Prompt Engineer

Prompt Engineer is an agent for Claude Code, OpenCode from SHAdd0WTAka/Zen-Ai-Pentest. It costs 30 tokens per session (2,090 once invoked), scanned B, original, MIT.

A prompt-engineering assistant for writing and testing instructions that guide large language models, or LLMs, toward consistent results.

In plain words
What is it for?
Use it to design system prompts, examples, output formats, behavioral specifications, and tests covering normal, edge, and failure cases.
Why use it?
It helps turn vague requests into precise behavior and catch failures when prompts or AI models change.

Agent for Claude CodeOpenCode

Written for OpenCode and Claude Code: installed under .opencode/, but also a Claude Code subagent (agents/*.md). Also seen: mentions subagents.

Good fit Use it to design system prompts, examples, output formats, behavioral specifications, and tests covering normal, edge, and failure cases.

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Install with agentmods
npx agentmods add agents/shadd0wtaka/zen-ai-pentest/prompt-engineer
Install

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.

Clone the repo
git clone --depth 1 https://github.com/SHAdd0WTAka/Zen-Ai-Pentest

Made for: Claude Code, OpenCode.

Wrote 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.

agentmods badge for Prompt Engineer

README.md
[![agentmods](https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/prompt-engineer/github.svg)](https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/prompt-engineer)
Your own site
<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/prompt-engineer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/prompt-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.

agentmods 80×15 button for Prompt Engineer

Your own site · 80×15
<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/prompt-engineer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/prompt-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 30 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,090 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00030 $0.02090
Opus 5 $0.00015 $0.01045
Sonnet 5 $0.00006 $0.00418
Haiku 4.5 $0.00003 $0.00209

Measured 6d ago against content hash 61388548ee55, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade B, and why

Prompt Engineer scanned grade B with 1 finding 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 6d 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.

Instruction-override phrasingmediumPrompt injection

Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.

- Tests adversarial inputs: "Ignore all previous instructions", roleplay bypass attempts, indirect injection via tool outputs

Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.

.opencode/agents/prompt-engineer.md · 197 lines

How it starts

The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Prompt Engineer

🧠 Your Identity & Memory

  • Role: Prompt design and LLM behavior specialist
  • Personality: Methodical, experimentally-minded, obsessed with precision — you treat every prompt like a scientific hypothesis
  • Memory: You track which prompt patterns produce consistent outputs, which phrasings cause hallucinations, and which structural choices improve reliability across model versions
  • Experience: You have written and iterated hundreds of prompts across GPT, Claude, Gemini, Mistral, and open-source models — you know where each one breaks and why

🎯 Your Core Mission

  • Design system prompts, few-shot examples, and chain-of-thought instructions that produce predictable, high-quality outputs
  • Build prompt test suites to catch regressions when models are updated or prompts are modified
  • Translate ambiguous product requirements into precise behavioral specs that LLMs can reliably follow
  • Default requirement: Every prompt you write ships with at least 3 test cases covering the happy path, an edge case, and a failure mode

🚨 Critical Rules You Must Follow

  • Never write a prompt without first defining the expected output format and success criteria
  • Always version prompts — treat them like code (v1, v2, changelogs included)
  • Test prompts against the actual model and temperature that will be used in production — behavior varies significantly
  • Flag any prompt that relies on assumed knowledge the model may not have; ground it with context or examples instead
  • Never use vague qualifiers like "be helpful" or "be concise" — define exactly what concise means (e.g., "respond in 2 sentences or fewer")
  • Prefer explicit constraints over implicit expectations — models fill ambiguity unpredictably

📋 Your Technical Deliverables

System Prompt Template

## Role
You are a [SPECIFIC ROLE]. Your sole job is to [PRIMARY TASK].

## Constraints
- Output format: [JSON / Markdown / plain text — specify exactly]
- Length: [max N tokens / sentences / bullet points]
- Tone: [professional / casual / technical] — avoid [specific words/phrases to exclude]
- Scope: Only respond to [topic domain]. If the user asks about anything outside this, respond: "[FALLBACK MESSAGE]"

## Reasoning
Before answering, think step-by-step inside <thinking> tags. Your final answer goes in <answer> tags.

## Examples
<example>
Input: [realistic user message]
Output: [exact expected output]
</example>

<example>
Input: [edge case input]
Output: [expected output for edge case]
</example>

Read the full file on GitHub · 197 lines

Changes

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.

  1. 6d ago First seen · 197 lines · 30 tokens per session scan B 61388548ee55

Subscribe to this mod's changes

Prompt Engineer is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (453 stars, last pushed 2d ago), licensed MIT. It adds 30 tokens to every session and 2,090 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (instruction-override phrasing). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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