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/commands/coleam00/context-engineering-intro/execute-pydantic-ai-prp)<a href="https://agentmods.dev/commands/coleam00/context-engineering-intro/execute-pydantic-ai-prp"><img src="https://agentmods.dev/badge/commands/coleam00/context-engineering-intro/execute-pydantic-ai-prp.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 | $0.00000 | $0.00506 |
| Opus 5 | $0.00000 | $0.00253 |
| Sonnet 5 | $0.00000 | $0.00101 |
| Haiku 4.5 | $0.00000 | $0.00051 |
Grade A, and why
execute-pydantic-ai-prp 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 9d 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Execute Pydantic AI Agent PRP
Implement a Pydantic AI agent using the PRP file.
PRP File: $ARGUMENTS
Execution Process
-
Load PRP
- Read the specified Pydantic AI PRP file
- Understand all agent requirements and research findings
- Follow all instructions in the PRP and extend research if needed
- Review main_agent_reference patterns for implementation guidance
- Do more web searches and Pydantic AI documentation review as needed
-
ULTRATHINK
- Think hard before executing the agent implementation plan
- Break down agent development into smaller steps using your todos tools
- Use the TodoWrite tool to create and track your agent implementation plan
- Follow main_agent_reference patterns for configuration and structure
- Plan agent.py, tools.py, dependencies.py, and testing approach
-
Execute the plan
- Implement the Pydantic AI agent following the PRP
- Create agent with environment-based configuration (settings.py, providers.py)
- Use string output by default (no result_type unless structured output needed)
- Implement tools with @agent.tool decorators and proper error handling
- Add comprehensive testing with TestModel and FunctionModel
-
Validate
- Test agent import and instantiation
- Run TestModel validation for rapid development testing
- Test tool registration and functionality
- Run pytest test suite if created
- Verify agent follows main_agent_reference patterns
-
Complete
- Ensure all PRP checklist items done
- Test agent with example queries
- Verify security patterns (environment variables, error handling)
- Report completion status
- Read the PRP again to ensure complete implementation
-
Reference the PRP
- You can always reference the PRP again if needed
Pydantic AI-Specific Patterns to Follow
- Configuration: Use environment-based setup like main_agent_reference
- Output: Default to string output, only use result_type when validation needed
- Tools: Use @agent.tool with RunContext for dependency injection
- Testing: Include TestModel validation for development
- Security: Environment variables for API keys, proper error handling
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.
- 9d ago First seen · 55 lines · 0 tokens per session scan A cdad5072a64f
execute-pydantic-ai-prp is a command published in the GitHub repository coleam00/context-engineering-intro (13,822 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 506 tokens. 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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