I replaced a fairly complex Reddit research agent with a Codex skill. I'm starting to think many "agents" should just be skills.

I've been looking through a number of research-agent projects recently, Most of them can be simply replaced with tools like codex. In today's age it's a fact that a capable harness like Codex already has reasoning, web access, tool execution, filesystem access and an interactive conversation. But people are like, "Show me the code". So I tried taking the workflow of a reasonably complex Reddit customer-research agent and implementing the use case as a Codex skill instead. It researches Reddit for customer pain points, verifies relevant communities, collects evidence, clusters problems, analyzes commercial signals and generates structured artifacts. There is also a human approval checkpoint before the main research starts. The (only) interesting part here to me is what I didn't have to build: no separate agent loop/runtime no separate LLM client no nested agents no custom browsing/search layer no dedicated UI no separate framework just to orchestrate the research The skill defines the research methodology and workflow. Codex provides the harness. I kept small Python helpers only where deterministic behavior matters: validation, scoring, canonical URLs, deduplication and artifact generation. So the architecture is basically: Codex harness → SKILL.md workflow → deterministic helpers where needed rather than: custom agent → model integration → tools → search → state → UI → orchestration → report generation There's also a useful side effect: the workflow doesn't end when the "research agent" returns its report. Because it's running inside Codex, I can continue the same conversation and ask it to investigate one finding further, challenge an assumption, modify the analysis, or start building something from the result. Codex also now has $skill-creator, so if you already have a working workflow you can ask it to turn that workflow/current chat into a reusable skill instead of manually creating everything from scratch. (That's what I did here) I'm increasingly thinking this should be the default question before building a specialized research agent: Does this use case really require a new agent runtime, or does it just require a domain-specific skill running inside an existing harness? Obviously there are cases where a custom agent/runtime is justified — especially when deployment model, independent execution, custom integrations, control boundaries or product UX are themselves requirements. But for most of the "research agent" projects, I'm not convinced they are.