GPT-6 Astra needs leaner prompts and fewer guardrails, OpenAI recommends
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. 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.Ad 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.Ad Provencher recommends keeping scope descriptions short and precise. A skill for Postgres schema migrations should only fire when creating or modifying a migration or when checking its rollout. If a skill covers multiple workflows, its main document should briefly point to the right supplementary docs and scripts, Provencher writes. That way the model only reads what it actually needs, since every extra read eats context and pushes it closer to summarization. Detailed step-by-step sequences can also slow down newer models, since they handle nuance and ambiguity better on their own, according to Provencher. But shared skills apply to every contributor’s agents, so teams need to be careful. What works for Sol or Luna might already be too restrictive for someone running Astra.Ad Mandatory reading before every change wastes context The rules in AGENTS.md that govern repository work need regular reviews too, Provencher says. Requiring the model to read multiple documents or a full project overview before every change is overkill for a typo fix.
