X (Twitter) Mention Monitor with OpenCode
Every sweep does the same four things: mentions, quote tweets over your own recent posts, a thread walk on anything promising, and an author lookup. OpenCode weighs almost nothing to set on a single focused job, and this one is focused enough that the interesting question is which model you want doing the tone judgement at the end of it.
Watch it work before it's live
Run a staged conversation — no account needed. The agent handles it for real while a simulated world answers its tool calls; nothing touches real accounts, and nothing is actually sent.
so the free tier is gone and nobody thought to mention it, cool
Quote tweets our pricing update from this morning. 1,340 reposts, 4,800 likes and 210 replies in three hours, from an account with 48,000 followers that has recommended us twice before. The replies are split: several point out the grandfather clause in our own post, several say they already cancelled. Our original post is still live and has not been updated.
Set up in minutes
Using this template drops you into a guided setup. It asks exactly this, nothing else:
Connect X
One sign-in. The agent acts through your account, scoped to what this template uses.
Connect Slack
One sign-in. The agent acts through your account, scoped to what this template uses.
Brand handles
Every handle worth watching, starting with your brand account and any founder or product accounts people tag instead. Add handles you have renamed away from, because people keep using those for years.
Ping policy
What lifts a mention above the noise: follower counts, tone, whether somebody asked a direct question, and which Slack channel the ones that qualify should land in.
Runs on OpenCode
Preselected for this page — connect your OpenCode account during setup, or switch to NoClick's built-in models with one click.
Watch it handle a test run
A staged conversation against a simulated world — then it’s live.
Why OpenCode for this agent
Quote tweets are half the job
Criticism migrates into quote tweets, which never surface in a notifications tab. Sweeping those alongside mentions is what makes the coverage real.
Follower count shapes the answer
A large account being wrong costs more than a small account being right. That weighting comes from a lookup, and the model only has to apply it.
Before you fork
How do we compare two models on the same mentions?
Point the agent at the staged mentions, read the three Slack messages, change the model, then read them again. The section that separates models is ANGLE, since anything can quote a post and report a follower count, while writing two sentences you would genuinely send is where a weaker model produces something bland enough to be useless.
Run it with a different agent
Put X (Twitter) Mention Monitor to work on OpenCode
Free to start. Guided setup, a test run against staged conversations, and it's live.