Skip to main content
Give Pi an LLM research trace — the fan-out search queries an LLM fired and/or the pages it read while answering a buyer’s question — and ask it to run the fanout-content-mining skill. Pi works a funnel (raw fan-out query to keywords to content ideas) and reads the trace through two lenses: similarity (ride the topics and formats LLMs already retrieve, so your content lands in the set they cite) and gap (find what the LLM kept re-asking or read but couldn’t get an answer from, and fill it so the LLM goes “huh, that’s new”). It’s the operational move behind Part 1 §1.2 — two search surfaces: you’re no longer ranking #1 for one phrase, you’re being citable across the machine’s whole fan-out. The output is a report of keywords and single-angle content ideas you can hand to Creating blogs.
🎬 Video planned: supply a research trace, mine the fan-out into keywords, pick angles optimized for LLM citation. See the shot-list.

​
Where the trace comes from

The whole play depends on watching the machine’s actual queries instead of guessing them. Synscribe ships two ways to capture a trace (per §1.2): a Chrome extension that reveals ChatGPT’s web-search queries, and Birdseye (a macOS app) that shows Claude Code’s queries and the pages it read. Either gives you the two inputs the skill wants:
  • Fan-out query log — the searches the LLM fired, in order. This is buyer intent, and where a query keeps recurring, unmet demand.
  • Pages-read log — the title, extract, and URL of each page it opened, plus the search term that surfaced it. This is what answered well versus what failed (a 403/404, or a “cannot answer from the content provided” / marketing-only page — both count as the answer being absent).
One log is enough; both is better. Paste in the seed buyer question too if you have it.

​
What Pi produces

The skill keeps the funnel — it doesn’t flatten it:
  1. Reads the fan-out and keeps the queries carrying real buyer intent in verbatim form (the exact string is the unit it decomposes), tagging each with intent, entities/jargon, a gap signal (did it hit a wall?), and a format signal (comparison / directory framing = the LLM wants side-by-sides).
  2. Fan-out to keywords, working each query in both directions: abstract up to the de-branded, category-level concept the buyer was circling (the most ownable keywords), and decompose down into the targets hiding inside a fat, entity-stuffed query. It flags a competitor’s proprietary API/webhook name as a low-ownership keyword and abstracts it up to the concept instead. Every keyword is tagged similarity or gap, with the source query (or read page title) as evidence.
  3. Keywords to content ideas, one keyword at a time, going wide: several single-angle pieces per keyword covering direct patterns (informational, comparison, decision, BOFU, regional cut) and the sharper lateral ones (consequence/economics, jobs-to-be-done, persona cut). Each idea names its type, title, the exact question it answers, the play, one line on why an LLM would cite it, and the must-include entities.
The highest-leverage move is originating the answer nobody has published. For the strongest gaps, the skill deliberately proposes net-new pieces — original data/benchmarks, a coined framework or taxonomy, a point-of-view thesis, or a brand announcement — the kind that makes an LLM cite you as a primary source rather than one more echo. Every net-new idea carries a visible NEEDS VALIDATION flag naming exactly what to verify before publishing (never fabricate a launch or a metric).

​
Read the two lenses right

Demand ≠ saturation. “The LLM kept asking” = demand. “The LLM found a good answer” = saturated. Demand plus no good answer is the best opportunity — weight those highest. Ride the retrieved formats for similarity; deliberately produce the missing shape for the gap.
The skill also filters for ownership: prefer category, decision, comparison, and taxonomy pieces where competitors appear as examples inside the piece, not as its subject. It won’t propose content that just explains a rival’s product — that educates buyers about a competitor and reads oddly coming from you.

​
Real prompt

Paste (or point Pi at) the captured trace and let it run the funnel: 
here's the fan-out query log and the pages-read log from a ChatGPT session where a
buyer was researching [the buyer question]. run fanout-content-mining: give me the
keywords and blog/LP ideas optimized to get me cited by AI, and rank the gaps worth
owning. flag anything that needs validation before I publish.
Pi returns a skimmable report: the seed question, the verbatim queries worth working, the keywords (each tied to its source query), the content ideas grouped under their keyword, and a closing ranked “gaps worth owning” table. Take the ACCEPT-worthy keywords through keyword evaluation before building, and hand the winning ideas to the blog writer.
Why (theory): Part 1 §1.2 Two search surfaces · Feeds: 2.2.2 Evaluate keywords · Creating blogs · Sibling how-tos: 2.8.1 Fix a page that won’t rank · 2.8.3 Bring your own DataForSEO key