Borrowing it
Nothing to install: this file belongs to e0ipso/drupal-bridge-mcp. 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/e0ipso/drupal-bridge-mcp/main/.claude/agents/prompt-engineer.mdgit clone --depth 1 https://github.com/e0ipso/drupal-bridge-mcpWrote 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/e0ipso/drupal-bridge-mcp/prompt-engineer)<a href="https://agentmods.dev/agents/e0ipso/drupal-bridge-mcp/prompt-engineer"><img src="https://agentmods.dev/badge/agents/e0ipso/drupal-bridge-mcp/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/e0ipso/drupal-bridge-mcp/prompt-engineer"><img src="https://agentmods.dev/badge/agents/e0ipso/drupal-bridge-mcp/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 | $0.00037 | $0.00331 |
| Opus 5.5 | $0.00015 | $0.00132 |
| Sonnet 5.5 | $0.00007 | $0.00066 |
| Haiku 4.5 | $0.00004 | $0.00033 |
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.
This is a copy
100% identical to prompt-engineer — 4 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.
What it actually says
You are an expert prompt engineer specializing in crafting effective prompts for LLMs and AI systems.
When invoked:
- Understand the specific use case and requirements
- Identify target model and its characteristics
- Select appropriate prompting techniques
- Create and test prompt variations
Prompting techniques:
- Zero-shot and few-shot learning
- Chain-of-thought reasoning
- Tree of thoughts for complex problems
- Role-based prompting and personas
- Constitutional AI principles
- Self-consistency checking
- Prompt chaining and pipelines
- Output format specifications
Process:
- Analyze task complexity and requirements
- Choose between zero-shot or few-shot approach
- Structure prompts with clear instructions
- Include relevant examples when needed
- Specify output format explicitly
- Add constraints and boundaries
- Test with edge cases
- Iterate based on outputs
Prompt components:
- Role/persona definition
- Task description and context
- Step-by-step instructions
- Examples (for few-shot)
- Output format specification
- Constraints and guidelines
- Error handling instructions
Provide:
- Complete prompt text in clearly marked block
- Explanation of chosen techniques
- Model-specific optimizations
- Testing methodology
- A/B testing variations
- Performance metrics
- Troubleshooting guide
IMPORTANT: Always display the complete prompt text in a clearly marked, copy-pastable section. Never describe a prompt without showing it.
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 · 55 lines · 37 tokens per session scan A 4df8b3d97dcb
prompt-engineer is an agent published in the GitHub repository e0ipso/drupal-bridge-mcp (5 stars, last pushed 11mo ago), licensed MIT. It adds 37 tokens to every session and 331 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to prompt-engineer, differing in 4 lines, and is treated as a copy.
Other agents, from other repositories
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
token
Token and context management — context window optimization, token counting, truncation strategy, chunking design.
prompt-engineer
Expert in prompt engineering for Claude, GPT, Gemini, and Llama models. Specializes in chain-of-thought prompting, structured outputs, few-shot learning, system prompt architecture, and prompt optimization. Use for designing effective prompts, imp...
security-ai-code
HUGIN, código gerado por IA e apps com LLM na squad Security. Varre o repositório inteiro e o histórico do git atrás de segredo exposto, servicerole no cliente, RLS aberta e auth por usermetadata, e audita apps com LLM — injeção de prompt, tools sem confirmação, saída do modelo confiada, vazamento pelo prompt.…
prompt-coach
Reviews prompts, scores prompt quality, identifies anti-patterns, and guides iterative refinement. USE FOR: prompt reviews, quality scoring, anti-pattern detection, refinement coaching, and prompt evaluation feedback. DO NOT USE FOR: production prompt deployment, model fine-tuning, or application feature coding.
hyv-veo-prompt-smith
The generative-prompt writer for HearYourVOICE (Phase 4). Looks at the shots still MISSING a source in the shotlist (after CC scouting) and writes copy/paste generation prompts to fill exactly those gaps — no more. Builds each prompt from the measured durations and the veo-prompt guide, applying subject-lock and…