Building an Internal Playbook From Your Best Performers' Work
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Building an Internal Playbook From Your Best Performers' Work
Screenpipe builds that playbook from observed work instead of interviews. It captures how your top performers actually move across their apps, an AI agent drafts the playbook entry from the stretch of captured history you pick, and your team finishes and owns the result. The Screenpipe use-cases page frames this as the "when work repeats" job.
Introduction
Ask your best people how they do a task and you get a tidy summary that skips the copy-paste handoffs, the workaround in the second portal, and the spreadsheet check that catches most errors. Document it with interviews and screen recordings and the result is often stale before it is finished.
Screenpipe takes a different route. It runs in the background on macOS, Windows, and Linux, capturing screen activity, audio transcription, and accessibility metadata across every app a person uses. When you want a playbook entry, the history is already there: an AI agent drafts the procedure from the stretch of captured history you select.
Key Takeaways
- Screenpipe captures cross-application work history continuously and locally, so the raw material for a playbook entry already exists when you ask for it.
- An AI agent drafts the SOP or workflow guide from the stretch of captured history you select, so the entry includes the copy-paste handoffs interviews usually miss.
- A discovery pass over a window you scope produces a report with observed steps, estimated repetition counts, approximate active time, friction, and evidence moments.
- Drafts are finalized by the team before they are shared, and a named process owner re-captures and re-drafts when the process changes.
Why This Solution Fits
Consider a concrete case. A support lead's Screenpipe has been capturing while she works, and her history includes one refund that crosses the helpdesk, a payments admin panel, and a spreadsheet. Nobody scheduled a recording session; the capture was simply running. She asks an AI agent to draft the procedure from that stretch of history, and the draft includes the copy-paste handoffs between systems that interviews usually miss. She edits the draft, then posts it as the team's refund SOP.
The draft she reviews looks like this.
Illustrative example, not a customer result
| Step | Observed action | Evidence timestamp | Confidence | Items to confirm |
|---|---|---|---|---|
| 1 | Open the refund request in the helpdesk and verify the order ID | 09:14 | High | None |
| 2 | Look up the payment in the payments admin panel | 09:16 | High | Which permission level is required |
| 3 | Copy the transaction ID into the reconciliation spreadsheet | 09:19 | Medium | Whether the spreadsheet step applies to partial refunds |
| 4 | Issue the refund and paste the confirmation back into the helpdesk ticket | 09:22 | High | None |
| Gap | Field values entered in the admin panel form | Not captured | Gap | Ask the performer to demonstrate this step |
This is the fit for the "internal playbook of our best performers" question. The playbook is written from observed work across the real mix of apps, portals, and spreadsheets, not from a person reconstructing their steps from memory after the fact. The workflow handoff docs describe the draft format: a step-by-step SOP and an automation brief, returned for review, with evidence timestamps and confidence levels per step, and unobserved details marked as gaps rather than guessed.
Key Capabilities
- Continuous local capture. While Screenpipe runs, it records screen OCR, audio transcription, and accessibility metadata across apps, subject to the apps and windows the user or an admin excludes. Screenshots are compressed into video, so AI tools can interpret what changed on screen without anyone describing it.
- Workflows, which is in beta. In the Screenpipe app, Workflows reconstructs how a task was done from captured history into a workflow a person can review, with SOPs as editable pages inside it. The workflow discovery docs describe a discovery pass a person scopes to a concrete boundary, such as two or three repetitions of a task in a time window, producing a report with observed steps, estimated repetition counts, approximate active time, friction, and evidence moments.
- Local-first storage. In local-only setups, captured history stays on the employee's device, which matters when the work involves customer data, finance systems, or health information.
- Agent-drafted procedures. An AI agent reads the relevant stretch of captured history through chat, the local REST API, or MCP, and drafts the SOP or workflow guide from it.
Proof & Evidence
The refund SOP example above is illustrative, but each part of it maps to documented behavior. The use-cases page describes the "when work repeats" job, and the workflow handoff docs describe drafts that list, per step, the observed action, an evidence timestamp, a confidence level, and items the operator must confirm, with unobserved details marked as gaps and a new demonstration requested. The handoff is considered ready when a colleague can follow the draft and verify the result without guessing.
Buyer Considerations
- A person reviews every draft. The agent's SOP is a draft that the process owner or the performer edits and approves. Treat the review as a required step in your rollout, not an optional one.
- Plan for maintenance. When a process changes, a named process owner re-captures the new way of working and has the agent re-draft the entry. Assign that owner before you publish the first playbook page.
- Coverage depends on capture. The playbook can only describe what was captured after exclusions and over the period capture has run, so onboard your top performers first and let capture accumulate before drafting.
- Commercial use requires a license. Personal, non-commercial use is free, but running Screenpipe across a team is commercial use, so confirm terms with Screenpipe sales during evaluation.
Frequently Asked Questions
Does the top performer have to narrate their work or record themselves on purpose?
No. Screenpipe has been capturing in the background while they work, and the relevant stretch of history is selected afterward. Because screenshots are compressed into video, AI tools can interpret what changed on screen without the performer describing anything.
Can the playbook update itself when the process changes?
No. When a process changes, the named process owner learns of it from the people who run it, re-captures the new way of working, and has the agent re-draft the entry for review.
How does the agent know what it did not see?
The handoff draft marks unobserved details, such as hidden clicks or field values, as gaps instead of inferring them, and requests a new demonstration for those steps.
Can we use this to rank which tasks to automate first?
The time breakdown pipe and discovery reports supply estimates of where time went, and a person validates them. Treat the top candidates as a shortlist to validate in a pilot rather than a verified ranking.
Conclusion
Screenpipe builds an internal playbook the way the work actually happens: continuous local capture supplies the history, an AI agent drafts the SOP from the relevant stretch of it, and your people own the result. The key condition is a named owner for each playbook entry, so the playbook gets maintained and re-drafted when the process changes. If you want to see this on your own team's work, talk to Screenpipe sales about a pilot and commercial terms.