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 arpitexplores/skills-super --skill super-ai-ml-foundationgit clone --depth 1 https://github.com/arpitexplores/skills-superWrote 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/arpitexplores/skills-super/super-ai-ml-foundation)<a href="https://agentmods.dev/skills/arpitexplores/skills-super/super-ai-ml-foundation"><img src="https://agentmods.dev/badge/skills/arpitexplores/skills-super/super-ai-ml-foundation/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/arpitexplores/skills-super/super-ai-ml-foundation"><img src="https://agentmods.dev/badge/skills/arpitexplores/skills-super/super-ai-ml-foundation.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 | $0.00418 |
| Opus 5 | $0.00018 | $0.00209 |
| Sonnet 5 | $0.00007 | $0.00084 |
| Haiku 4.5 | $0.00004 | $0.00042 |
Grade A, and why
super-ai-ml-foundation 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 11d 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
Super AI/ML Foundation
Overview
Establish the core AI/ML architecture before building agents or ops layers.
User Intent Examples
- "Need help with AI Engineering for my product/site."
- "Create a plan for Prompt Engineering."
- "Audit or improve RAG Engineering."
Workflow
- Confirm use case, success criteria, latency, and cost targets.
- Select model family and deployment approach (hosted vs self-hosted).
- Design prompts, tool interfaces, and guardrails.
- Plan and implement RAG: chunking, embeddings, indexing, retrieval.
- Validate relevance, quality, and failure modes with small tests.
- Document tradeoffs, risks, and next experiments.
Minimal Intake Questions
- Primary goal or outcome
- Scope (pages, systems, teams, or timeframe)
- Constraints (tools, budget, timeline)
Output Format
- Use-case brief and success metrics
- Model choice with rationale
- Prompt and tool plan
- RAG architecture plan
- Risks, mitigations, and next steps
Routing Map (Modules)
- AI Engineering ->
references/modules/ai-engineer.md - Prompt Engineering ->
references/modules/prompt-engineering-patterns.md - RAG Engineering ->
references/modules/rag-engineer.md
Bundled References
references/modules/scripts/assets/agents/
Compatibility Notes
- If any module references slash commands or tool-specific paths, translate them into plain-language steps.
- Keep outputs platform-agnostic unless the user specifies a specific tool, stack, or agent.
Guardrails
- Do not claim benchmark results without data.
- Separate measured results from hypotheses.
- Prefer the smallest viable model and simplest retrieval setup.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 56 lines · 36 tokens per session scan A 73a402d3fad9
super-ai-ml-foundation is a skill published in the GitHub repository arpitexplores/skills-super (2 stars, last pushed 4mo ago), licensed MIT. It adds 36 tokens to every session and 418 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.
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