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/nonlinear-xyz/factory-kitWrote 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/nonlinear-xyz/factory-kit/llm-workflow-engineer)<a href="https://agentmods.dev/agents/nonlinear-xyz/factory-kit/llm-workflow-engineer"><img src="https://agentmods.dev/badge/agents/nonlinear-xyz/factory-kit/llm-workflow-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/nonlinear-xyz/factory-kit/llm-workflow-engineer"><img src="https://agentmods.dev/badge/agents/nonlinear-xyz/factory-kit/llm-workflow-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 | $0.00102 | $0.00176 |
| Opus 5.5 | $0.00041 | $0.00070 |
| Sonnet 5.5 | $0.00020 | $0.00035 |
| Haiku 4.5 | $0.00010 | $0.00018 |
Grade A, and why
llm-workflow-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.
What it actually says
Apply the preloaded factory-llm-workflow-engineer skill to the delegated task. The canonical skill is the complete workflow and source of truth.
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 · 11 lines · 102 tokens per session scan A 312ebcc0075f
llm-workflow-engineer is an agent published in the GitHub repository nonlinear-xyz/factory-kit (9 stars, last pushed 7d ago), licensed MIT. It adds 102 tokens to every session and 176 once invoked, about $0.0004 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 2026-08-31.
Other agents, from other repositories
rag-specialist
Expert consultant for retrieval-augmented generation, knowledge bases, intelligent document processing, and semantic search systems. Use proactively for RAG architecture analysis, knowledge system design, document processing strategies, and semantic search optimization. Provides consultation and recommendations only …
vision
Senior AI Engineer — LLMs, autonomous agents, RAG, fine-tuning, model evaluation, AI integrations. Call to design and implement any artificial intelligence-based system.
AI Specialist
Expert in integrating LLMs, building RAG systems (pgvector), and creating MCP servers.
ai-engineer
An AI engineer who builds production applications powered by LLMs — designing RAG pipelines, agent architectures, tool use patterns, and evaluation frameworks. Distinct from ML engineer (who trains models) — focuses on integrating and orchestrating AI capabilities. Use for LLM application architecture, RAG design…
ai-ml-engineer
Expert in AI/ML pipelines, LLM integration, RAG systems, embeddings, and intelligent automation. Use for building AI-powered features, prompt engineering, and model integration. Triggers on ai, ml, machine learning, llm, gpt, embeddings, rag, vector, chatbot, agent.
rag-ops
An agent for operating a retrieval-augmented generation system, or RAG system, which lets an AI search stored information before answering. It manages indexing, collections, memory, and system health.