u/linegel

Who don't know, Grok Bot is actually part of Cursor Ultra

GitHub Copilot now has skills, custom agents, hooks, MCP servers and plugins. Grok's tooling has a similar stack. At some point, calling everything a skill stops being helpful.

My rough rule is to keep reusable procedure in a skill, hard policy in a hook, external access in MCP, and a separately scoped role in a custom agent. Use a plugin only when several pieces need to be installed together.

Does that match how you build these things, or is the boundary different in practice? I am especially interested in cases where you started with a skill and later split it up.

Reading: GitHub's customization guide | Grok's skills and plugins docs

A recent preprint called SWE-Skills-Bench ran real software engineering tasks with and without skills. It reports that 39 of 49 tested skills produced no pass-rate improvement. A few focused skills helped a lot, and some just added token overhead.

It is one benchmark, so I would not treat it as the final word. Still, it raises a fair question: what evidence should a skill author publish before saying a skill works?

My minimum would be the exact model and harness, a paired run without the skill, token and time cost, plus at least one task where the skill should not fire. What would you add or remove?

Paper: SWE-Skills-Bench

One interesting side effect of banked Codex resets is that the economics change on workflows you would normally skip. If your usage window is about to reset and you still have quota left, or you have a banked reset available, an expensive experiment no longer competes with your normal work in quite the same way.

img2threejs is a great example. It rebuilds a reference image as editable procedural Three.js code, then works through gated passes for blockout, structure, form, materials, lighting, interaction and optimization. Its own cost notes estimate about 80k to 180k model tokens for one object, or 150k to 350k for a character. Those are engineering estimates, not measured benchmarks.

That is hard to justify for routine experimentation. It can make perfect sense when the budget would otherwise go unused. A recent Codex user reported spending roughly half a day iterating and getting a result they considered more polished than what they had managed with Blender MCP. The live gallery helps explain the trade: the output is editable, code-native and animation-ready.

Whether it arrives as a standalone skill or inside a plugin, this is the kind of workflow that can turn spare quota into something you would not normally attempt. What else belongs in this category?

Links: img2threejs | token cost notes | live gallery | recent Codex experiment | OpenAI Codex plan notes