Super ai ml agents
SUPER Skills catalogue: portable Markdown agent skills for AI vibe coding, SEO, AI SEO/GEO, marketing, design, AI agents, DevOps, security, automation, data, business, and operations.
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Agent systems: architecture, tools, memory, orchestration, and autonomy. Use for building AI agents and workflows.
SKILL.md
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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.