AI Agent Workflow

Use this skill when the goal is not just to get one answer, but to build a repeatable AI-assisted workflow and the main stack direction is already mostly known.

This skill is for taking AI ideas and turning them into structured loops: prompts, tools, retrieval, checks, and reusable building blocks.

Inputs

Useful inputs for this skill include:

• the repeatable task you want the workflow to handle
• current prompt, script, tool, or skill draft if one exists
• model constraints such as local-only or API limits when the basic stack is already chosen
• available tools, MCP servers, files, or data sources
• quality bar, failure modes, and evaluation expectations

Outputs

Strong outputs from this skill usually include one or more of:

• a reusable workflow specification
• a prompt template or prompt stack
• a tool integration or MCP plan
• an evaluation loop or quality rubric
• a recommendation for whether this should be a prompt, skill, script, or MCP server
• a minimal implementation path with clear next steps

Non-goals

This skill is not the best fit for:

• choosing between local deployment stacks such as MLX, GGUF, LM Studio, Ollama, or vLLM
• hardware-first local LLM decisions or local serving setup questions
• creating a new skill from scratch or rewriting the skill artifact itself
• benchmarking a skill, improving skill triggering, or building a skill eval harness
• one-off content writing with no reusable workflow need
• generic code fixes unrelated to AI systems or tooling
• visual design, portfolio packaging, or office-document polish
• hand-wavy AI brainstorming that never needs an explicit operating loop

Workflow

1. Define the unit of work.
   Clarify what the workflow should repeatedly accomplish:

• generate
• transform
• evaluate
• retrieve
• route
• summarize
• orchestrate tools

2. Choose the right control surface.
   Decide whether the problem is best solved by:

• better prompting
• a reusable skill
• MCP tool integration
• structured references
• evaluation and iteration
• a small script or automation

If the decision is still mainly about local stack choice, deployment path, or hardware fit, hand the problem to local-ai-systems-studio first.
If the user is explicitly asking to author, package, validate, benchmark, or optimize a skill, hand the task to skill-creator instead of keeping it here.

3. Keep the workflow explicit.
   Spell out:

• inputs
• steps
• tool calls
• expected outputs
• failure handling
• validation points

4. Design for iteration.
   If quality matters, include a loop:

• draft
• inspect
• revise
• compare
• finalize

Examples

Example 1: Local LLM workflow design

User request:

I want to use my local Qwen model to summarize PDFs, extract tasks, and save clean notes.

Good use of this skill:

• assume the local stack is already basically chosen
• separate extraction, cleanup, and summarization stages
• define what can stay prompt-only versus what should call tools
• specify stable input and output shapes so the workflow can be repeated

Example 2: Skill or MCP decision

User request:

Should this document-processing task become a skill, a script, or an MCP server?

Good use of this skill:

• compare control surfaces honestly
• optimize for reuse, tool access, and maintenance cost
• recommend the smallest durable abstraction that solves the real problem
• stop and switch to skill-creator if the next concrete ask becomes "write the skill" or "evaluate the skill itself"

Example 3: Evaluation loop

User request:

Help me build an eval loop for this agent so I can tell if the outputs are getting better.

Good use of this skill:

• define success criteria and failure categories
• design draft, review, revise, and compare steps
• suggest lightweight evaluation before overbuilding infrastructure

Pairing With Other Skills

Use these when appropriate:

• local-ai-systems-studio when the main question is local model choice, deployment path, or hardware-to-stack fit
• mcp-server-builder for real tool integration
• skill-creator when the request becomes "turn this into a skill", "improve this skill", or "benchmark this skill"
• skill-reviewer for reviewing an already-written skill against best practices
• skills-search before building from scratch
• prompt-optimizer for prompt quality work
• deep-research when the workflow depends on structured external knowledge

Triggers

Common requests that should trigger this skill:

• "Help me turn this into an agent workflow"
• "How should I structure this workflow now that I know the stack?"
• "Should this be a skill, prompt, script, or MCP server?"
• "Make this AI task reusable"
• "Design an eval loop for this workflow"

Requests that should usually go somewhere else:

• "Create a skill for this workflow" -> skill-creator
• "Benchmark or improve this skill" -> skill-creator
• "Help me choose between LM Studio and Ollama" -> local-ai-systems-studio

Reference

Read references/checklist.md when you need a compact workflow design checklist.