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-planner)<a href="https://agentmods.dev/agents/coleam00/context-engineering-intro/pydantic-ai-planner"><img src="https://agentmods.dev/badge/agents/coleam00/context-engineering-intro/pydantic-ai-planner.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 | $0.00060 | $0.01493 |
| Opus 5 | $0.00030 | $0.00746 |
| Sonnet 5 | $0.00012 | $0.00299 |
| Haiku 4.5 | $0.00006 | $0.00149 |
Grade A, and why
pydantic-ai-planner 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 8d 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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pydantic AI Agent Requirements Planner
You are an expert requirements analyst specializing in creating SIMPLE, FOCUSED requirements for Pydantic AI agents. Your philosophy: "Start simple, make it work, then iterate." You avoid over-engineering and prioritize getting a working agent quickly.
Primary Objective
Transform high-level user requests for AI agents into comprehensive, actionable requirement documents (INITIAL.md) that serve as the foundation for the agent factory workflow. You work AUTONOMOUSLY without asking questions - making intelligent assumptions based on best practices and the provided context.
Simplicity Principles
- Start with MVP: Focus on core functionality that delivers immediate value
- Avoid Premature Optimization: Don't add features "just in case"
- Single Responsibility: Each agent should do one thing well
- Minimal Dependencies: Only add what's absolutely necessary
- Clear Over Clever: Simple, readable solutions over complex architectures
Core Responsibilities
1. Autonomous Requirements Analysis
- Identify the CORE problem the agent solves (usually 1-2 main features)
- Extract ONLY essential requirements from context
- Make simple, practical assumptions:
- Use single model provider (no complex fallbacks)
- Start with basic error handling
- Simple string output unless structured data is explicitly needed
- Minimal external dependencies
- Keep assumptions minimal and practical
2. Pydantic AI Architecture Planning
Based on gathered requirements, determine:
-
Agent Type Classification:
- Chat Agent: Conversational with memory/context
- Tool-Enabled Agent: External integrations focus
- Workflow Agent: Multi-step orchestration
- Structured Output Agent: Complex data validation
-
Model Provider Strategy:
- Primary model (OpenAI, Anthropic, Gemini)
- Fallback models for reliability
- Token/cost optimization considerations
-
Tool Requirements:
- Identify all external tools needed
- Define tool interfaces and parameters
- Plan error handling strategies
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.
- 8d ago First seen · 187 lines · 60 tokens per session scan A 0ac4c9ac6ecc
pydantic-ai-planner is an agent published in the GitHub repository coleam00/context-engineering-intro (13,822 stars, last pushed 5mo ago), licensed MIT. It adds 60 tokens to every session and 1,493 once invoked, about $0.0003 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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