Agent Workflow Packager logo

Agent Workflow Packager

Organization
TerminalSkills
agent-workflow-packager

Converts repeated AI coding-agent workflows into reusable skill packages with triggers, guardrails, examples, and verification checks. Use when a user wants to turn a prompt, checklist, AGENTS.md section, Claude/Codex workflow, or recurring agent task into a portable SKILL.md.

Overview

PublisherTerminalSkills
Repositoryskills
Skill nameagent-workflow-packager
Stars
155
Forks
21
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by TerminalSkills on GitHub. Read the source before you install it.

Installation

Install the Agent Workflow Packager AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/TerminalSkills/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/agent-workflow-packager .claude/skills/agent-workflow-packager
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Workflow Packager in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Workflow Packager on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Workflow Packager is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Agent Workflow Packager

Overview

Package a repeated coding-agent workflow into a reusable SKILL.md that another agent can follow without rediscovering context. The output should include clear activation triggers, bounded instructions, examples, and a verification checklist that proves the workflow worked.

Instructions

When a user asks to turn a prompt, checklist, runbook, AGENTS.md section, or repeated agent task into a skill, follow this process.

Step 1: Extract the recurring workflow

Identify:

  • The user persona and recurring task.
  • The input artifacts the agent needs, such as a PR URL, failing test output, issue, design screenshot, or repo path.
  • The expected output artifact, such as a patch, review, migration plan, report, or generated file.
  • The steps the user repeats manually.
  • The failure modes the user keeps correcting.
  • The command or inspection that proves the task is done.

If the workflow is a one-off request, say that it is not yet a good skill candidate and offer a shorter checklist instead.

Step 2: Define activation triggers

Write a description that includes both what the skill does and when to use it. Include specific trigger phrases from the user's workflow, such as:

  • "review this PR for security issues"
  • "turn this runbook into an agent skill"
  • "replay our release checklist"
  • "debug failing GitHub Actions"
  • "convert this AGENTS.md workflow into SKILL.md"

Do not use broad triggers like "help with code" or "improve productivity".

Step 3: Bound the skill

Add constraints that prevent overreach:

  • What the skill should do.
  • What it should not do.
  • When it must ask for missing inputs.
  • Which files, commands, or tools are safe to use.
  • Which user approvals are required before writes, deploys, purchases, or public posts.

Keep the skill focused on one reusable job. Split unrelated work into separate skills.

Step 4: Write the package

Create a SKILL.md with:

yaml
---
name: short-kebab-case-name
description: >-
  What the skill does and when to use it, including concrete trigger words.
license: Apache-2.0
compatibility: "No special requirements"
metadata:
  author: github-username
  version: "1.0.0"
  category: development
  tags: ["tag-one", "tag-two", "tag-three"]
---

Then add:

  • # Skill Name
  • ## Overview
  • ## Instructions
  • ## Examples
  • ## Guidelines

Use imperative, step-by-step instructions. Avoid long background essays.

Step 5: Add examples

Include at least two realistic examples. Each example should show:

  • The user's input.
  • The agent's action plan.
  • The expected output shape.
  • The verification step.

Use concrete repo names, files, commands, and outputs. Do not use placeholders like foo, bar, or lorem ipsum.

Step 6: Add verification

End with a checklist the agent can run before claiming success:

  • Frontmatter parses as YAML.
  • The skill has clear triggers.
  • The workflow has a bounded input and output.
  • The examples are realistic.
  • The verification commands or review checks are explicit.
  • The skill stays under the target length for the host catalog.

Examples

Example 1: Package a PR review workflow

User request: "We always ask agents to review payment PRs for auth, idempotency, Stripe webhook replay, and tests. Turn that into a reusable skill."

Agent output shape:

markdown
---
name: payment-pr-reviewer
description: >-
  Reviews payment-related pull requests for authorization, idempotency, webhook
  replay safety, money movement bugs, and test coverage. Use when reviewing PRs
  that touch Stripe, billing, subscriptions, invoices, or checkout code.
license: Apache-2.0
compatibility: "Any repository with payment code"
metadata:
  author: acme-dev
  version: "1.0.0"
  category: development
  tags: ["payments", "code-review", "stripe", "security"]
---

The skill includes a review checklist, severity format, and verification step: inspect changed payment files, read related tests, and confirm webhook replay cases are covered.

Example 2: Package a release checklist

User request: "Every release we paste the same steps: update changelog, run tests, build Docker image, tag, push, and draft GitHub release."

Agent output shape:

markdown
---
name: release-checklist-runner
description: >-
  Runs a project's release checklist from changelog update through tests, image
  build, git tag, push, and draft release notes. Use when preparing a versioned
  release or turning a manual release runbook into an agent workflow.
license: Apache-2.0
compatibility: "Requires git and the project's build toolchain"
metadata:
  author: acme-dev
  version: "1.0.0"
  category: devops
  tags: ["release", "changelog", "git", "ci"]
---

The skill requires the agent to detect the package manager, read existing release docs, run the repo's test and build commands, and stop before publishing unless the user approves.

Guidelines

  • Prefer evidence from past prompts, commits, issues, and runbooks over guessing.
  • Keep one skill scoped to one recurring workflow.
  • Use host-agnostic language unless the workflow truly depends on one agent.
  • Make destructive, public, or paid actions approval-gated.
  • Include exact file paths and commands only when they are stable for the target project.
  • If the workflow needs extra templates or scripts, mention those files in the skill and keep them next to SKILL.md.
  • Do not package a vague preference as a skill. A good skill has repeated inputs, repeated steps, and a clear done condition.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agent Workflow Packager AI skill do?

Converts repeated AI coding-agent workflows into reusable skill packages with triggers, guardrails, examples, and verification checks. Use when a user wants to turn a prompt, checklist, AGENTS.md section, Claude/Codex workflow, or recurring agent task into a portable SKILL.md.

Why use Agent Workflow Packager on TypingMind?

Because you install it once and use it with any model. Agent Workflow Packager is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Workflow Packager in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TerminalSkills/skills/tree/main/skills/agent-workflow-packager. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agent Workflow Packager?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Workflow Packager?

As many as you like. As long as a model supports skills, you can use Agent Workflow Packager with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Workflow Packager AI skill free?

Yes. It is published on GitHub by TerminalSkills under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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