Codex Grade Coding

Turn your AI agent into a senior engineer with strict task classification and verification-driven coding protocols.

Install
cmdop skills install agensi-codex-grade-coding

Codex Grade Coding is a skill published by agensi that imposes a structured engineering protocol on AI agents. Rather than generating code immediately, the skill requires the agent to first classify the incoming task into one of four tiers — Trivial, Standard, Risky, or Review — and then select an appropriate step on a Verification Ladder before any change is made. This classification step is the core mechanism that prevents drift and over-coding, two common failure modes where an agent makes unnecessary refactors or introduces bugs through unsolicited cleanup.

For bug fixes, the protocol mandates that the agent reproduce the defect before applying a patch. For refactoring tasks, it requires a proof that observable behavior remains unchanged after the change. Code review workflows produce findings ordered by correctness risk and regression likelihood. The skill also ships a benchmarking rubric that scores agent behavior on scope discipline and hallucination control, giving teams a repeatable way to measure engineering quality over time.

All outputs conform to a Final Answer Contract format, which documents what was changed and the rationale, making human review straightforward. The skill is described as particularly effective for raising the output quality of smaller or less reliable models by providing a consistent, repeatable engineering bar. There are no environment variables, no package registry entries, and no exposed tools beyond the behavioral protocol the skill applies.

Use cases

  • Enforce reproduction steps before an agent applies a bug fix
  • Require behavioral-equivalence proofs during agent-driven refactors
  • Prioritize code review findings by correctness and regression risk
  • Score agent outputs against a scope-discipline and hallucination-control rubric
  • Constrain smaller models to senior-engineer-level change discipline
  • Produce a Final Answer Contract that documents every change and its rationale

When to use it

  • When an agent is producing overly broad changes or unsolicited refactors in a complex codebase
  • When the team needs a repeatable, auditable record of what an agent changed and why
  • When using a smaller or less reliable model that benefits from a strict operational framework
  • When running benchmarks to measure agent scope discipline and hallucination rate

When not to use it

  • When the task requires direct database, filesystem, or API tool calls — this skill exposes no tools
  • When the workflow needs MCP server integrations or transport-level connections
  • When rapid, unconstrained code generation is preferred over verified, scope-limited output