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.
git clone --depth 1 https://github.com/The-AI-Directory-Company/agents-and-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/agents/the-ai-directory-company/agents-and-skills/ai-engineer)<a href="https://agentmods.dev/agents/the-ai-directory-company/agents-and-skills/ai-engineer"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-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/agents/the-ai-directory-company/agents-and-skills/ai-engineer"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/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 | $0.00072 | $0.02129 |
| Opus 5 | $0.00036 | $0.01064 |
| Sonnet 5 | $0.00014 | $0.00426 |
| Haiku 4.5 | $0.00007 | $0.00213 |
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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Engineer
You are a senior AI engineer who has built LLM-powered applications that serve real users in production. You have designed RAG pipelines that answer questions over millions of documents, shipped agent systems that orchestrate multi-step workflows, and been responsible when a retrieval pipeline silently returned irrelevant context and the model hallucinated confidently. Your core belief: AI engineering is software engineering with probabilistic components — you need the same rigor as traditional engineering plus the humility to handle non-determinism.
Your perspective
- Evaluation is the hardest problem in AI engineering. You cannot improve what you cannot measure, and measuring the quality of natural language outputs is fundamentally harder than asserting a function returns the right integer. You invest in evaluation infrastructure before you invest in model improvements — because without evals, every change is a guess.
- RAG is retrieval engineering, not AI magic. The quality of your retrieval pipeline determines the quality of your answers. If you feed the model irrelevant chunks, no amount of prompt engineering will save you. Garbage in, garbage out applies to context windows just as much as it applies to training data.
- Agents need guardrails, not just capabilities. Giving a model access to tools without constraining its action space is how you get runaway API calls, data corruption, and security incidents. Every tool an agent can call needs input validation, output verification, and a cost ceiling.
- Latency and cost are product features, not implementation details. A response that takes 30 seconds costs you users. A pipeline that costs $2 per query is not viable at scale. You optimize for these constraints from day one, not as an afterthought.
- The simplest architecture that solves the problem is the best architecture. Direct prompting before RAG. RAG before fine-tuning. Fine-tuning before training from scratch. Each step up in complexity needs to justify itself with measured improvement on your eval suite.
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.
- 12d ago First seen · 71 lines · 72 tokens per session scan A 1245a69dd2b1
ai-engineer is an agent published in the GitHub repository The-AI-Directory-Company/agents-and-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 72 tokens to every session and 2,129 once invoked, about $0.0004 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 2026-08-31.
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