agentleFS
jaron-goh-centrjaron-goh-centr/claude-skills/codex-build/SKILL.md

codex-build

Skill0 starsChanged 4 months agoNo licenceView on GitHub

Hand a frozen spec (PLAN.md or any locked plan) to OpenAI Codex to IMPLEMENT with full write access, while Codex stays the spec-writer and reviewer — the exact role-flip of /codex-review. Codex builds from the spec in a --yolo sandbox, Codex reads the full diff like a contributor PR, runs the proof test, and iterates fixes via the SAME Codex session up to MAX_FIX_ROUNDS before taking over. Human approves the diff before any commit. Use when the user says "/codex-build", "have codex build this", "codex implement the plan", "hand the plan to codex", "delegate the build to codex", or right after a plan survives /grill-me-codex, /grill-with-docs-codex, or /codex-review and they choose Codex for implementation (Act 3). Also for standalone delegation — refactors, mechanical migrations, bug fixes with a known repro, test/coverage writing — anything that reads as a work order. NOT for tiny edits (~<20 lines — delegation overhead loses), NOT for design work (if writing the spec forces decisions, that's /grill-me-codex first), NOT for reviewing existing code (/codex:review), and NOT for anything needing Codex-session tools (MCP, secrets, browser).

We split this skill into the rules it gives an agent

10 rules. About 0 are common, 4 are distinctive, and 6 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 6

    Very specific

    Tied to their project's names, files or tools. Probably skip.

No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

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Your trail

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