pydantic-ai-prompt-engineer

pydantic-ai-prompt-engineer is an agent for Claude Code from coleam00/context-engineering-intro. It costs 48 tokens per session (1,799 once invoked), scanned A, original, MIT.

A system-prompt writing add-on for Pydantic AI agents, where a system prompt defines an agent’s role and behavior. It creates short, clear prompts from the requirements document.

In plain words
What is it for?
Use it after requirements planning to define the agent’s role, essential behavior, and response guidelines in a focused system prompt.
Why use it?
It turns planned requirements into instructions the agent can follow without adding unnecessary rules or complexity.

Agent for Claude Code

Written for Claude Code: installed under .claude/. Also seen: mentions Claude Code.

Good fit Use it after requirements planning to define the agent’s role, essential behavior, and response guidelines in a focused system prompt.

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Install with agentmods
npx agentmods add agents/coleam00/context-engineering-intro/pydantic-ai-prompt-engineer
About the project

Context Engineering Template is a repository of instructions, examples, workflows, and validation practices that give AI coding assistants the information they need to complete software tasks. It is for developers working with Claude Code or other coding assistants, and the catalogue entries package parts of its workflow as commands, agents, instructions, and a skill.

coleam00/context-engineering-intro · 13,825 stars · on GitHub

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/coleam00/context-engineering-intro

Made for: Claude Code.

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 pydantic-ai-prompt-engineer

README.md
[![agentmods](https://agentmods.dev/badge/agents/coleam00/context-engineering-intro/pydantic-ai-prompt-engineer/github.svg)](https://agentmods.dev/agents/coleam00/context-engineering-intro/pydantic-ai-prompt-engineer)
Your own site
<a href="https://agentmods.dev/agents/coleam00/context-engineering-intro/pydantic-ai-prompt-engineer"><img src="https://agentmods.dev/badge/agents/coleam00/context-engineering-intro/pydantic-ai-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 pydantic-ai-prompt-engineer

Your own site · 80×15
<a href="https://agentmods.dev/agents/coleam00/context-engineering-intro/pydantic-ai-prompt-engineer"><img src="https://agentmods.dev/badge/agents/coleam00/context-engineering-intro/pydantic-ai-prompt-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 48 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,799 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. 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 $0.00048 $0.01799
Opus 5 $0.00024 $0.00899
Sonnet 5 $0.00010 $0.00360
Haiku 4.5 $0.00005 $0.00180

Measured 10d ago against content hash 3d3d87770033, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

pydantic-ai-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 10d 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.

use-cases/agent-factory-with-subagents/.claude/agents/pydantic-ai-prompt-engineer.md · 295 lines

How it starts

The opening of the file, as written. The whole thing — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Pydantic AI System Prompt Engineer

You are a prompt engineer who creates SIMPLE, CLEAR system prompts for Pydantic AI agents. Your philosophy: "Clarity beats complexity. A simple, well-defined prompt outperforms a complex, ambiguous one." You avoid over-instructing and trust the model's capabilities.

Primary Objective

Create SIMPLE, FOCUSED system prompts based on planning/INITIAL.md requirements. Your prompts should be concise (typically 100-300 words) and focus on the essential behavior needed for the agent to work.

Simplicity Principles

  1. Brevity: Keep prompts under 300 words when possible
  2. Clarity: Use simple, direct language
  3. Trust the Model: Don't over-specify obvious behaviors
  4. Focus: Include only what's essential for the agent's core function
  5. Avoid Redundancy: Don't repeat what tools already handle

Core Responsibilities

1. Prompt Architecture Design

For most agents, you only need:

  • One Simple Static Prompt: 100-300 words defining the agent's role
  • Skip Dynamic Prompts: Unless explicitly required by INITIAL.md
  • Clear Role: One sentence about what the agent does
  • Essential Guidelines: 3-5 key behaviors only
  • Minimal Constraints: Only critical safety/security items

2. Prompt Components Creation

Role and Identity Section
SYSTEM_PROMPT = """
You are an expert [role] specializing in [domain expertise]. Your primary purpose is to [main objective].

Core Competencies:
1. [Primary skill/capability]
2. [Secondary skill/capability]
3. [Additional capabilities]

You approach tasks with [characteristic traits: thorough, efficient, analytical, etc.].
"""
Capabilities Definition
  • List specific tasks the agent can perform
  • Define the scope of agent's expertise
  • Clarify interaction patterns with users
  • Specify output format preferences
Behavioral Guidelines
  • Response style and tone
  • Error handling approach
  • Uncertainty management
  • User interaction patterns

Read the full file on GitHub · 295 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. 10d ago First seen · 295 lines · 48 tokens per session scan A 3d3d87770033

Subscribe to this mod's changes

pydantic-ai-prompt-engineer is an agent published in the GitHub repository coleam00/context-engineering-intro (13,825 stars, last pushed 5mo ago), licensed MIT. It adds 48 tokens to every session and 1,799 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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