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/NickCrew/Claude-CortexWrote 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/nickcrew/claude-cortex/inactive-prompt-engineer)<a href="https://agentmods.dev/agents/nickcrew/claude-cortex/inactive-prompt-engineer"><img src="https://agentmods.dev/badge/agents/nickcrew/claude-cortex/inactive-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/nickcrew/claude-cortex/inactive-prompt-engineer"><img src="https://agentmods.dev/badge/agents/nickcrew/claude-cortex/inactive-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 | $0.00038 | $0.00990 |
| Opus 5.5 | $0.00015 | $0.00396 |
| Sonnet 5.5 | $0.00008 | $0.00198 |
| Haiku 4.5 | $0.00004 | $0.00099 |
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 1mo 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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. The prompt needs to be displayed in your response in a single block of text that can be copied and pasted.
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
Example Output
When asked to create a prompt for code review:
The Prompt
You are an expert code reviewer with 10+ years of experience. Review the provided code focusing on:
1. Security vulnerabilities
2. Performance optimizations
3. Code maintainability
4. Best practices
For each issue found, provide:
- Severity level (Critical/High/Medium/Low)
- Specific line numbers
- Explanation of the issue
- Suggested fix with code example
Format your response as a structured report with clear sections.
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.
- 1mo ago First seen · 171 lines · 38 tokens per session scan A 1f4f0a196c9c
prompt-engineer is an agent published in the GitHub repository NickCrew/Claude-Cortex (57 stars, last pushed 3mo ago), licensed MIT. It adds 38 tokens to every session and 990 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
Other agents, from other repositories
prompt
Designs versioned system prompts, few-shot libraries, and chain-of-thought patterns with A/B testing and regression coverage — treats prompts as production code. Use when engineering a production LLM feature, auditing a prompt library for drift, or building prompt versioning infrastructure. Trigger with "design this…
report-generator
Performs blind comparison of repeated prompt-execution pairs, then maps observed differences to optimization findings after identity reveal. Use when original and optimized prompt trials are available.
prompt-analyzer
Analyzes prompts against BP-001 through BP-009 and returns the prompt-optimization skill's gated JSON result. Use when prompt text or a prompt file is provided for optimization.
prompt-engineer
Use when writing, iterating, or debugging prompts. Enforces prompt-versioning, structures few-shot examples, and proposes eval criteria for the prompt being built.
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
token
Optimizes LLM context windows through token budgeting, chunking strategy, and truncation design. Use when you need to control token spend, design a chunking pipeline, or audit token usage in a production AI system. Trigger with "design my token budget", "fix my context overflow".