Rethinking AI Agent Configs for OpenAI's Astra: Auditing Skills, AGENTS.md, and Prompts

OpenAI's New Guide for Astra

OpenAI recently published Rethinking skills and prompts for GPT-6 Astra , a guide that explains how to update agent configurations — AGENTS.md, Skills, and task prompts — when moving to the more capable Astra model. The core message: many rules accumulated for older models (mandatory full-doc reads, step-by-step confirmations, broad trigger keywords) may now be unnecessary or counterproductive.

Narrow Skill Trigger Conditions

The guide uses a Postgres migration Skill as an example. The original trigger — "when database, query, or data model is involved" — is too broad; a simple SQL question would fire the whole migration flow. The fix: restrict triggers to "creating, modifying, or reviewing a migration release." This aligns the Skill's description with the actual task it should perform.

Skills can also adopt progressive disclosure : store only name and description in SKILL.md metadata; load the full body, reference materials, or scripts only after the Skill is selected. This reduces context overhead and helps the model choose among similar Skills. If multiple Skills have overlapping descriptions like "must use for any development task," the model struggles to pick the right one.

Task-Specific Document Reads in AGENTS.md

Another example shows an AGENTS.md that required reading architecture, database, and deployment docs before any change. For a typo fix, that's overkill. The guide recommends listing each document with its specific trigger:

Read architecture.md when service boundaries are involved.

Read database.md when modifying table schemas.

Read deployment.md when preparing a deployment.

This keeps the document entry points but adds conditional reads. Business constraints and acceptance criteria should likewise be tied to concrete tasks (e.g., frontend changes must pass build; behavior changes must locate corresponding tests). Checks already enforced by formatters or CI only need their entry points documented.

Confirmation and Completion Criteria

The guide notes that Astra follows instructions more literally and may ask more clarifying questions or run extra tests on small tasks. Old rules that pause after a first implementation for review can cause the agent to wait indefinitely. The instruction must explicitly state whether to continue with local verification or stop for review. Test stopping conditions also need precision: deliver when required checks pass and no new changes/issues exist; if a required test cannot run due to environment, document the gap. Vague directives like "test a few more times" are insufficient.

Practical Audit: interview-guide Project

The author fed the official guide URL and a review prompt to Codex to audit their interview-guide project. Codex returned five actionable suggestions. One highlighted a rate-limiting rule discrepancy:

Rule text: "Each @RateLimit is an independent dimension, executed via separate Redis Lua sliding-window calls."

Actual code ( RateLimitAspect ): All dimensions on the same method are checked in a single Lua call; the script verifies every dimension first, then atomically decrements all counters only if all pass.

The mismatch matters: if the old rule were followed, a global quota could be decremented before an IP-quota rejection is discovered, leaking a global token. Codex suggested updating the rule to: "All rate-limit dimensions for the same method are checked in one Lua call; only if all pass are counters decremented atomically; any rejection leaves all dimensions unchanged." The author verified the difference across the rule file, aspect, Lua script, and tests.

Ready-to-Use Audit Prompt

The article provides a prompt you can send to Codex (or Astra) to audit your own configs:

根据这篇官方文章 https://developers.openai.com/blog/rethinking-skills-and-prompts-for-gpt-6-astra ，检查当前项目的 AGENTS.md 和相关 Skills。
找出触发条件过宽、指令重复或冲突、无条件读取资料、
不必要等待，以及任务完成标准不明确的地方。

每项建议给出文件位置、原文、适用场景和最小修改 diff。
保留必要的业务约束、权限要求和验收标准。
区分已有执行记录支持的问题和仅根据规则推测的问题。
本轮先给建议，不修改文件；没有依据的问题不用凑数。

After receiving suggestions, pick one backed by code evidence, apply the minimal diff, and validate with a familiar task.

Summary

As model capabilities grow, the guardrails we added to prevent mistakes deserve re-evaluation. Determine per task what the model now handles natively versus what still requires project-level constraints. Keep Skill triggers, material scope, and acceptance criteria in sync with both the project and the model. For further reading on Skill selection and deprecating obsolete Skills, the author references two prior articles: "19w+ Star! These Four God-Tier Vibe Coding Skills Are a Blast!" and "Goodbye Superpowers! Many Skills Can Really Be Thrown Away Now."