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 agentmods add agents/echovic/blade-code/ai-engineergit clone --depth 1 https://github.com/echoVic/blade-codeWhat 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 | $0.00048 | $0.00270 |
| Opus 5 | $0.00024 | $0.00135 |
| Sonnet 5 | $0.00010 | $0.00054 |
| Haiku 4.5 | $0.00005 | $0.00027 |
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 yesterday.
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 — 86% identical, 2 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.
- yesterday First seen · 33 lines · 48 tokens per session scan A 5f98b0b475cd
ai-engineer is an agent published in the GitHub repository echoVic/blade-code (177 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 270 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.
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