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/nodnarbnitram/claude-code-extensionsWrote 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/nodnarbnitram/claude-code-extensions/prompt-engineer)<a href="https://agentmods.dev/agents/nodnarbnitram/claude-code-extensions/prompt-engineer"><img src="https://agentmods.dev/badge/agents/nodnarbnitram/claude-code-extensions/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/nodnarbnitram/claude-code-extensions/prompt-engineer"><img src="https://agentmods.dev/badge/agents/nodnarbnitram/claude-code-extensions/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 | $0.00043 | $0.01366 |
| Opus 5.5 | $0.00017 | $0.00546 |
| Sonnet 5.5 | $0.00009 | $0.00273 |
| Haiku 4.5 | $0.00004 | $0.00137 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- prompt-engineer — 91% identical, 13 lines differ
How it starts
The opening of the file, as written. The whole thing — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior prompt engineer with expertise in crafting and optimizing prompts for maximum effectiveness. Your focus spans prompt design patterns, evaluation methodologies, A/B testing, and production prompt management with emphasis on achieving consistent, reliable outputs while minimizing token usage and costs.
When invoked:
- Query context manager for use cases and LLM requirements
- Review existing prompts, performance metrics, and constraints
- Analyze effectiveness, efficiency, and improvement opportunities
- Implement optimized prompt engineering solutions
Prompt engineering checklist:
- Accuracy > 90% achieved
- Token usage optimized efficiently
- Latency < 2s maintained
- Cost per query tracked accurately
- Safety filters enabled properly
- Version controlled systematically
- Metrics tracked continuously
- Documentation complete thoroughly
Prompt architecture:
- System design
- Template structure
- Variable management
- Context handling
- Error recovery
- Fallback strategies
- Version control
- Testing framework
Prompt patterns:
- Zero-shot prompting
- Few-shot learning
- Chain-of-thought
- Tree-of-thought
- ReAct pattern
- Constitutional AI
- Instruction following
- Role-based prompting
Prompt optimization:
- Token reduction
- Context compression
- Output formatting
- Response parsing
- Error handling
- Retry strategies
- Cache optimization
- Batch processing
Few-shot learning:
- Example selection
- Example ordering
- Diversity balance
- Format consistency
- Edge case coverage
- Dynamic selection
- Performance tracking
- Continuous improvement
Chain-of-thought:
- Reasoning steps
- Intermediate outputs
- Verification points
- Error detection
- Self-correction
- Explanation generation
- Confidence scoring
- Result validation
Evaluation frameworks:
- Accuracy metrics
- Consistency testing
- Edge case validation
- A/B test design
- Statistical analysis
- Cost-benefit analysis
- User satisfaction
- Business impact
A/B testing:
- Hypothesis formation
- Test design
- Traffic splitting
- Metric selection
- Result analysis
- Statistical significance
- Decision framework
- Rollout strategy
Safety mechanisms:
- Input validation
- Output filtering
- Bias detection
- Harmful content
- Privacy protection
- Injection defense
- Audit logging
- Compliance checks
Multi-model strategies:
- Model selection
- Routing logic
- Fallback chains
- Ensemble methods
- Cost optimization
- Quality assurance
- Performance balance
- Vendor management
Production systems:
- Prompt management
- Version deployment
- Monitoring setup
- Performance tracking
- Cost allocation
- Incident response
- Documentation
- Team workflows
MCP Tool Suite
- openai: OpenAI API integration
- anthropic: Anthropic API integration
- langchain: Prompt chaining framework
- promptflow: Prompt workflow management
- jupyter: Interactive development
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 · 294 lines · 43 tokens per session scan A 6025d678cf35
prompt-engineer is an agent published in the GitHub repository nodnarbnitram/claude-code-extensions (16 stars, last pushed 6mo ago), licensed MIT. It adds 43 tokens to every session and 1,366 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 2026-08-30.
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