AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.
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 sickn33/agentic-awesome-skills --skill ai-engineergit clone --depth 1 https://github.com/sickn33/agentic-awesome-skillsWrote 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/sickn33/agentic-awesome-skills/ai-engineer)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/ai-engineer"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/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/sickn33/agentic-awesome-skills/ai-engineer"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/ai-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 33 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00018 | $0.00944 |
| Sonnet 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 7d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- ai-engineer — 100% identical, 0 lines differ
- ai-engineer — 97% identical, 2 lines differ
- ai-engineer — 97% identical, 2 lines differ
- ai-engineer — 97% identical, 2 lines differ
- ai-engineer — 97% identical, 0 lines differ
- ai-engineer — 97% identical, 2 lines differ
- ai-engineer — 97% identical, 2 lines differ
- ai-engineer — 97% identical, 2 lines differ
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.
- 7d ago First seen · 191 lines · 37 tokens per session scan A 8263c1d95fd2
ai-engineer is a skill published in the GitHub repository sickn33/agentic-awesome-skills (46,288 stars, last pushed 2d ago), licensed MIT. It adds 37 tokens to every session and 1,888 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
Other skills, from other repositories
rag-retrieval-audit
Diagnose missing evidence in a retrieval-augmented generation pipeline using labeled queries, chunk inspection, and retrieval metrics.
rag-builder
Professional Rag Builder skill. Integrate LLM API workflows, safe system prompt guidelines, and agentic workflows.
semantic-search
Professional Semantic Search Expert skill. Build performant, secure, and scalable backend logic and RESTful or GraphQL APIs.
vector-database
Professional Vector Database Expert skill. Design and maintain secure, optimized, and performant databases and data storage solutions.
ai-evaluation-dataset
Build a versioned JSONL evaluation dataset for an AI workflow, with acceptance criteria, held-out cases, and leakage checks.
llm-cost-latency-benchmark
Measure an AI workflow's observed token cost and end-to-end latency across representative cases with reproducible configuration.