AI visibility tasks in Linear: from answer gap to issue

Finseo, the one vendor page built for this query, makes the case in a sentence: much of generative engine optimization is engineering work, such as structured data, crawler access for GPTBot and PerplexityBot, server rendering, internal linking and page speed. Work like that is shipped from a tracker, not from a dashboard that nobody on the platform team opens.

Finseo does the filing itself, with a personal API key, an Export to Linear button and automation rules that fire above a minimum impact. Its tasks are generated by its own agent. Linkeddit takes the other route: it supplies measured evidence and your own assistant does the drafting, so a person reads each issue before it exists. Neither approach is free of cost, since automation files faster and review files fewer, worse issues less often.

On Linkeddit's test project the queue held two cards for one measured question. One, a competitor_won card, said a named competitor holds the answer, with 3 competitor mentions and 12 competitor citations behind it. The other, a content_gap card, said the brand was absent in 3 samples. Both carry a rank, a confidence of 1 and an evidence score of 22.

Only content_gap and competitor_won cards are ever generated. The tool's own notes say the other five types exist in the schema and have no evidence source yet, so a short queue does not mean nothing is left to do. Statuses are suggested, todo, in_progress, done and dismissed, and a dismissed card keeps suppressing its finding permanently, so dismiss deliberately.

Start from one gap, not the whole queue. answer_gaps_get returns the question where an engine named someone else or nobody, and answer_sources_fetch returns the pages the engines cited for it. Read those sources before you write the issue, because the fix is usually to match what the cited page has and yours lacks: a comparison, a plain answer in the first paragraph, a fact the engines agreed on.

Put three things in the Linear issue: the buying question, the cited source links, and a plain definition of done, such as the page published and the question re-run. Linkeddit's fix tools can save a draft for your approval, and Claude can attach the saved draft to the issue with create_comment. The publish step stays with whoever owns the site.

Then measure. The same question, run again, shows whether the gap closed. Engines vary between samples, so judge a fix over repeated runs, not one answer.

The first is filing every card. Linear's own prompts ask the assistant to flag uncertainty and show proposed issues for review before creating them, and that habit fits here: rank by the card's rank and confidence and file the top handful.

The second is stale context. The Unblocked guide notes that MCP is pull-based, so an agent sees an issue as it was when fetched; have Claude re-read the issue before it updates it. The third is scope: Unblocked also flags that Claude Code warns when an MCP tool returns more than 10,000 tokens, so ask for a state or limit instead of the whole queue.

The fourth is the wrong workspace. Linear's docs say each workspace needs its own authentication context, so if you manage issues for more than one team, confirm which workspace Claude is signed in to before it files anything. Ask it to name the team and the label it will use in every draft, so a misrouted issue is caught at the review step and not in someone else's backlog.