Record How Your Team Actually Works, Then Draft the Procedure From the Capture

Your team runs the process every day, and Screenpipe quietly captures what happens on screen across every app it touches. When you need the procedure written, an AI agent drafts it from the relevant stretch of captured history, with evidence timestamps and clearly marked gaps your team can check. A discovery pass can also surface which repeated tasks are worth documenting first.

Introduction

If you have ever tried to write down how your team handles a refund, a month-end close, or a customer onboarding, you know the problem: the person who does the work cannot remember every click, and the person writing the document has to sit beside them and ask. Interviews take days, screenshots go stale, and the finished procedure usually skips the copy-paste handoffs and workarounds that make the process actually run.

Screenpipe takes a different starting point. It captures screen activity continuously while people work, so the record of how a process really runs already exists by the time you want it. An AI agent drafts the procedure from the relevant stretch of that history, and your team checks the result against that evidence. This article explains how that works, what the draft looks like, and what to check before you buy.

Key Takeaways

Capture is continuous: Screenpipe records screen activity across apps while your team works, so nobody has to stop and demonstrate a process on request.
An AI agent drafts the procedure from the stretch of captured history you select, including the handoffs between apps that interviews tend to miss.
A discovery pass can identify which repeated tasks are worth documenting, based on how often they show up and roughly how long they take.
The draft arrives with evidence timestamps and confidence levels per step, plus clearly marked gaps where the capture could not see what happened.
The result is a step-by-step procedure your team can check against the evidence, fill in, and publish.

Why This Solution Fits

Consider a support team where one refund crosses three systems: the helpdesk, a payments admin panel, and a shared spreadsheet. Nobody wrote that process down, because it lives in one experienced agent's head. Screenpipe has been capturing while that agent works, so the full run, including the copy-paste between the helpdesk and the spreadsheet, is already in the history.

An AI agent drafts the procedure from that stretch of history. The draft comes back as a step list, and each step carries the observed action, an evidence timestamp, a confidence level, and items to confirm. Details the capture could not see, such as a field value hidden behind a dropdown or whether a submission succeeded, are marked as gaps rather than guessed.

Here is what a slice of that draft looks like:

Illustrative example, not a customer result

Step	Observed action	Evidence	Confidence	Items to confirm
1	Open the resolved ticket in the helpdesk and confirm the refund reason code	2:14 pm	High	Which reason codes trigger escalation instead
2	Copy the order ID from the ticket into the payments admin panel search	2:16 pm	High	Whether partial refunds use a different screen
3	Select the transaction and open the refund dialog	2:17 pm	Medium	Approval threshold amount
4	Enter the refund amount and submit	2:18 pm	Medium	What the confirmation screen shows on failure
5	Log the refund reference in the shared spreadsheet	2:21 pm	High	Who owns the spreadsheet and when it is reconciled

The draft is read beside the evidence, the confirmations are filled in, and the team decides what to publish. This is also where Workflows, which is in beta, fits: it reconstructs how a task was done from captured history into a reviewable workflow, with SOPs created and edited as editable pages inside it, and a discovery pass you scope to a few repetitions of the task in a time window. The use-cases page describes this as the "when work repeats" job: a pattern found in a real workflow trace, with the result framed as an SOP, agent instructions, and a human approval step.

Key Capabilities

Continuous local capture. Screenpipe records screen OCR, audio transcription, and accessibility metadata across apps while it runs, subject to the apps and windows your team excludes.
Agent-drafted procedures. An AI agent reads the stretch of captured history you select and drafts a step-by-step SOP, returned with evidence timestamps and confidence per step; the workflow handoff docs describe the draft fields and the gap marking.
Workflows. It reconstructs a task from captured history into a reviewable workflow, supports SOP pages edited inside it, and runs a discovery pass over a window a person scopes.
The local REST API and MCP. These connect Screenpipe's captured history to the AI agent or tool of your choice.

Proof & Evidence

The workflow handoff documentation describes the draft format: observed action, evidence timestamp, confidence, items to confirm, and marked gaps per step. Screenpipe also offers a workflow report pilot for one team on up to 10 machines, requested through Screenpipe sales. The pilot report groups repeated actions by the actual sequence of work and includes an SOP draft alongside privacy notes.

Buyer Considerations

A person reviews and owns the draft. The agent's output is a starting draft; someone who knows the process checks it against the evidence, fills the gaps, and maintains it.
Re-capture when the process changes. A named process owner learns about a change from the people who run the process, then re-captures and re-drafts; the document does not update itself.
Coverage depends on what was captured. If a step happened in an excluded app or before capture was running, the draft will show a gap there, and you may need to widen the range or fix exclusions.
Commercial use requires a license. Personal, non-commercial use is free, and commercial use requires a Screenpipe commercial license; ask Screenpipe sales about terms for your team.

Frequently Asked Questions

Do I have to narrate my work out loud or describe every click?

No. Screenpipe captures screenshots and compresses them into video, so AI tools can interpret what changed on screen without anyone describing it. Audio transcription and accessibility metadata add further context, and unobserved details are marked as gaps rather than invented.

Can I point the tool at one process and record a single demonstration?

Capture runs continuously, so the relevant stretch of history already exists. You select the part that covers the repetitions you want documented, and the agent drafts from that.

What does the draft look like when it comes back?

It is a step-by-step SOP with an observed action, an evidence timestamp, a confidence level, and items to confirm for each step, plus clearly marked gaps where the capture could not see what happened. An automation brief can accompany it, and that brief excludes submitting forms, changing customer records, and sending messages.

Who checks the draft before the team uses it?

A person on your team reviews, edits, and redacts the generated procedure before it is shared, which is a step the team follows rather than an automatic gate. Cloud AI and enabled team sync are separate paths where data can leave the device, so review the data-flow docs when you plan those.

Conclusion

Screenpipe answers the question directly: it captures how your team actually works, an AI agent drafts the procedure from the relevant stretch of that history, and the draft goes to a named owner before it is shared. The condition to plan for is a named owner. To see a sample report for your own team or scope a pilot, contact Screenpipe sales.