Generate and iterate on a local codebase from a natural-language spec with gpt-engineer
Use gpt-engineer when an agent/operator needs to turn a prompt file into a local project scaffold, inspect the generated code, and run a supervised improvement loop before adopting the result.
npx skills add agentskillexchange/skills --skill generate-and-iterate-on-a-local-codebase-from-a-natural-language-spec-with-gpt-engineer
Use this skill to run a bounded spec-to-code workflow with gpt-engineer: create a project folder, write the requested behavior in a prompt file, run `gpte `, then review, test, and optionally iterate with `gpte -i`. Invoke it when the useful unit of work is a generated local codebase or a reviewable improvement pass, not when the user only needs normal IDE autocomplete or a managed app-builder product. Scope boundary: the skill is limited to local prompt-file driven code generation, inspection, and iteration; it is not a generic coding-agent framework listing or an endorsement to merge generated code without human review.
What this skill actually does
Inputs and prerequisites: Python, pip, OpenAI-compatible or supported model API key, gpte CLI.
Setup notes: Install with `python -m pip install gpt-engineer`; set an API key such as `OPENAI_API_KEY`; create a project folder with a `prompt` file; run `gpte ` or `gpte -i` for improvement mode.
Source and verification boundary: use https://gpt-engineer.readthedocs.io/en/latest/ as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.
Framework fit: publish this as a Multi-Framework workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.