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Terminal.skills
Skills/agent-workflow-packager
>

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.

#ai-agents#skills#workflow#codex#claude-code
MemoAshv1.0.0
Works with:claude-codeopenai-codexgemini-clicursor
Source
Trust Score
93/ 100
9.00×
Impact

Validation

Quality
93/ 100
Does it follow best practices?
5 PASS · 1 WEAK
Security
Passed
No known issues
Content review + injection scan
Impact
9.00×
10% → 90% agent success
Avg across 3 eval scenarios
Scored 7/6/2026 · skill v1.0.0
$
✓ Installed agent-workflow-packager v1.0.0

Getting Started

  1. Install the skill using the command above
  2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
  3. Reference the skill in your prompt
  4. The AI will use the skill's capabilities automatically

Example Prompts

  • "Review the open pull requests and summarize what needs attention"
  • "Generate a changelog from the last 20 commits on the main branch"

Documentation

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.

Information

Version
1.0.0
Author
MemoAsh
Category
Development
License
Apache-2.0