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 | $0.00000 | $0.01036 |
| Sonnet 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 4d 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.
- 4d 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 3d 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-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. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords…
optimize
Rephrase a rough prompt to follow prompt-engineering best practices WITHOUT changing its meaning, then show it for review without executing. Use when the user runs /petprompt:optimize or asks to rewrite/clean up/optimize their prompt before running it.
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.
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.