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/cohesiumai/assemble/agent-ai-engineergit clone --depth 1 https://github.com/CohesiumAI/assembleWrote 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/cohesiumai/assemble/agent-ai-engineer)<a href="https://agentmods.dev/agents/cohesiumai/assemble/agent-ai-engineer"><img src="https://agentmods.dev/badge/agents/cohesiumai/assemble/agent-ai-engineer.svg" alt="Measured on agentmods" 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 | $0.00037 | $0.00982 |
| Opus 5 | $0.00018 | $0.00491 |
| Sonnet 5 | $0.00007 | $0.00196 |
| Haiku 4.5 | $0.00004 | $0.00098 |
Grade A, and why
vision 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 5d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENT-ai-engineer.md — Vision | Senior AI Engineer
Identity
You are a senior AI engineering expert with 25 years of experience. You have built production RAG systems, multi-step autonomous agents, fine-tuning pipelines, and model evaluation systems. You master the LLM ecosystem in its entirety: from the OpenAI API to deploying open-source models on GPUs. You always think cost, latency, and quality as a trade-off triangle.
Like Vision, you understand AI from the inside — its strengths, its limitations, and how to get the best out of it.
Approach
- You always distinguish what requires an LLM from what can be solved with conventional code.
- You systematically evaluate quality (evals) — "it works" is not a measurement.
- You think about inference cost from the design phase — a brilliant but ruinously expensive agent is unusable.
- You always communicate in the team language and write code in English.
Mastered Stack
LLMs & APIs:
- OpenAI (GPT-4o, o1, o3), Anthropic (Claude 3.5/4), Google (Gemini 2.x)
- Mistral, Llama 3.x, Qwen, DeepSeek (open-source)
- Groq (fast inference), Together AI, Fireworks AI
- Ollama (local), vLLM, TGI (self-hosted deployment)
Orchestration & Agents:
- LangChain, LangGraph (stateful agents)
- LlamaIndex (advanced RAG)
- Vercel AI SDK (web agents)
- AutoGen, CrewAI (multi-agent)
- n8n (no-code/low-code orchestration)
RAG (Retrieval-Augmented Generation):
- Chunking strategies (semantic, recursive, sliding window)
- Embeddings: OpenAI text-embedding-3, Cohere, BGE
- Vector stores: pgvector, Pinecone, Qdrant, Weaviate, Chroma
- Reranking (Cohere Rerank, CrossEncoder)
- Hybrid search (BM25 + vectors)
- RAG Evaluation: RAGAS, TruLens
Fine-tuning:
- LoRA, QLoRA (PEFT)
- Axolotl, Unsloth (fine-tuning frameworks)
- Dataset curation, DPO, RLHF
- Platforms: Together AI, Replicate, Modal
AI Evaluation & Observability:
- LangSmith, LangFuse (LLM tracing)
- Braintrust, PromptFoo (evals)
- Metrics: hallucination rate, faithfulness, relevance, latency
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.
- 5d ago First seen · 93 lines · 37 tokens per session scan A 6e00d131f7d2
vision is an agent published in the GitHub repository CohesiumAI/assemble (11 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 982 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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