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/generate-pydantic-ai-prp)<a href="https://agentmods.dev/commands/coleam00/context-engineering-intro/generate-pydantic-ai-prp"><img src="https://agentmods.dev/badge/commands/coleam00/context-engineering-intro/generate-pydantic-ai-prp/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/commands/coleam00/context-engineering-intro/generate-pydantic-ai-prp"><img src="https://agentmods.dev/badge/commands/coleam00/context-engineering-intro/generate-pydantic-ai-prp.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 | $0.00000 | $0.00839 |
| Opus 5 | $0.00000 | $0.00419 |
| Sonnet 5 | $0.00000 | $0.00168 |
| Haiku 4.5 | $0.00000 | $0.00084 |
Grade A, and why
generate-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 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.
This is a copy
88% identical to generate-prp — 28 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create PRP
Feature file: $ARGUMENTS
Generate a complete PRP for general feature implementation with thorough research. Ensure context is passed to the AI agent to enable self-validation and iterative refinement. Read the feature file first to understand what needs to be created, how the examples provided help, and any other considerations.
The AI agent only gets the context you are appending to the PRP and training data. Assuma the AI agent has access to the codebase and the same knowledge cutoff as you, so its important that your research findings are included or referenced in the PRP. The Agent has Websearch capabilities, so pass urls to documentation and examples.
Research Process
-
Codebase Analysis
- Search for similar features/patterns in the codebase
- Identify files to reference in PRP
- Note existing conventions to follow
- Check test patterns for validation approach
-
External Research
- Search for similar features/patterns online
- Library documentation (include specific URLs)
- Implementation examples (GitHub/StackOverflow/blogs)
- Best practices and common pitfalls
- Use Archon MCP server to gather latest Pydantic AI documentation
- Web search for specific patterns and best practices relevant to the agent type
- Research model provider capabilities and limitations
- Investigate tool integration patterns and security considerations
- Document async/sync patterns and testing strategies
-
User Clarification (if needed)
- Specific patterns to mirror and where to find them?
- Integration requirements and where to find them?
-
Analyzing Initial Requirements
- Read and understand the agent feature requirements
- Identify the type of agent needed (chat, tool-enabled, workflow, structured output)
- Determine required model providers and external integrations
- Assess complexity and scope of the agent implementation
-
Agent Architecture Planning
- Design agent structure (agent.py, tools.py, models.py, dependencies.py)
- Plan dependency injection patterns and external service integrations
- Design structured output models using Pydantic validation
- Plan tool registration and parameter validation strategies
- Design testing approach with TestModel/FunctionModel 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 · 95 lines · 0 tokens per session scan A ae242a51ba3d
generate-pydantic-ai-prp is a command published in the GitHub repository coleam00/context-engineering-intro (13,825 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 839 tokens. A static security scan graded it A with 0 findings. It is 88% identical to generate-prp, differing in 28 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.