Overly long skill descriptions, blanket reading requirements, and rigid approval rules can get in GPT-6 Astra's way, according to OpenAI. The company recommends that developers tie instructions more tightly to specific tasks and define more clearly when the job is done.
Instructions that have piled up over time can eat up context or cause GPT-6 Astra to stop work too early, writes OpenAI's Eric Provencher. He recommends reviewing skills, AGENTS.md, and task prompts whenever switching models. More capable models need less hand-holding, according to Provencher. That lines up with earlier advice from OpenAI on model transitions.
Vague skill descriptions lead to wrong picks
Skills are prompts stored as Markdown files that can include resources and scripts. Provencher says they work best for specific workflows or applications. Their names and descriptions go into the model's context so Codex can pick the right skill for a given task.
Too many skills force Codex to truncate descriptions, stripping out information it needs to choose correctly. Conflicting descriptions or overly broad scope can also load instructions the model doesn't need, according to the blog post.
