菜单
Advanced prompt engineering — chain-of-thought, few-shot, tree-of-thought, self-consistency, meta-prompting, system design, debugging, and optimization for production AI systems. Use when working with prompt engineering.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/oyi77/1ai-skills --skill prompt-engineering命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Android and mobile application security testing — emulators, rooting, traffic interception, dynamic instrumentation. Use when testing mobile apps for vulnerabilities, reversing APKs, or bypassing security controls on Android.
Self-reflection + Self-criticism + Auto-learning from corrections + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when working with self improving.
Plan and execute a comprehensive red team engagement covering reconnaissance through post-exploitation using MITRE ATT&CK-aligned TTPs to evaluate an organization's detection and response capabilities. Use when working with conducting full scope red team engagement.
基于 SOC 职业分类
正在显示 SKILL.md
| name | prompt-engineering |
| description | Advanced prompt engineering — chain-of-thought, few-shot, tree-of-thought, self-consistency, meta-prompting, system design, debugging, and optimization for production AI systems. Use when working with prompt engineering. |
| domain | core |
| author | oyi77 |
| license | Apache-2.0 |
| subdomain | core-platform |
| tags | ["engineering","infrastructure","memory","prompt","self-improvement"] |
| version | 1.0.0 |
Trigger phrases:
"prompt engineering"
"Advanced prompt engineering — chain-of-thought, few-shot, tree-of-thought, self-"
LLM outputs are inconsistent or low quality
Complex reasoning tasks that need step-by-step thinking
Building reusable prompt templates for production systems
Optimizing prompts for cost (fewer tokens) or accuracy
Creating system prompts and custom instructions for AI agents
Debugging prompt performance issues
Designing multi-turn conversation flows
Prompt Engineering is a foundational core infrastructure skill that provides system foundation capabilities for the agent ecosystem.
| Rationalization | Reality |
|---|---|
| "I will add monitoring later" | Without monitoring, you cannot detect failures. Add it from day one. |
| "One model is enough" | Different tasks need different models. Route intelligently. |
| "Premature optimization" | Infrastructure decisions are hard to change later. Design for scale early. |
# Example: Model routing
ROUTES = {
"code": ["claude-sonnet-4-20250514", "gpt-4o"],
"vision": ["gemini-2.5-pro", "gpt-4o"],
"fast": ["gemini-2.5-flash", "gpt-4o-mini"],
}
def route_request(task: str, prompt: str):
models = ROUTES.get(task, ROUTES["fast"])
for model in models:
try:
return call_model(model, prompt)
except Exception:
continue
raise RuntimeError("All models failed")