessons, and records artifacts for later runs. Developers use it to help agents and future versions of those agents perform better on recurring tasks. The catalogue add-ons support its agent-improvement workflow.
This repository also configures its own agents. See what autocontext tells them →
Skill Claude CodeCodex
Operational knowledge for the gridctf scenario including strategy playbook, lessons learned, and resource references. Use when generating, evaluating, coaching, or debugging gridctf strategies.
Skill Claude Code
Iterative strategy generation and evaluation system. Use when the user wants to evaluate agent output quality, run improvement loops, queue tasks for background evaluation, check run status, inspect runtime artifacts and session branch lineage, or discover available scenarios. Provides LLM-based judging with…
Skill Claude CodeCodex
Use when an agent needs to USE knowledge Autocontext already produced - find which scenarios have knowledge, read the playbook and lessons for one, understand the on-disk file and folder layout, and move knowledge between checkouts. Host-agnostic; requires only the autoctx CLI and the filesystem.
Skill Claude CodeCodex
Use when an agent needs to CREATE knowledge with Autocontext - run a scenario or plain-language task through the improvement loop, judge or improve a single output, and inspect what the run produced. Host-agnostic; requires only the autoctx CLI.
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: