HOW TO USE CODEX BEYOND THE FIRST PROMPT

Short summaryMost AI demos stop when something appears on screen. This one starts there.Dalton Anderson uses OpenAI Codex to turn fictional customer feedback into a working dashboard, then critiques the first output, steers the redesign, and explains the system around reliable agent work: Plan as the map, Goal as the contract, skills as reusable procedures, and evaluations plus stop conditions for agentic loops.A practical episode for founders, operators, and builders who want better results from Codex without giving up judgment.### Mobile-first show notesMost AI demos stop when something appears on screen. This one starts there.Dalton gives Codex a fictional customer-feedback dataset and asks for a decision-ready dashboard. The first output works, but it is not good enough. That becomes the real lesson.This episode shows how to move from a one-shot prompt to a workflow you can inspect, steer, and verify.You will learn:- Why Plan is the map and Goal is the contract- The four parts of a strong goal: outcome, context, constraints, and done-when evidence- When a repeatable workflow should become a skill- How clear names keep a growing skill library usable- Why every agentic loop needs an evaluation and a hard stop- Where human judgment still mattersThe demonstration uses fictional data. No outreach is sent.## Official OpenAI resources- [Codex use cases](https://developers.openai.com/codex/use-cases)- [Build skills](https://learn.chatgpt.com/docs/build-skills)- [Build plugins](https://learn.chatgpt.com/docs/build-plugins)- [Subagents](https://learn.chatgpt.com/docs/agent-configuration/subagents)- [AGENTS.md](https://learn.chatgpt.com/docs/agent-configuration/agents-md)- [Follow a goal](https://learn.chatgpt.com/use-cases/follow-goals)- [Scheduled tasks](https://learn.chatgpt.com/docs/automations)- [Git worktrees](https://learn.chatgpt.com/docs/environments/git-worktrees)- [OpenAI Codex repository](https://github.com/openai/codex)- [OpenAI Plugins repository](https://github.com/openai/plugins)Note: the older [openai/skills repository](https://github.com/openai/skills) is deprecated and now directs readers to OpenAI Plugins.## Skill repositories worth exploring- [Anthropic Skills](https://github.com/anthropics/skills): official Claude skill examples and templates- [Superpowers](https://github.com/obra/superpowers): a cross-agent software development workflow and skill collection- [Microsoft Skills](https://github.com/microsoft/skills): skills and custom agents for Microsoft developer workflows- [Microsoft Learn Agent Skills](https://github.com/MicrosoftDocs/Agent-Skills): Microsoft and Azure skills grounded in Learn documentation- [Gemini CLI](https://github.com/google-gemini/gemini-cli): Google's open-source coding agent with Agent Skills support- [Gemini CLI Agent Skills guide](https://geminicli.com/docs/cli/using-agent-skills/)- [Agent Skills specification](https://github.com/agentskills/agentskills): the open format behind portable skills- [Vercel Skills](https://github.com/vercel-labs/skills): a cross-agent CLI for discovering, installing, and sharing skillsInstall selectively. Read a skill before trusting it, understand the tools and permissions it can use, and test it on bounded work first.## Chapters00:00 Why this episode exists01:45 Turning fictional feedback into a dashboard02:29 Commands, context, compact, goals, and Plan04:16 What the first plan is doing07:05 Steering the build with butter yellow09:03 Reviewing the first dashboard10:37 Plan is the map, Goal drives the work15:54 An honest review of the redesign17:23 Building a Goal and the four-part prompt formula21:08 Commands, skills, and reusable workflows24:57 Naming skills so they stay usable26:34 Broad threads and focused projects28:27 A simple context-and-constraints analogy30:52 Keeping agentic loops safe32:21 Closing