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.
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/coleam00/context-engineering-introWrote 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/coleam00/context-engineering-intro/pydantic-ai-prompt-engineer)<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.
<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>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 | $0.00048 | $0.01799 |
| Opus 5 | $0.00024 | $0.00899 |
| Sonnet 5 | $0.00010 | $0.00360 |
| Haiku 4.5 | $0.00005 | $0.00180 |
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.
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
- Brevity: Keep prompts under 300 words when possible
- Clarity: Use simple, direct language
- Trust the Model: Don't over-specify obvious behaviors
- Focus: Include only what's essential for the agent's core function
- 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
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.
- 10d ago First seen · 295 lines · 48 tokens per session scan A 3d3d87770033
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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llm-integration-agent
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