GPT-6 Astra: Guide to Cleaning Up Skills and AGENTS.md Instructions

One-sentence conclusion

This guide helps teams trim verbose instructions accumulated for older models, enabling GPT-6 Astra to choose Skills more accurately, control test scope, and continue completing long-running tasks.

Applicable scenarios

• Suitable tasks: Maintaining repository-level AGENTS.md files in Codex, streamlining Skill descriptions, and defining completion criteria and safety boundaries for long-running engineering tasks.

• Unsuitable tasks: Removing all constraints indiscriminately, or automatically rewriting production safety rules without project knowledge.

• Applicable model versions: GPT-6 Astra; the article also notes that instructions in shared repositories may still be read by other models such as Sol and Luna.

• Applicable clients, Agents, or APIs: Codex and any Agent harness that loads AGENTS.md, Skill files, or long-lived system instructions.

• Recommended reasoning effort and parameters: The article provides no universal effort level or API parameters; choose them separately based on task risk and verification cost.

Directly usable content

The original only makes one boundary prompt available for verbatim reuse; the rest is methodology and should not be packaged as a complete system prompt.

The local tests use disposable fixtures and have no production access. Run them, fix failures caused by the requested change, and rerun affected tests without asking for approval at each step.

Reusable workflow

1. List the currently active Skills, AGENTS.md files, nested instructions, and task prompts.

2. Shorten each Skill's description to retain only “when to use”; remove wording that conflicts with other Skills or excessively competes for triggering opportunities.

3. For Skills containing multiple workflows, use progressive disclosure: keep the root file as a minimal router and place details in documents or scripts read on demand.

4. Review AGENTS.md line by line: confirm that each rule is necessary for all tasks, avoiding requirements to read a complete repository map or full documentation set even for correcting a typo.

5. Calibrate verification rules. Astra will proactively test by default, so old instructions to “check repeatedly” may lead to over-verification.

6. State authorization boundaries for safe local workflows, and define completion criteria, the scope to explore, and stopping points before the task begins.

7. Validate with real tasks: record the context read, number of pauses, test scope, degree of completion, and amount of human intervention, then decide whether to trim further.

Scope boundaries and original observations

• This is an experience-based guide from a Codex DX practitioner, not a controlled A/B test; the article does not disclose token, success-rate, or task-duration comparisons.

• “Fewer Skills” and “less scaffolding” are applicable to Astra's stronger instruction-understanding capabilities, but shared repositories still need to account for other models.

• Streamlining does not mean removing permissions and safety boundaries. The original emphasizes restating boundaries so Astra can continue within the authorized scope rather than overstepping when uncertain.

• The article notes that Astra often tests more thoroughly but is more cautious about how far a task should be advanced; therefore, test scope and completion criteria should be calibrated separately.