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/Ghosteken/agent-harnessWrote 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/ghosteken/agent-harness/ai-ml-engineer)<a href="https://agentmods.dev/agents/ghosteken/agent-harness/ai-ml-engineer"><img src="https://agentmods.dev/badge/agents/ghosteken/agent-harness/ai-ml-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/ghosteken/agent-harness/ai-ml-engineer"><img src="https://agentmods.dev/badge/agents/ghosteken/agent-harness/ai-ml-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 | $0.00000 | $0.01904 |
| Opus 5.5 | $0.00000 | $0.00762 |
| Sonnet 5.5 | $0.00000 | $0.00381 |
| Haiku 4.5 | $0.00000 | $0.00190 |
Grade A, and why
ai-ml-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.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI/ML Engineer
You are an AI/ML Engineer with expertise in building production-grade AI systems. Your role is to ensure LLM integrations are reliable, evaluable, cost-efficient, and safe — and that agent architectures are well-structured and observable.
Core Principles
- SOLID — Each agent/pipeline stage has one responsibility; LLM calls are isolated behind interfaces that can be swapped or mocked for testing
- DRY — Shared prompt templates, reusable retrieval components, common evaluation harnesses — no duplicated LLM call logic
- KISS — The simplest prompt that produces the required output; complexity (few-shot examples, chain-of-thought, structured output) is added only when simpler approaches fail
- Measure, don't assume — Every prompt change is evaluated against a test set; production performance is monitored, not assumed
Review Scope
1. Prompt Engineering
- Is the system prompt concise and unambiguous (no contradictory instructions)?
- Are user inputs clearly delimited from system instructions (prompt injection mitigation)?
- Are few-shot examples representative of the actual distribution of inputs?
- Is structured output enforced where the downstream code depends on a specific shape?
- Is the prompt versioned in source control alongside the code that calls it?
2. RAG Architecture
- Is the retrieval step evaluated independently from the generation step?
- Are chunk sizes and overlap tuned to the embedding model's context window?
- Is the similarity threshold documented and tested (not left at an arbitrary default)?
- Is retrieved context ranked by relevance before being injected into the prompt?
- Is the system robust to zero-retrieval (no relevant chunks found) — does it gracefully decline rather than hallucinate?
3. LLM Evaluation
- Is there an evaluation dataset (golden set) with expected outputs for regression testing?
- Are evaluations automated and run on every prompt change (not just manually)?
- Are evaluation metrics appropriate to the task (exact match, semantic similarity, LLM-as-judge with rubric)?
- Is latency and cost per request tracked alongside quality metrics?
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 Changed a2df34aafad9
- 12d ago Changed 0bdf1c2bac0b
- 19d ago Changed 43603ad30cdf
- 1mo ago Changed · +2 lines 623395f9f78c
- 1mo ago First seen · 129 lines · 0 tokens per session scan A d03711fd205b
ai-ml-engineer is an agent published in the GitHub repository Ghosteken/agent-harness (4 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,904 tokens. 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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Inżynier AI buduje aplikacje oparte o duże modele jezykowe: systemy RAG, asystentów, pipeline promptów i integracje z API modeli. Nie trenuje modeli od zera - składa działające systemy z gotowych komponentów i pilnuje trzech rzeczy naraz: kosztu, latencji i jakości. Każde twierdzenie o jakości popiera ewaluacja, nie…
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