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.
npx skills add fatihkan/badi --skill ai-automationgit clone --depth 1 https://github.com/fatihkan/badiWrote 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/skills/fatihkan/badi/ai-automation)<a href="https://agentmods.dev/skills/fatihkan/badi/ai-automation"><img src="https://agentmods.dev/badge/skills/fatihkan/badi/ai-automation/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/skills/fatihkan/badi/ai-automation"><img src="https://agentmods.dev/badge/skills/fatihkan/badi/ai-automation.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 | $0.00000 | $0.02072 |
| Opus 5.5 | $0.00000 | $0.00829 |
| Sonnet 5.5 | $0.00000 | $0.00414 |
| Haiku 4.5 | $0.00000 | $0.00207 |
Grade A, and why
ai-automation 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 — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Automation Skills
59 structured procedures
Skill List
chatbot-design
The skill of designing and building NLP-based chatbots fitting user needs. Builds end-to-end chatbot architecture including conversation flows, intent recognition, and context management.
prompt-engineering
The skill of designing, testing, and optimizing effective prompts for the best LLM results. Covers system prompts, few-shot examples, and chain-of-thought techniques.
rag-system-setup
The skill of designing and building Retrieval-Augmented Generation (RAG) systems. Delivers a complete RAG pipeline including document indexing, vector-database integration, and query optimization.
llm-fine-tuning
The skill of fine-tuning large language models for specific use cases. Covers data preparation, training-process management, and model evaluation.
automation-workflow
The skill of designing and building workflows that automate repetitive processes. Builds efficient automation systems with triggers, conditions, and action chains.
ai-assistant-development
The skill of building customized AI assistants. Builds end-to-end assistant applications with UI, backend services, and AI-model integration.
data-labeling
The skill of designing and managing the data-labeling process for ML models. Covers labeling guidelines, quality control, and inter-annotator agreement measurement.
model-evaluation
The skill of systematically evaluating AI model performance. Runs comprehensive model analysis with accuracy, precision, recall, and domain-specific metrics.
ai-ethics-framework
The skill of building ethical principles and an accountability framework for AI applications. Covers bias detection, transparency, and accountability mechanisms.
speech-recognition
The skill of designing and building speech-to-text systems. Builds ASR models, audio preprocessing, and real-time transcription pipelines.
image-classification
The skill of building and deploying image-classification models. Builds high-accuracy classifiers with transfer learning, data augmentation, and model optimization.
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 · 309 lines · 0 tokens per session scan A d198425f9fab
ai-automation is a skill published in the GitHub repository fatihkan/badi (7 stars, last pushed 21d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,072 tokens. 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 2026-09-06.
Other skills, from other repositories
ai-expertise-engine
Comprehensive AI/ML expertise covering prompt engineering, LLM architecture, AI agent design, RAG systems, fine-tuning, AI safety, and cutting-edge AI research for building and leveraging AI systems.
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
ai-product
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.
building-agents
Use when building or restructuring an LLM agent — provider adapter, tool calling, structured output, RAG, agent loop, eval gate, cost routing, tracing, MCP server — model-agnostic across OpenAI/Anthropic/Gemini/OSS so a model swap is a config change. NOT vector-store SQL alone (that is postgresdb) or service…
llm-application-dev
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
llm-app-development
LLM app development with RAG, prompt engineering, vector databases, and AI agents.