Create a watchlist

Keep a fixed set of competitors, influencers, subreddits, or URLs under continuous watch by building a URL-based dataset and attaching a listening agent focused on what matters.

A "watchlist" is the set of exact accounts, profiles, subreddits, or URLs you want to keep an eye on over time — your rivals' accounts, the loudest influencers in your category, the subreddits where your niche lives, or specific posts you care about. If you run a B2B SaaS project-management tool, that might be the X and LinkedIn accounts of two competing tools, a couple of productivity influencers, and r/projectmanagement. buzzabout has no literal Watchlist button or object — instead you assemble one from two real building blocks: a URL query dataset that collects from the exact profiles you name, and a listening agent that re-scrapes it on a schedule and posts a focused digest of what's new.

Use this pattern to track competitors, track influencers, monitor your niche by exact profiles or subreddits, or watch specific URLs — anything where you already know who or what you want to follow.

There is no Watchlist object or page in buzzabout. A watchlist is a use-case pattern you build from two real pieces: a URL query dataset (Step 1) and a listening agent (Step 2).

Prerequisites

A set of profile, account, subreddit, or post URLs you want to watch. Supported platforms are Reddit (including subreddits), TikTok, YouTube, X, Instagram, and LinkedIn.

Walkthrough

Add URL…

In any chat, start a research preview and switch it to a URL query. Paste each profile, account, subreddit, or post URL into the Add URL… input — every accepted URL becomes a chip in the list. For the project-management example you might add the X and LinkedIn accounts of two rival tools, a couple of productivity influencers on YouTube and TikTok, and the r/projectmanagement subreddit.

You can add up to 100 URLs to a single query, and they all go into one dataset. Each host is validated as you add it:

An unsupported host is rejected with Unsupported URL — must be reddit, tiktok, youtube, x, instagram, or linkedin.
A repeat is rejected with URL already in list.

Start research

A URL query behaves differently from a keyword query. There is no research-depth picker and no credit badge — the sources are derived from the URLs you pasted, not chosen separately. Instead of a mention estimate, the preview shows a profiles-found count — for example 7 profiles found — confirming how many of your pasted URLs were recognised.

Click Start research to collect from those profiles into a single dataset. This is the watchlist dataset your listening agent will watch.

New listening agent

Now attach a listening agent so the watchlist keeps refreshing. Open the Listening agents page from the left sidebar (/agents), or open a project and switch to its Listening agents tab. Click New agent (or Add agent on a project tab) to open the New listening agent modal, whose first step is titled Configure what this agent tracks and when it runs.

Instructions

Give the agent a Name — the placeholder reads e.g. Daily AI mentions. Something like Competitor & niche watch fits this example. Then write Instructions, which answers What should this agent summarise from the scraped posts? This field is where you focus the watch: instead of generic updates, tell the agent exactly what to surface each run. Pick the angle that matters to you, for example:

Complaints monitoring — "Flag complaints and frustrations about rival project-management tools."
Gotchas — "Call out pricing changes, deprecations, and migration pain people mention."
Trends analysis — "Summarise emerging trends and shifts in how the category is discussed."
Topics analysis — "Group new posts by topic and highlight which topics are growing."

A focused instruction is what turns a raw scrape into a digest worth reading.

Datasets

Select the watchlist dataset you collected in Step 2 from the Datasets picker. This is a multi-select, so one agent can watch several datasets at once — for example a competitors dataset and an influencers dataset. At least one is required. If the list is empty you'll see No datasets in your account yet with the hint Run a research from any chat first; the resulting dataset will appear here.

Mentions per run

Choose Mentions per run — the options are 10, 25, 50, 100, 150, and 200, defaulting to 50. This sets how many mentions each scheduled run pulls from the watched profiles. A larger count means more data per run, and more cost per run. It is a separate control from the initial Start research collection.

Schedule

Pick a Schedule: Daily or Weekly. Weekly adds a day picker. Then choose a time — note it is in (UTC), so convert from your local time. A daily morning slot is a sensible default for keeping up with competitor activity.

Create agent

Optionally click Next to advance to the destinations step and deliver each digest via Slack, email, or webhook — this is optional, and the agent still posts summaries into its own chat with no integrations. Then click Create agent to save. You land back on the Listening agents page with the new agent in the side panel.

From now on, each run re-scrapes your watchlist, grows the dataset with new mentions, and posts a focused digest you can read like a chat.

Now analyse it

The digest is the daily readout, but the real value is that every run feeds fresh mentions back into the watchlist dataset — so the same analysis you run on a one-off collection stays current automatically. Once the agent has been running for a while, open the dataset and dig in:

Run analysis skills on the fresh data. Apply skills to surface pain points, feature requests, objections, and competitor share of voice over the mentions your watchlist keeps pulling in. See use skills.
Save a live Library view. Filter the growing mentions by sentiment, platform, dataset, or posting date and save the view — it keeps showing the latest mentions as the agent adds them. See use insights.
Keep competitor and audience work current. Because the dataset keeps growing, the watchlist is the easiest way to keep competitor research and audience analysis from going stale.

Gotchas

There is no "Watchlist" feature. A watchlist is the two-step pattern above — a URL query dataset plus a listening agent. Don't look for a Watchlist button or page; there isn't one.
Six platforms are supported. A URL query accepts Reddit (including subreddits), TikTok, YouTube, X, Instagram, and LinkedIn. Anything else is rejected inline with Unsupported URL — must be reddit, tiktok, youtube, x, instagram, or linkedin, and duplicates with URL already in list.
Up to 100 URLs per query. A single URL query is capped at 100 URLs. To watch more than that, split them across more than one dataset and attach the same agent to all of them.
The URL flow has no depth or credit picker. Unlike a keyword query, a URL query hides the depth tabs and the credit badge — sources are derived from the URLs, and the preview shows a profiles-found count instead of a mention estimate.
Mentions per run is the agent's control, not the collection's. The agent's Mentions per run (10–200, default 50) sets how much each scheduled run scrapes; it is separate from the initial Start research collection.
Schedule is Daily or Weekly only, in UTC. Times are fixed hours in UTC. Weekly adds a day picker.
Every run consumes credits. Each scheduled or manual run re-scrapes the watchlist, which consumes credits scaling with Mentions per run. There is no per-run credit figure shown in the agent UI; the cost comes from the underlying scrape.