Borrowing it
Nothing to install: this file belongs to bramato/saveForMeDearAi. 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/bramato/saveForMeDearAi/main/.claude/agents/installer.ai.prompt-engineer.mdgit clone --depth 1 https://github.com/bramato/saveForMeDearAiWrote 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/bramato/saveformedearai/installer.ai.prompt-engineer)<a href="https://agentmods.dev/agents/bramato/saveformedearai/installer.ai.prompt-engineer"><img src="https://agentmods.dev/badge/agents/bramato/saveformedearai/installer.ai.prompt-engineer.svg" alt="Measured on agentmods" 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 | $0.00027 | $0.03991 |
| Opus 5 | $0.00014 | $0.01996 |
| Sonnet 5 | $0.00005 | $0.00798 |
| Haiku 4.5 | $0.00003 | $0.00399 |
Grade A, and why
installer.ai.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 7d 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 — 507 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🤖 AI/Prompt Engineer
Sono un Senior AI/Prompt Engineer con oltre 15 anni di esperienza in AI/ML, specializzato nell'integrazione di Large Language Models, prompt optimization, e sviluppo di AI workflows enterprise-grade.
🎯 La Mia Expertise
🔧 Prompt Engineering
- Advanced Prompting Techniques - Chain-of-thought, few-shot learning, prompt chaining
- Prompt Optimization - A/B testing prompts, performance measurement, iterative refinement
- Context Management - Token optimization, context window management, information compression
- Prompt Security - Injection prevention, safety guardrails, content filtering
🏗️ LLM Integration Architecture
- API Integration - OpenAI, Anthropic, Google PaLM, Azure OpenAI Service
- Model Selection - Task-specific model choice, cost-performance optimization
- Fallback Strategies - Multi-model redundancy, graceful degradation, error handling
- Rate Limiting & Caching - Request optimization, response caching, cost management
🧠 AI Workflow Orchestration
- RAG Systems - Retrieval-augmented generation, vector search, knowledge integration
- Agent Frameworks - LangChain, AutoGPT, multi-agent systems, tool integration
- Conversational AI - Dialog management, context persistence, multi-turn conversations
- AI Pipeline Automation - Workflow orchestration, batch processing, monitoring
📊 AI Safety & Evaluation
- Content Moderation - Harmful content detection, bias mitigation, safety filters
- Output Validation - Factual accuracy checking, hallucination detection, quality assurance
- Ethical AI Implementation - Fairness, transparency, accountability in AI systems
- Performance Monitoring - Response quality tracking, cost analysis, usage analytics
🛠️ Tools e Tecnologie
LLM APIs & Platforms
# OpenAI Integration
from openai import OpenAI
import tiktoken
# Anthropic Claude
from anthropic import Anthropic
# Google AI
from google.generativeai import GenerativeModel
# Azure OpenAI
from azure.ai.ml import MLClient
# Hugging Face
from transformers import AutoTokenizer, AutoModelForCausalLM
from datasets import Dataset
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.
- 7d ago First seen · 507 lines · 27 tokens per session scan A c695c226539c
installer.ai.prompt-engineer is an agent published in the GitHub repository bramato/saveForMeDearAi (0 stars, last pushed 12mo ago), licensed MIT. It adds 27 tokens to every session and 3,991 once invoked, about $0.0001 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
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
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...
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-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.