Seller field guide · 3 min read
How do you assess Linear data for an AI license?
Evaluate a defined set of company-owned product or engineering decisions before offering a Linear workspace archive. Issue descriptions and current status can describe work, but a buyer may need discussion and outcome context that your selected export does not preserve. Confirm both technical coverage and permitted use.
A Linear CSV is not a complete workspace replica
Linear documents workspace and issue-view CSV exports, with permissions that vary by role and plan. Its export guide lists issue fields and states that CSV exports omit attachment files, although descriptions may contain attachment links.
Use those documented fields as a starting checklist, then inspect the actual output. Do not infer that a status field includes its change history, or that an attachment URL remains accessible outside the workspace. Private-team material should be selected deliberately rather than included for convenience.
Source notes: Linear: exporting data
Find decisions that explain product work
A useful evaluation might follow a bug from triage to prioritization and completion, or a product request through a documented decision not to build it. Negative decisions can be informative when the reasoning is present. Do not relabel cancelled work as a successful implementation.
Distinguish internal product reasoning from customer request text copied into an issue. The company may own its analysis while the supporting customer material remains confidential. If removal of that material leaves the decision understandable, describe the narrower permitted sample honestly.
Choose a defensible evaluation scope
This is our suggested scoping framework, not a statement that a buyer accepts every record below. Review contractual rights and the intended use before preparing a sample. Technical access to a record does not establish permission to license it.
| Record or context | What to evaluate | Boundary to keep |
|---|---|---|
| Issue description and acceptance criteria | Problem framing and expected result | Remove customer identifiers, secrets and restricted content |
| Project or milestone context | How a decision fits a larger plan | Exclude confidential roadmap commitments |
| Attachment and customer-request references | Whether context exists outside the CSV | Review separately and identify missing material |
Evaluate structure before transforming the archive
Compare one completed issue, one cancelled issue and one issue with important external context against their exported representations. Record what the export preserves and loses. This small check prevents a large transformation project from being built around the wrong assumption.
Ask the buyer whether current issue text is sufficient or whether it needs a historical decision sequence. If that sequence is not available through an allowed method, say so. Do not synthesize history from today’s title, priority and status.
Illustrative scenario · fictional company
Linear: a fictional scoping example
A fictional product team considers a Linear project about internal billing-tool improvements. It excludes customer requests, security issues and private roadmap discussions. The first CSV sample retains useful descriptions but references screenshots that are not included. The team labels those references as missing context and selects another group of self-contained issues. A cancelled proposal remains in the sample with its actual reason for cancellation.
When to pause or narrow the scope
Client work and customer files are excluded from this starting scope. Assess company-owned internal records separately from personnel, health, privileged or NDA-restricted material.
- The selected export cannot demonstrate the type of history the buyer needs.
- Issues depend on confidential customer requests that cannot be reused.
- Private roadmap or security material cannot be reliably separated.
Your preparation checklist
- Select owned projects and confirm export permissions.
- Check included fields and attachment references.
- Identify completed, cancelled and unresolved work accurately.
- Separate customer-provided context from internal analysis.
- Describe export limitations before requesting a valuation.
Start with a description of the records. The quote form does not ask you to upload a dataset.
Discuss your dataSources and further reading
- Linear: exporting data · Checked October 26, 2026
This is general information, not legal advice or confirmation that a buyer will accept your records. Published buyer ranges describe their programs, not an offer for your dataset. Buyers may pay QuoteMyData when a deal closes; see how we are paid.
