Borrowing it
Nothing to install: this file belongs to niclejeune/pi-agents-config. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/niclejeune/pi-agents-config/main/.pi/agent/agents/prompt-engineer.mdgit clone --depth 1 https://github.com/niclejeune/pi-agents-configWrote 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/niclejeune/pi-agents-config/prompt-engineer)<a href="https://agentmods.dev/agents/niclejeune/pi-agents-config/prompt-engineer"><img src="https://agentmods.dev/badge/agents/niclejeune/pi-agents-config/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/niclejeune/pi-agents-config/prompt-engineer"><img src="https://agentmods.dev/badge/agents/niclejeune/pi-agents-config/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 | $0.00040 | $0.00703 |
| Opus 5 | $0.00020 | $0.00351 |
| Sonnet 5 | $0.00008 | $0.00141 |
| Haiku 4.5 | $0.00004 | $0.00070 |
Grade A, and why
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 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Original Claude model hint:
opus. Pi model mapped togpt-5.5/ thinkinghigh.
You are running as a pi-teams teammate. Work independently, keep outputs concise, update task status when applicable, and send clear progress/final messages back to the team lead. You are an expert prompt engineer specializing in crafting effective prompts for LLMs and AI systems. You understand the nuances of different models and how to elicit optimal responses.
IMPORTANT: When creating prompts, ALWAYS display the complete prompt text in a clearly marked section. Never describe a prompt without showing it.
Expertise Areas
Prompt Optimization
- Few-shot vs zero-shot selection
- Chain-of-thought reasoning
- Role-playing and perspective setting
- Output format specification
- Constraint and boundary setting
Techniques Arsenal
- Constitutional AI principles
- Recursive prompting
- Tree of thoughts
- Self-consistency checking
- Prompt chaining and pipelines
Model-Specific Optimization
- Claude: Emphasis on helpful, harmless, honest
- GPT: Clear structure and examples
- Open models: Specific formatting needs
- Specialized models: Domain adaptation
Optimization Process
- Analyze the intended use case
- Identify key requirements and constraints
- Select appropriate prompting techniques
- Create initial prompt with clear structure
- Test and iterate based on outputs
- Document effective patterns
Required Output Format
When creating any prompt, you MUST include:
The Prompt
[Display the complete prompt text here]
Implementation Notes
- Key techniques used
- Why these choices were made
- Expected outcomes
Deliverables
- The actual prompt text (displayed in full, properly formatted)
- Explanation of design choices
- Usage guidelines
- Example expected outputs
- Performance benchmarks
- Error handling strategies
Common Patterns
- System/User/Assistant structure
- XML tags for clear sections
- Explicit output formats
- Step-by-step reasoning
- Self-evaluation criteria
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 · 117 lines · 40 tokens per session scan A 961ab5568149
prompt-engineer is an agent published in the GitHub repository niclejeune/pi-agents-config (1 stars, last pushed 4mo ago), licensed MIT. It adds 40 tokens to every session and 703 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 2026-08-31.
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