sujian666666com-glitch
sujian666666com-glitch/kk-skill
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/sujian666666com-glitch/kk-skill --skill artificial-intelligence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。
Discover and filter 15,330 The Graph subgraphs by domain, network, protocol type, or natural language goal. Each result includes an x402 query URL — $0.01 USDC on Base per call, no API key required.
Audit and reduce AI agent runtime spend in dollars. Use for AI costs, agent spend, token waste, runtime attribution, detector coverage, and FinOps. Works with OpenClaw, Hermes, QM, Claude Code, Cursor, and generic event ingest.
Use this skill whenever the user is troubleshooting a VMware/vSphere problem — a reported error, an exception, a log dump, a slow or failed VM, a host that went sideways — and needs help locating the root cause. It is the diagnostic brain of the VMware family: it drives a systematic investigation, pulls the right signals from the other skills, correlates events into one timeline, ranks root-cause hypotheses, and tells you what to check next even when you don't know where to start. Always use this skill for "diagnose this VMware issue", "why is my VM slow", "troubleshoot this vSphere error", "what does this log mean", "help me figure out what broke" when the context is explicitly VMware/vSphere/ESXi/NSX. It is READ-ONLY: it never changes anything. Do NOT use it to execute fixes — single fixes go to vmware-aiops, multi-step gated remediation goes to vmware-pilot. Do NOT use it for routine inventory or health checks with no problem to solve — use vmware-monitor.
基于 SOC 职业分类
正在显示 SKILL.md
| name | Artificial Intelligence |
| description | Answer AI questions with current info instead of outdated training data. |
| metadata | {"clawdbot":{"emoji":"🤖","os":["linux","darwin","win32"]}} |
Before answering questions about pricing, rankings, or availability:
openrouter.ai/models (aggregates all providers)lmarena.ai (crowdsourced ELO, updates weekly)Don't cite specific prices, context windows, or rate limits from memory — they change quarterly.
"How do I reduce hallucinations?" Not just "use RAG." Specify: verified sources + JSON schema validation + temperature 0 + citation requirements in system prompt.
"Should I fine-tune or use RAG?" RAG first, always. Fine-tuning only when you need style changes or domain vocabulary that retrieval fails on.
"What hardware for local models?" Give numbers: 7B = 8GB VRAM, 13B = 16GB, 70B = 48GB+. Quantization (Q4) halves requirements.
Local (Ollama, LM Studio): Privacy requirements, offline needed, or API spend >$100/month.
API: Need frontier capabilities, no GPU, or just prototyping.
~4 characters per token in English. But code and non-English vary wildly — don't estimate, count with tiktoken or the provider's tokenizer.