azukiazusa1/sapper-blog-app/en
Claude Code and agent skill by azukiazusa1/sapper-blog-app
- community
Checking that the SKILL.md file is still in the repository.
Install
npx vskill@latest install azukiazusa1/sapper-blog-app/enSKILL.md
Codex's Record & Replay is a feature that turns your actions on macOS into a reusable skill by demonstration. Record & Replay shines when a workflow is routine and you repeat it frequently, or when showing the actual steps is easier to understand than describing them in a prompt. Examples include filing expense reports, clocking in or entering work hours on an attendance app, and producing regular reports. By recording these actions as a skill and combining it with Computer Use, you can expect to hand tedious, repetitive chores off to AI. Record & Replay is especially well suited to workflows whose steps are stable and whose success criteria are clear.
In this article, I'll walk through actually trying out Record & Replay. This time, I'll use a fictional attendance app called "TimePort" running locally to record a clock-in and work-hour entry workflow, and show an example of reusing it as a skill.
Getting Started with Record & Replay
As a prerequisite for using Record & Replay, you need the Codex App (the desktop app). It is also currently available only on macOS. Install the Codex App from the link below.
https://developers.openai.com/codex/app
Once you've installed the Codex App, select "Plugins" from the sidebar. Type "Record" into the search box in the plugin list, and the Record & Replay plugin will appear. Click "Add plugin" to add the Record & Replay plugin.
[Image: untitled]
The plugin consists of the following two components.
- The
event-streamMCP server - The
record-and-replayskill
The event-stream MCP server provides tools that record the user's actions and save them as JSON files. The record-and-replay skill actually calls the event-stream MCP server to record the user's actions, and provides the procedure for creating a skill based on the recorded content.
The event-stream MCP server records the user's actions by combining macOS's accessibility API with input event monitoring. It exposes the lifecycle of starting, checking the status of, and stopping a recording as the following three main tools. Recording is built on top of the Computer Use machinery, so just like Computer Use, recording requires accessibility and screen recording permissions.
event_stream_start: Starts recording the user's actions. Confirmation is requested before recording begins. Once recording has started, it waits until the user signals that they have finished. Recording can last up to 30 minutes.event_stream_status: Checks the recording status and the paths to the generated metadata and event files.event_stream_stop: Stops recording the user's actions and returns results such as the save location.
The actions that get recorded include the following.
- Information about the apps and windows used
- Mouse and keyboard actions and their target elements
- Focused UI elements
- Selected text
- The accessibility tree and its changes
The recorded actions themselves are not returned directly as the MCP response; instead, they are saved as the following files. When the user finishes and event_stream_stop is called, the save paths are returned, so Codex reads those files to check their contents.
session.json: recording duration, end reason, and so onevents.jsonl: the actual action events
The contents of events.jsonl after recording an actual attendance-app session look like this.
As an example, let's look at the event for selecting the date of a work-hour entry.
kind is mouse.click, so we can tell the event was recorded as a mouse click. Since app.name is Google Chrome and window.title is TimePort | 勤怠管理, we can tell which app and which window the action took place in. The description of mouse.target, "6/15(月)の工数を入力" (Enter work hours for 6/15 (Mon)), indicates the purpose of the click. The role AXCheckBox and the subrole AXToggleButton indicate the kind of UI element under macOS's accessibility API. Because the action is stored as UI element information obtained from the accessibility API rather than as screen coordinates, the intent of the action can be understood and reproduced even if the screen layout changes.
The record-and-replay skill provides the procedure for creating a skill that reproduces a workflow based on recorded events like these. The full text of the skill is as follows.
<details> <summary>Contents of the record-and-replay skill</summary>
This is the start of the file. Read the full SKILL.md on GitHub.
Security
- Security scanPassed, 95 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingcontents/blogPost/en/workflow-to-reusable-skill.md
Popularity
- 7-day trend score
- 5
Not refreshed yet.
Works with
Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.
- Claude Code
- Cursor
- GitHub Copilot
- Windsurf
- Codex
- Gemini CLI
- Cline
- Amp
- Roo Code
- Goose
- Continue
- Kilo Code
- Junie
- Trae
- OpenHands
- Qwen Code
- AI Agent
- Tabnine Chat
- Sourcegraph Cody
- Amazon Q
- Firebase Genkit
- Sweep
- Devin
- Phind
- Mentat
- Claude Desktop
- Aider
- GPT Pilot
- Plandex
- bolt.new
- v0
- Replit Agent
- Anse
- TypingMind
- BetterChatGPT
- LibreChat
- Warp AI
- Pieces
- Fig AI
Embed badge
[](https://verified-skill.com/skills/azukiazusa1/sapper-blog-app/en)