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 manu14357/zskills --skill ai-mlgit clone --depth 1 https://github.com/manu14357/zskillsWrote 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/manu14357/zskills/ai-ml)<a href="https://agentmods.dev/skills/manu14357/zskills/ai-ml"><img src="https://agentmods.dev/badge/skills/manu14357/zskills/ai-ml/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/manu14357/zskills/ai-ml"><img src="https://agentmods.dev/badge/skills/manu14357/zskills/ai-ml.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 | $0.00030 | $0.01467 |
| Opus 5 | $0.00015 | $0.00733 |
| Sonnet 5 | $0.00006 | $0.00293 |
| Haiku 4.5 | $0.00003 | $0.00147 |
Grade A, and why
ai-ml 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.
This is a copy
98% identical to ai-ml — 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.
How it starts
The opening of the file, as written. The whole thing — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI/ML Workflow Bundle
Overview
Comprehensive AI/ML workflow for building LLM applications, implementing RAG systems, creating AI agents, and developing machine learning pipelines. This bundle orchestrates skills for production AI development.
When to Use This Workflow
Use this workflow when:
- Building LLM-powered applications
- Implementing RAG (Retrieval-Augmented Generation)
- Creating AI agents
- Developing ML pipelines
- Adding AI features to applications
- Setting up AI observability
Workflow Phases
Phase 1: AI Application Design
Skills to Invoke
ai-product- AI product developmentai-engineer- AI engineeringai-agents-architect- Agent architecturellm-app-patterns- LLM patterns
Actions
- Define AI use cases
- Choose appropriate models
- Design system architecture
- Plan data flows
- Define success metrics
Copy-Paste Prompts
Use @ai-product to design AI-powered features
Use @ai-agents-architect to design multi-agent system
Phase 2: LLM Integration
Skills to Invoke
llm-application-dev-ai-assistant- AI assistant developmentllm-application-dev-langchain-agent- LangChain agentsllm-application-dev-prompt-optimize- Prompt engineeringgemini-api-dev- Gemini API
Actions
- Select LLM provider
- Set up API access
- Implement prompt templates
- Configure model parameters
- Add streaming support
- Implement error handling
Copy-Paste Prompts
Use @llm-application-dev-ai-assistant to build conversational AI
Use @llm-application-dev-langchain-agent to create LangChain agents
Use @llm-application-dev-prompt-optimize to optimize prompts
Phase 3: RAG Implementation
Skills to Invoke
rag-engineer- RAG engineeringrag-implementation- RAG implementationembedding-strategies- Embedding selectionvector-database-engineer- Vector databasessimilarity-search-patterns- Similarity searchhybrid-search-implementation- Hybrid search
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 · 254 lines · 30 tokens per session scan A da45bc97e4e8
ai-ml is a skill published in the GitHub repository manu14357/zskills (16 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 1,467 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to ai-ml, differing in 4 lines, and is treated as a copy.
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