Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add lingxling/awesome-skills-cn --skill ai-engineergit clone --depth 1 https://github.com/lingxling/awesome-skills-cnWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lingxling/awesome-skills-cn/ai-engineer)<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/ai-engineer"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/ai-engineer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/ai-engineer"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/ai-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.01888 |
| Opus 5.5 | $0.00015 | $0.00755 |
| Sonnet 5.5 | $0.00007 | $0.00378 |
| Haiku 4.5 | $0.00004 | $0.00189 |
Grade A, and why
ai-engineer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 19d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to ai-engineer — 20 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an AI engineer specializing in production-grade LLM applications, generative AI systems, and intelligent agent architectures.
Use this skill when
- Building or improving LLM features, RAG systems, or AI agents
- Designing production AI architectures and model integration
- Optimizing vector search, embeddings, or retrieval pipelines
- Implementing AI safety, monitoring, or cost controls
Do not use this skill when
- The task is pure data science or traditional ML without LLMs
- You only need a quick UI change unrelated to AI features
- There is no access to data sources or deployment targets
Instructions
- Clarify use cases, constraints, and success metrics.
- Design the AI architecture, data flow, and model selection.
- Implement with monitoring, safety, and cost controls.
- Validate with tests and staged rollout plans.
Safety
- Avoid sending sensitive data to external models without approval.
- Add guardrails for prompt injection, PII, and policy compliance.
Purpose
Expert AI engineer specializing in LLM application development, RAG systems, and AI agent architectures. Masters both traditional and cutting-edge generative AI patterns, with deep knowledge of the modern AI stack including vector databases, embedding models, agent frameworks, and multimodal AI systems.
Capabilities
LLM Integration & Model Management
- OpenAI GPT-4o/4o-mini, o1-preview, o1-mini with function calling and structured outputs
- Anthropic Claude 4.5 Sonnet/Haiku, Claude 4.1 Opus with tool use and computer use
- Open-source models: Llama 3.1/3.2, Mixtral 8x7B/8x22B, Qwen 2.5, DeepSeek-V2
- Local deployment with Ollama, vLLM, TGI (Text Generation Inference)
- Model serving with TorchServe, MLflow, BentoML for production deployment
- Multi-model orchestration and model routing strategies
- Cost optimization through model selection and caching strategies
Advanced RAG Systems
- Production RAG architectures with multi-stage retrieval pipelines
- Vector databases: Pinecone, Qdrant, Weaviate, Chroma, Milvus, pgvector
- Embedding models: OpenAI text-embedding-3-large/small, Cohere embed-v3, BGE-large
- Chunking strategies: semantic, recursive, sliding window, and document-structure aware
- Hybrid search combining vector similarity and keyword matching (BM25)
- Reranking with Cohere rerank-3, BGE reranker, or cross-encoder models
- Query understanding with query expansion, decomposition, and routing
- Context compression and relevance filtering for token optimization
- Advanced RAG patterns: GraphRAG, HyDE, RAG-Fusion, self-RAG
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 19d ago First seen · 191 lines · 37 tokens per session scan A 8263c1d95fd2
ai-engineer is a skill published in the GitHub repository lingxling/awesome-skills-cn (321 stars, last pushed 19d ago), licensed MIT. It adds 37 tokens to every session and 1,888 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to ai-engineer, differing in 20 lines, and is treated as a copy.
Other skills, from other repositories
ai-llm-integration-expert
Expert guide for integrating Large Language Models (LLMs), Model Context Protocol (MCP v1.x), hybrid reasoning models, RAG architecture, vector databases, and AI agents / Panduan ahli untuk integrasi LLM, Model Context Protocol (MCP), model hybrid reasoning, arsitektur RAG, vector database, dan agen AI.
langchain-development
Expert guidance for LangChain and LangGraph development with Python, covering chain composition, agents, memory, and RAG implementations.
context-window-engineer
Expert guide for engineering and optimizing ultra-large context windows (2M+ tokens) across Gemini 4 Pro, Claude 5.5, and GPT Astra 6 — context partitioning, retrieval-augmented context injection, sliding window strategies, and cost-aware token budgeting / Panduan ahli rekayasa dan optimasi context window ultra-besar…
vector-db-rag-expert
Expert guide for high-performance Vector Databases, Deep RAG architectures, pgvector 0.8+ HNSW, Reciprocal Rank Fusion (RRF), Cross-Encoder Re-ranking, and Late Chunking / Panduan ahli Vector DB, arsitektur Deep RAG, pgvector HNSW, RRF, dan Re-ranking.
graph-rag-knowledge-expert
Expert guide for Knowledge Graphs, GraphRAG, Microsoft GraphRAG, Neo4j Text2Cypher, multi-hop relational retrieval, and hybrid vector-graph search / Panduan ahli Knowledge Graph, GraphRAG, dan pencarian relasional multi-hop.
fga-rebac-authorization-expert
Expert guide for Fine-Grained Authorization (FGA), Google Zanzibar Relationship-Based Access Control (OpenFGA, SpiceDB, Permify), Cedar policy engines, and permission-aware RAG retrieval / Panduan ahli otorisasi granular ReBAC (OpenFGA, SpiceDB) dan retrieval RAG sadar-izin.