Credentials. The run needs your model key in the environment, and the agent setup wires into whichever CLIs you have installed.
Who it is forA developer who maintains a shared skill or prompt for Claude Code or Codex and wants it tuned against examples without leaving the agent.
The problemIterating on a skill file by hand inside an agent means guessing, rerunning, and losing track of what changed.
The outcomeA detached evolution run, status checks from the agent, and an evolved skill that is written back only after you approve.
What you need first
- Python 3.9 or newer
- pip to install the agentdescent package from PyPI
- For model-backed runs: an OpenAI-compatible endpoint, its base URL, and an API key set in environment variables (OPENAI_BASE_URL and OPENAI_API_KEY)
- For agent-plugin use: Claude Code or Codex installed, and git to clone the repo
Steps
git clone https://github.com/Birfy/agentdescent && cd agentdescentbash scripts/setup-hosts.shagentdescent doctor- In the agent, say "improve this prompt against these examples" and point it at your example file.
- Review the call count it quotes, approve the run, then approve or reject the write-back.
Prompt for your agent
Clone https://github.com/Birfy/agentdescent, run `bash scripts/setup-hosts.sh`, then ask the agent to improve <path to skill or prompt file> against <path to examples file>. Expected output: a call count, a status report, and a proposed evolved version. Verify that nothing is written back to the target file until the user confirms.
Caveat. The plugin setup is documented from the current main checkout, so the install path may differ from the PyPI release.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for Birfy/agentdescent and "Improve an agent skill from inside Claude Code"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
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