ai-engineer

ai-engineer is an agent for Claude Code from xiaoyuge886/aigc. It costs 43 tokens per session (275 once invoked), scanned A, original, MIT.

A specialist agent for building applications that use large language models, retrieval-augmented generation, vector search, and automated AI workflows.

In plain words
What is it for?
Building chatbots, document-search systems, retrieval pipelines, prompt workflows, vector-database integrations, and multi-agent applications.
Why use it?
It provides a structured approach to designing these systems while considering failures, evaluation, token use, and operating cost.

Agent for Claude Code

Install

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.

agentmods
npx agentmods add agents/xiaoyuge886/aigc/ai-engineer
Clone the repo
git clone --depth 1 https://github.com/xiaoyuge886/aigc

Made for: Claude Code.

Wrote 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.

agentmods badge for ai-engineer

README.md
[![agentmods](https://agentmods.dev/badge/agents/xiaoyuge886/aigc/ai-engineer.svg)](https://agentmods.dev/agents/xiaoyuge886/aigc/ai-engineer)
Your own site
<a href="https://agentmods.dev/agents/xiaoyuge886/aigc/ai-engineer"><img src="https://agentmods.dev/badge/agents/xiaoyuge886/aigc/ai-engineer.svg" alt="Measured on agentmods" height="20"></a>
Per session 43 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 275 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00043 $0.00275
Opus 5 $0.00022 $0.00138
Sonnet 5 $0.00009 $0.00055
Haiku 4.5 $0.00004 $0.00028

Measured 4d ago against content hash 16b4f1532088, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 4d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.claude/agents/ai-engineer.md · 34 lines

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

  1. Start with simple prompts, iterate based on outputs
  2. Implement fallbacks for AI service failures
  3. Monitor token usage and costs
  4. Use structured outputs (JSON mode, function calling)
  5. 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.

Changes

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.

  1. 4d ago First seen · 34 lines · 43 tokens per session scan A 16b4f1532088

Subscribe to this mod's changes

ai-engineer is an agent published in the GitHub repository xiaoyuge886/aigc (197 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 275 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 2026-08-30.