Xenopus

Xenopus

Xenopus, just like the lab frog of the same name, is my experimental Claude + Codex workflow library for high-rigor engineering work. It collects the instructions, specialist agents, reusable skills, and portable config examples behind a deliberately heavyweight engineering posture: maximum quality and performance, with no concern for speed, latency, token cost, or compute cost.

This library is for teams and individuals who want principal-level, FAANG-scale review pressure in their local assistant workflows. It is slightly opinionated: inspect real sources, prefer native Claude/Codex surfaces, validate hard, and keep only durable workflow artifacts that earn their maintenance cost.

At a Glance

Surface	Claude target: Opus 4.7	Codex target: GPT 5.5
Canonical workflows	75	75
Custom agents	33	33
Skills	46	46

Repository Layout

LICENSE
README.md
Claude/
  Instructions/
    Opus 4.7/
      maximum-quality.md
      settings.example.json
  Agents/
    Opus 4.7/
      .md
  Skills/
    Opus 4.7/
      /
        SKILL.md
Codex/
  Instructions/
    GPT 5.5/
      AGENTS.md
      config.example.toml
      rules/
        default.rules
  Agents/
    GPT 5.5/
      .toml
  Skills/
    GPT 5.5/
      /
        SKILL.md
        agents/
          openai.yaml

How the Pieces Fit

Agents define delegated specialist roles. They are narrow, bounded lanes for implementation, review, research, and validation work where isolated context improves quality.

Config examples show the portable setup shape. They are intentionally examples, not drop-in credentials or machine-specific state. Review paths, permissions, plugins, MCP servers, connector settings, and environment variables before adapting them.

Instructions define the default behavior for a runtime. `maximum-quality.md` is the Claude output style for this stack, and `AGENTS.md` is the Codex-native instruction surfac