Breaking the cycle of the same competitor research every day
Marketer J.B. (@VibeMarketer_) shared a four-step prompt workflow on X for automatically tracking competitors with Kimi K3, and it spread. The key is a structure where, instead of the team doing new research every time, an agent reports only the parts that changed. This workflow isn't an official product feature of any company but a prompt design shared by an individual, and the original post cites a separately introduced "Kimi K3 memory engineering" guide.
Kimi K3 is an open-weights model released by Moonshot AI that supports up to a 1-million-token context and scored near the top on real-world automation benchmarks. Its long context and ability to carry out tasks autonomously are the technical foundation that makes this workflow work.
The four prompt steps, as written
The first step is building a market map. You ask, "Build a context graph with all competitors, products, launches, pricing, integrations, positioning claims, and trusted sources," to bundle what your team tracks into one living map.
The second is watching for changes. If you instruct it, "Compare today's evidence with the current graph, ignore what hasn't changed, and show only what's new, updated, conflicting, or stale," the team receives only what actually changed instead of repeating the research.
The third step is connecting changes to action. If you ask, "For every meaningful change, say what it affects and which team should respond," a pricing change is automatically sorted into a battlecard update, a new integration announcement into a partnership opportunity, and a positioning change into a need for response messaging.
The last step is making corrections permanent. You instruct, "When a person corrects a fact, source, relationship, or rule, save it and apply it to every future run," so the agent doesn't repeat the same mistake.
Why this workflow actually works
This structure works thanks to Kimi K3's long context. To keep the products, pricing, and positioning of dozens of competitors in one graph, the model has to hold that entire context at once, and the context of up to 1 million tokens technically supports that need. However, the specific use case of a "competitor analysis agent" is an application proposed by a user, not an official Moonshot AI announcement, so its actual accuracy and false-positive rate are not figures validated by official benchmarks.
The prompts themselves can be reproduced by entering them in order into the Kimi K3 chat interface or API without any special tooling, making this a low-barrier method you can try right away without extra development.
What to check before adopting it
To use this workflow in real work, a person has to decide first how much to trust the sources that go into the graph. If you let the model decide on its own what a "trusted source" is, outdated information or inaccurate rumors can get mixed into the graph. On the first run, a person has to review the results carefully and go through the correction step several times before accuracy reaches a usable level.