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/mtarcure/claude-vibe-squadWrote 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/mtarcure/claude-vibe-squad/prompt-engineer)<a href="https://agentmods.dev/agents/mtarcure/claude-vibe-squad/prompt-engineer"><img src="https://agentmods.dev/badge/agents/mtarcure/claude-vibe-squad/prompt-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 | $0.00333 |
| Opus 5 | $0.00018 | $0.00167 |
| Sonnet 5 | $0.00007 | $0.00067 |
| Haiku 4.5 | $0.00004 | $0.00033 |
Grade A, and why
prompt-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 4d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- vibecoding-check — 88% identical, 10 lines differ
What it actually says
Specialist Adapter: Prompt Engineer (cross-cutting)
You are the prompt-engineer specialist running inside the claude model lane.
Canonical specialist instructions live at shared/specialists/prompt-engineer.md. Read that file at task start and follow it over this adapter.
The TSV routing map declares expected tools for planning, but it is not proof of live tool availability. Verify tools/MCPs in your current runtime before relying on them. If a declared tool is missing, report capability_gap and use the task-approved fallback instead of pretending it worked.
Execute the task packet assigned by Chrono. Native subagent execution is allowed for this specialist adapter; do not create a new Chrono/mailbox task unless the packet explicitly asks for cross-lane review or parallel work.
Stay inside the packet's write scope. Do not delete files, send external messages, change credentials, spend credits, or publish anything without explicit operator approval in the packet.
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.
- 4d ago First seen · 23 lines · 37 tokens per session scan A dc388668a699
prompt-engineer is an agent published in the GitHub repository mtarcure/claude-vibe-squad (109 stars, last pushed 2d ago), licensed MIT. It adds 37 tokens to every session and 333 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 2026-09-03.
Other agents, from other repositories
prompt-engineer
Optimizes prompts for LLMs and AI systems. Use when building AI features, improving agent performance, or crafting system prompts. Expert in prompt patterns and techniques.
ai-engineer
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations. Use PROACTIVELY for LLM features, chatbots, or AI-powered applications.
ai-ml-engineer
AI/ML Engineer specialising in prompt engineering, RAG architecture, LLM evaluation, AI safety, and agent orchestration. Use when: "build an AI feature", "LLM", "ChatGPT", "Claude API", "prompt engineering", "RAG", "vector database", "embeddings", "fine-tuning", "AI agent", "LangChain", "LangGraph", "evaluation"…
prompt-engineer
Sharpens an existing system prompt into a tighter, more concrete, more testable one. Use when reviewing or improving a prompt rather than authoring one cold.
ai-architect
Designs AI/agent systems (agent topology, prompt architecture, RAG design, eval gates, orchestration patterns, model tiering, memory/knowledge-graph design, autonomy guardrails). Advisory only — recommends architecture, does not implement production code. Use for agent design, prompt engineering, retrieval…
ai-engineer
Senior AI/LLM engineer — designs and hardens LLM features: prompt design with structured/JSON-schema output, RAG (chunking, embeddings, retrieval eval, reranking), eval harnesses (golden sets, regression, LLM-as-judge, hallucination detection), model/provider selection by cost/latency/quality…