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A skill is the right choice when you want to teach the AI how your team handles a task. In TeamCopilot, you can create a skill in two ways:
  • manually from the Skills tab
  • by asking the AI Assistant to create one for you
In both cases, the skill is stored as instruction files in your workspace.

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Before you create one

Create a skill when:
  • you want reusable instructions for the AI
  • the task depends on judgment, policy, or process
  • you want the AI to follow your team’s way of working
If the task needs code to run against APIs, databases, or external systems, create a workflow instead.

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Two ways to create a skill

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Create a skill manually

  1. Open TeamCopilot.
  2. Go to the Skills tab.
  3. Click Create Skill.
  4. Enter a skill name.
  5. Open the new skill in the editor.
  6. Edit the SKILL.md file.
  7. Save your changes.
  8. Have an engineer approve the skill before your team starts using it.
When you create a skill, TeamCopilot normalizes the name into a lowercase slug. The name and slug are treated as the same value.

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Create a skill with AI

  1. Open TeamCopilot.
  2. Go to the AI Assistant.
  3. Ask the assistant to create a skill for a specific task or process.
  4. Describe what the skill should do, when it should be used, and any rules it must follow.
  5. Review the generated skill in the Skills tab.
  6. Refine the SKILL.md instructions as needed.
  7. Have an engineer approve the skill before your team starts using it.
This is useful when you want a fast first draft and prefer refining instructions instead of writing them from scratch.

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What gets created

Each skill lives in: 
.agents/skills/<slug>/
The main file is: 
SKILL.md
That file is the source of truth for the skill.

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Import existing skills

You do not always need to create a skill from scratch. You can also import existing skills into TeamCopilot. One practical option is to use the skills CLI on the machine where the TeamCopilot service is running.

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How to import skills

  1. Access the machine where TeamCopilot is running.
  2. Navigate to your TeamCopilot workspace directory.
  3. Run an npx skills add ... command from that workspace.
  4. Confirm the install target if prompted.
  5. Verify that the imported skills appear under .agents/skills/.
  6. Open TeamCopilot and go to the Skills tab.
  7. Have an engineer review and approve the imported skills.
  8. After approval, update permissions so the right people or the whole team can use them.
Because the CLI installs project-scoped skills into the workspace, imported skills end up in TeamCopilot’s .agents/skills/ folder, where TeamCopilot can discover them.

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Example commands

List the skills available in a repository before installing: 
npx skills add vercel-labs/agent-skills --list
Install a specific skill into the current workspace: 
npx skills add vercel-labs/agent-skills --skill frontend-design -a opencode
Install all skills from a repository into the current workspace: 
npx skills add vercel-labs/agent-skills --skill '*' -a opencode
The important part is to run the command from your TeamCopilot workspace directory so the imported skills are added to the workspace-level .agents/skills folder.

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After import

Imported skills are not automatically ready for organization-wide use. After importing them:
  • review the imported SKILL.md files
  • have an engineer approve them in TeamCopilot
  • set permissions for specific people or Everyone
That approval step is what makes the imported skills usable in a controlled team environment.

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What to put in SKILL.md

A good skill should tell the AI a few key things:
  • what the skill is for
  • when it should be used
  • how to perform the task well
  • what rules or constraints must be followed
A practical structure is:
  1. A short name and description
  2. Any required_secrets the skill needs
  3. The task or use case
  4. Step-by-step instructions for the AI
  5. Important rules, checks, and constraints
  6. References to relevant files, scripts, systems, or docs
If the skill needs credentials, declare them in frontmatter and reference them by placeholder instead of raw value: 
---
required_secrets:
  - SENDGRID_API_KEY
---

Use {{SECRET:SENDGRID_API_KEY}} in the Authorization header.
Users then add that key in Profile Secrets. If your team has a shared fallback, an engineer can provide it as a Global Secret.

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What good skill instructions look like

Good skill instructions are:
  • specific
  • actionable
  • scoped to a clear use case
  • opinionated where your team has standards
Good examples:
  • “Check package.json and package-lock.json versions before publishing.”
  • “Always review the last approved config before suggesting infra changes.”
  • “Summarize the issue in customer-friendly language before proposing next steps.”
Weak examples:
  • “Help with releases.”
  • “Be careful.”
  • “Do the right thing.”

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Tips for writing better skills

  • Keep one skill focused on one job or type of task.
  • Prefer concrete instructions over general advice.
  • Include decision rules when the AI needs to choose between options.
  • Reference scripts or internal docs when they are part of the process.
  • Update the skill as your team’s process changes.

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Approval and access

Skills are visible in TeamCopilot, but engineer approval matters. Before a skill becomes something the platform should rely on for team use:
  • an engineer should review it
  • an engineer should approve it
  • access permissions should be set appropriately for your organization
This helps ensure the AI only uses reviewed instructions in production team workflows.

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When to choose a skill instead of a workflow

Choose a skill when the main value is in the instructions themselves. Examples:
  • incident response guidance
  • code review standards
  • release process rules
  • support escalation playbooks
If you need Python code, structured inputs, or repeatable execution, create a workflow instead.