Safe Long-Run Mode (GPT-5.4 Only)
Use this skill when GPT-5.4 is the default operating model for both orchestration and delegated work.
Core rule
Use GPT-5.4 for long work by making tasks cheap, segmented, and resumable. Since the model layer is cost-efficient, the real risks are provider throttling, session interruption, and lack of checkpoints.
When to use this mode
Use it when:
- - the user wants to minimize model cost
- the task is implementation-heavy
- the task is file-heavy or repetitive
- multiple subagents may be involved
- external services may throttle
- quality depends more on process discipline than premium model nuance
Operating procedure
1. Route to GPT-5.4 by default
Use GPT-5.4 for:
- - coding
- docs
- research
- skills
- website work
- project tracker updates
- internal tooling
- multi-agent delegated work
- long build/test loops
Do not escalate to another model unless the user asks or the task clearly requires premium polish/judgment.
2. Split work aggressively
Break long tasks into explicit phases and write down the next step before moving on.
Preferred phases:
- 1. inspect
- plan
- execute
- validate
- report
3. Save progress continuously
Always leave artifacts that make recovery easy:
- - notes
- drafts
- partial outputs
- checkpoint files
- project updates
- result summaries
4. Use subagents as workers
For large or parallel tasks, use subagents to keep the main thread clean.
Delegate when:
- - tasks are independent
- multiple files or systems are involved
- work may take a while
- specialized roles improve throughput
5. Treat external APIs as the true bottleneck
In GPT-5.4-only mode, model cost is not the main concern. External limits are.
Be careful with:
- - Azure / Microsoft Graph
- ClawHub / GitHub-backed operations
- Orgo runtime and VM usage
- websites / browser automation
- messaging providers
Use batching, backoff, and fewer larger writes.
6. Make every task resumable
If interrupted, resume from artifacts instead of recreating work. Always know:
- - what is already done
- what file contains the latest state
- what exact next action should happen
Ideal GPT-5.4-only use cases
- - codebase changes
- documentation builds
- repeated content generation
- Azure script development
- internal automation
- multi-agent production work
- long back-office workflow creation
What to tell the user
Explain that GPT-5.4-only safe mode works because:
- - model cost stays low
- throughput stays high
- reliability comes from checkpoints, not from one giant run
- external APIs, not tokens, usually become the limiting factor
Failure handling
If interrupted:
- 1. summarize completed work
- cite the saved files
- state the resume point
- continue from the last checkpoint
References
- - Read
references/checklist.md for the pre-flight checklist and GPT-5.4 operating pattern.
安全长运行模式(仅限GPT-5.4)
当GPT-5.4是编排和委派工作的默认运行模型时,使用此技能。
核心规则
通过使任务廉价、分段和可恢复,使用GPT-5.4进行长时间工作。由于模型层成本效益高,真正的风险是提供商限流、会话中断和缺乏检查点。
何时使用此模式
在以下情况下使用:
- - 用户希望最小化模型成本
- 任务以实施为主
- 任务涉及大量文件或重复性工作
- 可能涉及多个子代理
- 外部服务可能限流
- 质量更多取决于流程纪律而非高级模型的细微差别
操作流程
1. 默认路由到GPT-5.4
使用GPT-5.4进行:
- - 编码
- 文档
- 研究
- 技能
- 网站工作
- 项目跟踪器更新
- 内部工具
- 多代理委派工作
- 长构建/测试循环
除非用户要求或任务明确需要高级润色/判断,否则不要升级到其他模型。
2. 积极拆分工作
将长任务分解为明确的阶段,并在继续之前写下下一步。
推荐阶段:
- 1. 检查
- 规划
- 执行
- 验证
- 报告
3. 持续保存进度
始终留下便于恢复的工件:
4. 使用子代理作为工作者
对于大型或并行任务,使用子代理保持主线程清晰。
在以下情况下委派:
- - 任务相互独立
- 涉及多个文件或系统
- 工作可能需要较长时间
- 专业角色可提高吞吐量
5. 将外部API视为真正的瓶颈
在仅GPT-5.4模式下,模型成本不是主要关注点。外部限制才是。
注意:
- - Azure / Microsoft Graph
- ClawHub / GitHub支持的操作
- Orgo运行时和VM使用
- 网站/浏览器自动化
- 消息提供商
使用批处理、退避和更少但更大的写入。
6. 使每个任务可恢复
如果中断,从工件恢复而非重新创建工作。始终知道:
- - 已完成的内容
- 哪个文件包含最新状态
- 下一步应执行的确切操作
理想的仅GPT-5.4使用场景
- - 代码库更改
- 文档构建
- 重复内容生成
- Azure脚本开发
- 内部自动化
- 多代理生产工作
- 长后台工作流创建
告知用户的内容
解释仅GPT-5.4安全模式有效的原因:
- - 模型成本保持低位
- 吞吐量保持高位
- 可靠性来自检查点,而非一次性大规模运行
- 外部API(而非令牌)通常是限制因素
故障处理
如果中断:
- 1. 总结已完成的工作
- 引用已保存的文件
- 说明恢复点
- 从最后一个检查点继续
参考
- - 阅读 references/checklist.md 获取预检清单和GPT-5.4操作模式。