open-record-replay

name	open-record-replay
description	Record a user-demonstrated macOS workflow with the Open Record/Replay CLI, inspect the captured event stream, prepare a skill evidence package, and hand it to the host agent's native skill creator. Use when the user asks the agent to watch them perform a task, record a workflow, or create/refine a reusable Computer Use skill from a demonstration.

Open Record/Replay

Use this skill to learn a user-demonstrated macOS workflow through Open Record/Replay.

Open Record/Replay records evidence. It does not create the final skill by itself. After recording and packaging, invoke the current host agent's native Skill Creator or skill authoring flow.

CLI Mapping

The OpenAI Record & Replay tool names map to this project's CLI flow:

event_stream_start  -> node bin/orr.js record start
event_stream_stop   -> node bin/orr.js record stop
metadataPath        -> runs/sessions/<session-id>/session.json
eventsPath          -> runs/sessions/<session-id>/events.jsonl
skill handoff       -> node bin/orr.js skill prepare

Run commands from the Open Record/Replay repository root unless the user provides another path.

Recording Workflow

1. Check permissions first:

node bin/orr.js permissions check

2. If permissions are missing, run:

node bin/orr.js permissions request

Tell the user which macOS permissions are missing and wait for them to enable the permissions before recording.

3. Start recording only when the user is ready:

node bin/orr.js record start --name my-workflow --out runs

4. After recording starts, do not poll or wait in a loop. End your turn and ask the user to perform the workflow and tell you when they are done.

5. When the user says they are done, stop the recording:

node bin/orr.js record stop latest --out runs

6. Validate the recording:

node bin/orr.js session validate-recording latest --out runs

7. Prepare a skill evidence package:

node bin/orr.js skill prepare latest --runs runs --out skill-inputs

8. Confirm the package path and inspect the package files:

skill-inputs/<session-id>/
├── README.md
├── events.jsonl
└── session.json

If The User Cancels

If the user says they cancelled recording, do not continue to skill creation.

If needed, inspect session.json or orr_session.json to confirm cancellation, then acknowledge that no skill will be created from that recording.

Interpreting Events

Treat events.jsonl as the primary evidence. session.json gives timing and file paths only.

When inspecting events, pay attention to:

• App and window attribution.
• window.changed events.
• mouse.click and mouse.drag targets.
• keyboard.text_input and keyboard.submit targets.
• selection.changed, selected text, and selected items.
• AX full-tree or diff payloads.

Do not infer unsupported actions from generic targets such as AXGroup, AXScrollArea, or low-confidence action clusters. If the workflow is unclear, ask the user what the intended action was.

Creating Or Refining A Skill

If the recording contains enough evidence to identify a reusable workflow, create or refine a real discoverable skill by using the current host agent's native Skill Creator.

Do not stop at a summary, replay plan, or Markdown runbook unless the user explicitly asks for only that.

When creating the final skill:

• Treat the recording as evidence of the user's intended outcome, not a requirement to reproduce every raw event.
• Prefer stable app/window/control targets from the event stream.
• Include verification steps after side-effect actions such as send, upload, create, post, save, or publish.
• Parameterize reusable inputs such as recipient, file path, URL, search query, prompt, or message text.
• Avoid coordinate-only instructions unless no semantic target is available.
• Ask the user when a key action or destination is ambiguous.

Privacy

Do not include sensitive values from recorded events in summaries or generated skills.

Treat the following as sensitive:

• Passwords.
• OTPs.
• API keys or tokens.
• SSNs, passports, financial account or card numbers.
• Private personal, medical, legal, HR, or customer data.
• Private local file paths or document names when not necessary for the workflow shape.

Use placeholders or generic descriptions when the skill needs to mention a sensitive value.