GPT -6 Astra için becerileri ve istemleri yeniden düşünmek

Coding agents have come a long way, and best practices are changing fast. With more capable models, what used to require a lot of handholding and scaffolding no longer does.

If you’ve been using agents like Codex for your projects over the last year, you’ve likely accumulated a lot of instructions as you worked to steer the models toward good outcomes. With each release, it’s been worth revisiting those assumptions, but with GPT-6 Astra, it’s more important than ever.

These instructions can take many forms: skills, AGENTS.md , and your task prompts are all shaping how the model gets work done.

These instructions can be in the form of skills, which are essentially prompts stored as Markdown files that can also be packaged with resources and bundled scripts. Generally, they are most useful for guidance around a specific workflow, or when using certain apps.

People now default to packaging a lot of skills into their projects, and each skill comes with a name and description that are loaded into the model’s context so it knows when to use them. But many descriptions are far too long, and when you add too many skills, Codex starts shortening their descriptions to fit.

The model ends up seeing less of each description, making it harder to know which skill to pick.

What’s worse is that descriptions can often contradict each other or over-emphasize when skills should be used, leading the model to load instructions that don’t actually help the task.

A common workflow to create skills is to use the $skill-creator skill. We recently updated its guidance to help mitigate many of the failure modes we’ve seen in practice.

First, skill descriptions should be as short as possible while making it clear when the model should use them:

Create and validate Postgres schema migrations. Use when working with databases, queries, models, or persistence.

Create and validate Postgres schema migrations. Use when adding or changing a migration, or reviewing its rollout.

Here, the bad skill description can push the model to use it anytime it touches anything related to a database, rather than only when it has to handle a migration.

Second, one of the key markers of a useful skill is progressive disclosure. Reading a skill takes up context, bringing you closer to compaction and introducing guidance that may not apply to the task.