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AI Workflows

Practical guide: what to use, why, tradeoffs, and a minimal setup.

Quick Decision

  • One-off task -> Prompt
  • Repeatable behavior rule -> Instruction
  • Repeatable task flow -> Skill
  • Multi-step execution -> Agent
  • External system/tool access -> MCP
  • Fast shell/devops flow -> CLI

Topics

Prompt

  • Best for: One request
  • Benefit: Fast and flexible
  • Drawback: Quality depends on context quality

Instructions

  • Best for: Team conventions
  • Benefit: Consistent outputs across sessions
  • Drawback: Can become noisy if too broad

Skills

  • Best for: Repeatable workflows
  • Benefit: Reusable, structured execution
  • Drawback: Upfront design effort

globally installed skills are located here: %USERPROFILE%\.agents\skills or %USERPROFILE%\.copilot\skills

Agents

  • Best for: Complex multi-step tasks
  • Benefit: Better planning and autonomy
  • Drawback: More setup and maintenance

MCP

  • Best for: External tools and APIs
  • Benefit: Real data and integrations
  • Drawback: Security and ops overhead

Loading Behavior

  • Instructions are not automatically loaded in every case.
  • Broad instructions can be loaded very often, for example global instructions or wide applyTo patterns.
  • Narrow instruction files are only useful if they match the current task.
  • Skills are usually loaded on demand, not on every request.
  • Result: put stable behavior rules in instructions, but put heavier workflows into skills or agents.

Minimal Setup

  1. Add global behavior rules in AGENTS.md or .github/copilot-instructions.md.
  2. Add domain instructions for focused areas (api, security, docs).
  3. Add one prompt for recurring asks (for example: "draft ADR", "review API").
  4. Add one skill for a common workflow (for example: "create concise doc page").
  5. Add one agent when task orchestration becomes repetitive.

Example structure:

.github/
  copilot-instructions.md
  instructions/
    docs.instructions.md
    api.instructions.md
  prompts/
    write-short-doc.prompt.md
  skills/
    create-doc-page/SKILL.md
  agents/
    docs-editor.agent.md
AGENTS.md

Concrete Usage

  • Prompt: "Summarize this page in 5 bullets with 1 action per bullet."
  • Instruction: "For docs pages: short headings, short bullets, avoid duplicate content."
  • Skill: "When asked to create docs, first link related pages, then draft minimal page."
  • Agent: "Investigate, propose options, apply edits, run checks, report risks."

Token Optimization

  • Keep instructions short. Long global instructions cost tokens on many requests.
  • Avoid applyTo: "**" unless the rule really applies everywhere.
  • Split rules by area: docs, backend, frontend, security.
  • Link to existing docs instead of embedding the same guidance again.
  • Prefer small prompts with good context over large generic prompts.
  • Use skills for larger workflows so the details are loaded only when needed.
  • Remove stale instructions. Old rules create noise and contradictions.

Good for tokens:

  • "Write a short page in the style of docs/frontend/best-practices.md."

Bad for tokens:

  • Large prompt + copied style guide + copied examples + repeated repo rules

Typical Workflow

  1. Start with a prompt and success criteria.
  2. Promote repeated guidance into instructions.
  3. Promote repeated process into a skill.
  4. Promote repeated orchestration into an agent.
  5. Add MCP only if local context is not enough.

Best Practices

  • Keep each instruction file narrow in scope.
  • Prefer links to existing docs over copied text.
  • Treat skills as small building blocks.
  • Keep agents focused on one workflow outcome.
  • Review AI output before merge or publish.