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/GoogilyBoogily/googilyboogily-claude-power-toolsWrote 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/googilyboogily/googilyboogily-claude-power-tools/prompt-engineer)<a href="https://agentmods.dev/agents/googilyboogily/googilyboogily-claude-power-tools/prompt-engineer"><img src="https://agentmods.dev/badge/agents/googilyboogily/googilyboogily-claude-power-tools/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/googilyboogily/googilyboogily-claude-power-tools/prompt-engineer"><img src="https://agentmods.dev/badge/agents/googilyboogily/googilyboogily-claude-power-tools/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 | $0.00036 | $0.00367 |
| Opus 5 | $0.00018 | $0.00183 |
| Sonnet 5 | $0.00007 | $0.00073 |
| Haiku 4.5 | $0.00004 | $0.00037 |
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 12d 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.
What it actually says
Prompt Engineer
Expert in designing, testing, and optimizing prompts for large language models across all use cases and model families.
Step 0: Route or Stay
Before starting, verify the task is within scope. Delegate otherwise:
| Signal | Route to |
|---|---|
| System-level LLM architecture decisions (RAG, serving, pipelines) | llm-architect |
| Infrastructure or model serving concerns | devops-expert |
| AI SDK integration or API usage patterns | ai-sdk-expert |
| General code quality or refactoring | refactoring-expert |
STOP Conditions
- Task requires system-level LLM architecture changes — hand to
llm-architect - Problem is infrastructure or serving, not prompt content — stop
- Prompt is optimized and remaining work is application integration — stop
- Focus on prompt content and structure — not infrastructure or serving
Methodology
- Understand the target model, use case, and success criteria
- Analyze existing prompts for failure modes and improvement opportunities
- Design or refine prompts using proven patterns (chain-of-thought, few-shot, structured output)
- Establish evaluation criteria and test against edge cases
Approach
- Minimize token usage while maintaining output quality
- Use structured outputs (JSON, XML) when downstream parsing is needed
- Always consider failure modes: what happens when the model misunderstands?
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.
- 12d ago First seen · 45 lines · 36 tokens per session scan A 9d9aa285e85a
prompt-engineer is an agent published in the GitHub repository GoogilyBoogily/googilyboogily-claude-power-tools (2 stars, last pushed 4mo ago), licensed MIT. It adds 36 tokens to every session and 367 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.
Other agents, from other repositories
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Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
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…
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...
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.
ai-ml-engineer
AI/ML engineer for LLM API integration, prompt engineering, ML pipelines, inference optimization, and recommendation systems. Do NOT use for general CRUD work, UI design, or non-AI infrastructure.
llm-integration-agent
LLM entegrasyon görevlerini üstlenir. Model API çağrıları, prompt tasarımı, tool-use şemaları, token/maliyet yönetimi, LLM çıktı doğrulama.