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-agentsgit 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-agents)<a href="https://agentmods.dev/skills/arpitexplores/skills-super/super-ai-ml-agents"><img src="https://agentmods.dev/badge/skills/arpitexplores/skills-super/super-ai-ml-agents/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-agents"><img src="https://agentmods.dev/badge/skills/arpitexplores/skills-super/super-ai-ml-agents.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 | $0.00405 |
| Opus 5.5 | $0.00012 | $0.00162 |
| Sonnet 5.5 | $0.00006 | $0.00081 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
super-ai-ml-agents 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 2mo 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 Agents
Overview
Build reliable agent systems with clear tool boundaries, memory, and orchestration.
User Intent Examples
- "Need help with Agent Memory Systems for my product/site."
- "Create a plan for Agent Architecture."
- "Audit or improve Multi-Agent Patterns."
Workflow
- Define the agent role, scope, and allowable actions.
- Design tool interfaces and safety constraints.
- Choose memory strategy: short-term, long-term, and retrieval.
- Select orchestration pattern (single agent, multi-agent, supervisor).
- Implement guardrails, fallbacks, and human-in-the-loop controls.
- Validate behaviour with scenarios and regression tests.
Minimal Intake Questions
- Primary goal or outcome
- Scope (pages, systems, teams, or timeframe)
- Constraints (tools, budget, timeline)
Output Format
- Agent scope and behaviour contract
- Tooling and permissions plan
- Memory architecture
- Orchestration pattern and flow
- Validation plan with scenarios
Routing Map (Modules)
- Agent Memory Systems ->
references/modules/agent-memory-systems.md - Agent Architecture ->
references/modules/ai-agents-architect.md - Multi-Agent Patterns ->
references/modules/multi-agent-patterns.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
- Avoid ambiguous tool routing; keep responsibilities explicit.
- Keep memory bounded and purge stale data.
- Use human approval for high-risk actions.
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.
- 2mo ago First seen · 56 lines · 30 tokens per session scan A 65fe64991467
super-ai-ml-agents is a skill published in the GitHub repository arpitexplores/skills-super (2 stars, last pushed 6mo ago), licensed MIT. It adds 30 tokens to every session and 405 once invoked, about $0.0001 per session on Opus 5.5. 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.
super-ai-ml-agents compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| super-ai-ml-agentsarpitexplores/skills-super | Skill | 2 | 6mo ago | MIT | A | 30 | Codex |
| plansyrangana/Plans | Skill | 3 | 29d ago | MIT | A | 69 | Claude Code |
| markdown-ui-dslMegaByteMark/markdown-ui-dsl | Skill | 25 | 7mo ago | MIT | A | 25 | Claude Code, Codex |
| orchestrateblas0/UnseveredMemory | Skill | 49 | 10mo ago | MIT | A | 0 | Claude Code |
| refocusingmvschwarz/openrig | Skill | 8.7k | today | Apache-2.0 | A | 59 | Claude Code |
| rulesyncdyoshikawa/rulesync | Skill | 1.6k | yesterday | MIT | A | 64 | Claude Code, Codex |
| energy-procurementhashgraph-online/awesome-codex-plugins | Skill | 1.5k | yesterday | Apache-2.0 | A | 93 | Claude Code, Codex |