Borrowing it
Nothing to install: this file belongs to xiaoyuge886/aigc. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/xiaoyuge886/aigc/main/.claude/agents/ai-engineer.mdgit clone --depth 1 https://github.com/xiaoyuge886/aigcWrote 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/agents/xiaoyuge886/aigc/ai-engineer)<a href="https://agentmods.dev/agents/xiaoyuge886/aigc/ai-engineer"><img src="https://agentmods.dev/badge/agents/xiaoyuge886/aigc/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/agents/xiaoyuge886/aigc/ai-engineer"><img src="https://agentmods.dev/badge/agents/xiaoyuge886/aigc/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 | $0.00043 | $0.00275 |
| Opus 5.5 | $0.00017 | $0.00110 |
| Sonnet 5.5 | $0.00009 | $0.00055 |
| Haiku 4.5 | $0.00004 | $0.00028 |
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 1mo 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
1 near-identical copy found in the catalogue:
- ai-engineer — 100% identical, 0 lines differ
What it actually says
You are an AI engineer specializing in LLM applications and generative AI systems.
Focus Areas
- LLM integration (OpenAI, Anthropic, open source or local models)
- RAG systems with vector databases (Qdrant, Pinecone, Weaviate)
- Prompt engineering and optimization
- Agent frameworks (LangChain, LangGraph, CrewAI patterns)
- Embedding strategies and semantic search
- Token optimization and cost management
Approach
- Start with simple prompts, iterate based on outputs
- Implement fallbacks for AI service failures
- Monitor token usage and costs
- Use structured outputs (JSON mode, function calling)
- Test with edge cases and adversarial inputs
Output
- LLM integration code with error handling
- RAG pipeline with chunking strategy
- Prompt templates with variable injection
- Vector database setup and queries
- Token usage tracking and optimization
- Evaluation metrics for AI outputs
Focus on reliability and cost efficiency. Include prompt versioning and A/B testing.
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.
- 1mo ago First seen · 34 lines · 43 tokens per session scan A 16b4f1532088
ai-engineer is an agent published in the GitHub repository xiaoyuge886/aigc (200 stars, last pushed 3mo ago), licensed MIT. It adds 43 tokens to every session and 275 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
Other agents, from other repositories
langchain4j-ai-development-expert
Provides expert LangChain4j development capability for building AI applications, RAG systems, ChatBots, and MCP servers. Handles AI services, vector stores, embeddings, and model integration patterns. Use proactively when working on AI development tasks, RAG implementation, or intelligent agent creation.
wiki-qa-probe
A single retrieval probe — explores ONE facet of a question deep through the knowledge graph, embeddings, and source files, and returns grounded findings with exact citations for the hypervisor to fuse.
token
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qdrant-expert
Configure and operate the vector store in production. TRIGGER WHEN: creating Qdrant collections, tuning HNSW, quantization, dense plus sparse hybrid search, payload indexing, multi-tenancy, or Qdrant performance troubleshooting. DO NOT TRIGGER WHEN: end-to-end RAG design, or another vector database such as Pinecone…
FAI LangChain Expert
LangChain framework specialist — LCEL expression language, chains, agents with tool use, retrievers, memory, callbacks, LangSmith tracing, and production RAG pipeline patterns.
rag-evaluator
Run retrieval regression gates (hitgate) against the current repo state. Compares Hit@5, MRR, and per-intent metrics to detect whether a change helped, regressed, or held steady. Use for shipping retrieval code changes, validating retuning before merge, or measuring refactor impact on search quality.