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The purpose of this agent is to help you answer a whether-to question. We have these all the time in marketing. Whether to spend money on TV or outdoor, for example; where to start targeting a new audience segment; whatever it is. The best people to answer a question like this are your customers, of course. Or, the next best thing, synthetic versions of them. The first thing you do with this agent is have a conversation with in in Telegram to get to your whether-to question. In the PDF, you'll see one of our whether-to questions: should we sell our technology directly to cinema chain CMOs or approach their media agencies instead? In Telegram, you also establish the audience segment you want to talk to by sharing, for example, what you already know about their attitudes and behaviours. The agent then emails you with a set of questions you need to ask your audience. It then does your secondary marketing research for you. All good market research starts with secondary market research. You are one of many companies competing in a market, which means there's bound to be some secondary data out there that will help you - articles in the trade press, academic papers, case studies on competitor websites, that kind of stuff. You'll see the agent uses a Perplexity node for this work, so when you wire it up, you will need a few dollars of Perplexity credits. Now, not only does it email you the useful data it finds on the open web, it also tells you what it didn't find and therefore what questions you need to ask the focus group. It then engineers the focus group respondents using a sophisticated method (I wont share it here because it would take too long, but if you do download the JSON file, you'll find it all in the nodes). The agent then conducts the focus group, shares their answers with you and produces a final report that answers your whether-to question. In the olden days, this would have taken months and cost you several thousand dollars. Today you're looking at half an hour of your time. You're welcome:) Lee52:T86a, And I've attached a PDF with screen grabs of the output if you'd like to see it in action. The purpose of this agent is to help you answer a whether-to question. We have these all the time in marketing. Whether to spend money on TV or outdoor, for example; where to start targeting a new audience segment; whatever it is. The best people to answer a question like this are your customers, of course. Or, the next best thing, synthetic versions of them. The first thing you do with this agent is have a conversation with in in Telegram to get to your whether-to question. In the PDF, you'll see one of our whether-to questions: should we sell our technology directly to cinema chain CMOs or approach their media agencies instead? In Telegram, you also establish the audience segment you want to talk to by sharing, for example, what you already know about their attitudes and behaviours. The agent then emails you with a set of questions you need to ask your audience. It then does your secondary marketing research for you. All good market research starts with secondary market research. You are one of many companies competing in a market, which means there's bound to be some secondary data out there that will help you - articles in the trade press, academic papers, case studies on competitor websites, that kind of stuff. You'll see the agent uses a Perplexity node for this work, so when you wire it up, you will need a few dollars of Perplexity credits. Now, not only does it email you the useful data it finds on the open web, it also tells you what it didn't find and therefore what questions you need to ask the focus group. It then engineers the focus group respondents using a sophisticated method (I wont share it here because it would take too long, but if you do download the JSON file, you'll find it all in the nodes). The agent then conducts the focus group, shares their answers with you and produces a final report that answers your whether-to question. In the olden days, this would have taken months and cost you several thousand dollars. Today you're looking at half an hour of your time. You're welcome:) Lee53:Ta54,Here's the n8n JSON file for the Social Media Agent we've built at my UK marketing technology startup Into-it with the marketing department at The London School of Economics. Here's a video demo of it (go to 19 minutes in) And I've attached a PDF with screen grabs of the output if you'd like to see it in action. The purpose of this agent is to help you answer a whether-to question. We have these all the time in marketing. Whether to spend money on TV or outdoor, for example; where to start targeting a new audience segment; whatever it is. The best people to answer a question like this are your customers, of course. Or, the next best thing, synthetic versions of them. The first thing you do with this agent is have a conversation with in in Telegram to get to your whether-to question. In the PDF, you'll see one of our whether-to questions: should we sell our technology directly to cinema chain CMOs or approach their media agencies instead? In Telegram, you also establish the audience segment you want to talk to by sharing, for example, what you already know about their attitudes and behaviours. The agent then emails you with a set of questions you need to ask your audience. It then does your secondary marketing research for you. All good market research starts with secondary market research. You are one of many companies competing in a market, which means there's bound to be some secondary data out there that will help you - articles in the trade press, academic papers, case studies on competitor websites, that kind of stuff. You'll see the agent uses a Perplexity node for this work, so when you wire it up, you will need a few dollars of Perplexity credits. Now, not only does it email you the useful data it finds on the open web, it also tells you what it didn't find and therefore what questions you need to ask the focus group. It then engineers the focus group respondents using a sophisticated method (I wont share it here because it would take too long, but if you do download the JSON file, you'll find it all in the nodes). The agent then conducts the focus group, shares their answers with you and produces a final report that answers your whether-to question. In the olden days, this would have taken months and cost you several thousand dollars. Today you're looking at half an hour of your time. You're welcome:) Lee54:T5c1,Here's a YouTube agent I created. It's about to enter the marketplace but there's a platform issue holding that up What it does: Each week, the agent pulls YouTube citation share and AI search demand for your topics, identifies where citation share is dropping while demand is rising, and produces a ready-to-use content brief for each gap: recommended format, title options, talking points, description, and tags. Where it fits: Research and content creation stages of GEO. The outcome is recovered citation share on YouTube, with clear priority on where you're losing ground fastest. How accuracy is maintained: • All citation share, search demand, and sub-topic data comes from live Profound and DataForSEO data. Nothing is estimated. • Before writing a brief, the agent checks your knowledge base for existing video content and brand guidelines, so it can flag likely duplicates and keep tone consistent. If the knowledge base has no relevant content yet, the brief notes that so nothing is assumed. • Every brief is routed for human review before it's shared. Inputs needed: Topic keywords (required) and brand name (optional, used to tailor tone and positioning). If you're looking for a quick and effective way to build out YouTube content...the right content...this could help I used the output this week to pitch 10 YouTube ideas to a potential client If you have something similar, or feedback on ways to improve it, can you tag me here? Cheers🍻55:T5cd,Here's a *YouTube* agent I created. It's about to enter the marketplace but there's a platform issue holding that up *What it does:* Each week, the agent pulls YouTube citation share and AI search demand for your topics, identifies where citation share is dropping while demand is rising, and produces a ready-to-use content brief for each gap: recommended format, title options, talking points, description, and tags. *Where it fits:* Research and content creation stages of GEO. The outcome is recovered citation share on YouTube, with clear priority on where you're losing ground fastest. *How accuracy is maintained:* • All citation share, search demand, and sub-topic data comes from live Profound and DataForSEO data. Nothing is estimated. • Before writing a brief, the agent checks your knowledge base for existing video content and brand guidelines, so it can flag likely duplicates and keep tone consistent. If the knowledge base has no relevant content yet, the brief notes that so nothing is assumed. • Every brief is routed for human review before it's shared. *Inputs needed:* Topic keywords (required) and brand name (optional, used to tailor tone and positioning). If you're looking for a quick and effective way to build out YouTube content...the right content...this could help I used the output this week to pitch 10 YouTube ideas to a potential client If you have something similar, or feedback on ways to improve it, can you tag me here? Cheers:beers:56:T1ad5,Fan-Out Site: Audit Purpose Measures how often ChatGPT's fan-out (web-search sub-query) decomposition explicitly targets a website using the site: operator, and how often that targeting lands on the tracked project's own domain versus other domains/brands. This surfaces whether ChatGPT is treating a site as a directly-searchable, authoritative source — and which brands it's doing that for most. Who is this for? GEO/AI-search team members auditing a tracked project's visibility inside ChatGPT's fan-out search behaviour. Default parameters Data period: trailing 3 months (90 days) ending today, unless the user specifies otherwise. Platform: ChatGPT only. Chart view: daily, unless the user asks for weekly or monthly instead (see Inputs). Domain: the project's own tracked domain, resolved automatically from the project profile (e.g. example.com for that project). Not asked per run. Inputs Ask for (one or two questions at a time, don't dump them all at once): Project/client — which project to run this against. Confirm the match if the name is ambiguous. Date range — confirm the default 3-month window, or take an explicit override if given. Chart view — ask whether the user wants the charts shown daily, weekly, or monthly. Default to daily if they don't have a preference. Platform and domain are fixed by the defaults above — don't ask about these unless the user wants to change them for a specific run. Instructions Step 1 — Resolve the project and its own domain Find/confirm the project. Pull the project profile to confirm its own tracked root domain — this is the domain used for the "your domain" chart (Step 7). If the profile doesn't expose a clean root domain, confirm it with the user once and use that for the rest of the run. Step 2 — Pull all ChatGPT fan-out queries for the period Pull fan-out/search queries for the project across the confirmed date window, filtered to ChatGPT's model channels/model IDs only (see Default parameters). Request at minimum: date, query text (plus chat/prompt IDs if useful for dedupe). Paginate through the full result set — do not truncate. A truncated pull will under-report site: counts, which are often small in absolute terms and easy to lose if you stop early. Step 3 — Flag site: queries For every query, flag it as a "site query" if its text contains the substring site: (case-insensitive), allowing for a following space (site: example.com) as well as no space (site:example.com). For each flagged query, extract the domain that follows site: (strip whitespace, protocol, and trailing punctuation) to identify which domain was targeted. Separately flag whether the extracted domain matches the project's own domain from Step 1, allowing for www./no-www and subdomain variants — but do NOT fuzzy-match genuinely different domains (e.g. example.co.uk and example.com must stay distinct). Step 4 — Check: does reddit.com (and specific subreddits) appear? From the flagged site: queries, check separately whether reddit.com appears as the targeted domain at all. Within those, check whether the query goes further and targets a specific subreddit path (e.g. site:reddit.com/r/london) rather than the bare domain. Report as a short callout: total site:reddit.com count, and a breakdown of which subreddits (if any) were specifically targeted, with counts. Step 5 — Check: do any site: commands target a folder/path? From the flagged site: queries, check whether the domain is followed by a folder or path (e.g. site:example.com/folder) rather than just the bare domain. Report as a short callout: count and % of site: queries that included a folder/path, plus the most common folders seen (if any pattern emerges). Step 6 — Chart 1: % of fan-out queries containing site:, over time Group all queries (flagged and unflagged) by the confirmed chart view (daily/weekly/monthly — see Inputs). For each period: (count of site: queries) ÷ (total fan-out queries) × 100. Render as a single-series line chart — x-axis = date, y-axis = % of queries. Step 7 — Chart 2: own-domain site: queries, over time From the site: queries only, filter to those matching the project's own domain. Group by the same period granularity as Step 6. Render as a line chart (use a bar chart instead if counts are mostly zero/very sparse — it reads more honestly than a flat line at zero). If the count is consistently very low or zero, say so plainly in the summary — that's a real and common finding, not a data gap. Step 8 — Top 10 brands table From all site: queries in the window, group by extracted domain and map each domain to a tracked brand/competitor where possible; label any unmapped domain by its raw domain string. Count occurrences per brand/domain, rank descending, take the top 10. Render as a table: Brand/Domain | Number of site: mentions | % of all site: queries in the window. Step 9 — Chart 3: top 10 brands, over time For the same top 10 brands/domains identified in Step 8, compute their site: query counts per period (same granularity as Steps 6–7). Render as a multi-series line chart, one line per brand (10 series max). Step 10 — Write a short summary Cover, in plain English: The overall % of fan-out queries using site: over the period, and whether that's trending up, down, or flat. How the project's own domain compares — a meaningful share of site: queries, negligible, or effectively absent. Which brand leads the top 10, and how the tracked brand's own line looks against it (including if the tracked brand doesn't make the top 10 at all). The reddit.com/subreddit finding from Step 4, and the folder/path finding from Step 5. Outputs Callout — reddit.com / subreddit-level site: targeting Callout — folder/path-level site: targeting Line chart — % of fan-out queries containing site: over time (ChatGPT only) Chart — own-domain site: query volume over time Table — top 10 brands/domains by site: mentions in the period Line chart — top 10 brands/domains' site: query volume over time Short written summary Data Source Fan-out / web-search sub-query data and brand/domain mapping from the project's tracked search-query and brand data (project profile, search query log, tracked brand list). Notes ChatGPT-only by design — this audits ChatGPT's specific fan-out behaviour, not a cross-engine comparison. If a future version needs Gemini/Perplexity, add an explicit platform parameter, but keep the default ChatGPT-only until asked otherwise. site: matching is a substring match on the raw query text — a fan-out query can combine site: with other terms and should still be flagged; it doesn't need to be the entire query. The top 10 count is fixed for both the table (Step 8) and the line chart (Step 9) — don't let these drift apart. Same process applies across all clients — no client-specific variants for now.57:T1b96,Just to add to above, here is a skill I've been running inside of Profound to get some similar data to that screenshot above, - it struggles with longer period analysis but ok for snapshots Fan-Out Site: Audit Purpose Measures how often ChatGPT's fan-out (web-search sub-query) decomposition explicitly targets a website using the site: operator, and how often that targeting lands on the tracked project's own domain versus other domains/brands. This surfaces whether ChatGPT is treating a site as a directly-searchable, authoritative source — and which brands it's doing that for most. Who is this for? GEO/AI-search team members auditing a tracked project's visibility inside ChatGPT's fan-out search behaviour. Default parameters Data period: trailing 3 months (90 days) ending today, unless the user specifies otherwise. Platform: ChatGPT only. Chart view: daily, unless the user asks for weekly or monthly instead (see Inputs). Domain: the project's own tracked domain, resolved automatically from the project profile (e.g. example.com for that project). Not asked per run. Inputs Ask for (one or two questions at a time, don't dump them all at once): Project/client — which project to run this against. Confirm the match if the name is ambiguous. Date range — confirm the default 3-month window, or take an explicit override if given. Chart view — ask whether the user wants the charts shown daily, weekly, or monthly. Default to daily if they don't have a preference. Platform and domain are fixed by the defaults above — don't ask about these unless the user wants to change them for a specific run. Instructions Step 1 — Resolve the project and its own domain Find/confirm the project. Pull the project profile to confirm its own tracked root domain — this is the domain used for the "your domain" chart (Step 7). If the profile doesn't expose a clean root domain, confirm it with the user once and use that for the rest of the run. Step 2 — Pull all ChatGPT fan-out queries for the period Pull fan-out/search queries for the project across the confirmed date window, filtered to ChatGPT's model channels/model IDs only (see Default parameters). Request at minimum: date, query text (plus chat/prompt IDs if useful for dedupe). Paginate through the full result set — do not truncate. A truncated pull will under-report site: counts, which are often small in absolute terms and easy to lose if you stop early. Step 3 — Flag site: queries For every query, flag it as a "site query" if its text contains the substring site: (case-insensitive), allowing for a following space (site: example.com) as well as no space (site:example.com). For each flagged query, extract the domain that follows site: (strip whitespace, protocol, and trailing punctuation) to identify which domain was targeted. Separately flag whether the extracted domain matches the project's own domain from Step 1, allowing for www./no-www and subdomain variants — but do NOT fuzzy-match genuinely different domains (e.g. example.co.uk and example.com must stay distinct). Step 4 — Check: does reddit.com (and specific subreddits) appear? From the flagged site: queries, check separately whether reddit.com appears as the targeted domain at all. Within those, check whether the query goes further and targets a specific subreddit path (e.g. site:reddit.com/r/london) rather than the bare domain. Report as a short callout: total site:reddit.com count, and a breakdown of which subreddits (if any) were specifically targeted, with counts. Step 5 — Check: do any site: commands target a folder/path? From the flagged site: queries, check whether the domain is followed by a folder or path (e.g. site:example.com/folder) rather than just the bare domain. Report as a short callout: count and % of site: queries that included a folder/path, plus the most common folders seen (if any pattern emerges). Step 6 — Chart 1: % of fan-out queries containing site:, over time Group all queries (flagged and unflagged) by the confirmed chart view (daily/weekly/monthly — see Inputs). For each period: (count of site: queries) ÷ (total fan-out queries) × 100. Render as a single-series line chart — x-axis = date, y-axis = % of queries. Step 7 — Chart 2: own-domain site: queries, over time From the site: queries only, filter to those matching the project's own domain. Group by the same period granularity as Step 6. Render as a line chart (use a bar chart instead if counts are mostly zero/very sparse — it reads more honestly than a flat line at zero). If the count is consistently very low or zero, say so plainly in the summary — that's a real and common finding, not a data gap. Step 8 — Top 10 brands table From all site: queries in the window, group by extracted domain and map each domain to a tracked brand/competitor where possible; label any unmapped domain by its raw domain string. Count occurrences per brand/domain, rank descending, take the top 10. Render as a table: Brand/Domain | Number of site: mentions | % of all site: queries in the window. Step 9 — Chart 3: top 10 brands, over time For the same top 10 brands/domains identified in Step 8, compute their site: query counts per period (same granularity as Steps 6–7). Render as a multi-series line chart, one line per brand (10 series max). Step 10 — Write a short summary Cover, in plain English: The overall % of fan-out queries using site: over the period, and whether that's trending up, down, or flat. How the project's own domain compares — a meaningful share of site: queries, negligible, or effectively absent. Which brand leads the top 10, and how the tracked brand's own line looks against it (including if the tracked brand doesn't make the top 10 at all). The reddit.com/subreddit finding from Step 4, and the folder/path finding from Step 5. Outputs Callout — reddit.com / subreddit-level site: targeting Callout — folder/path-level site: targeting Line chart — % of fan-out queries containing site: over time (ChatGPT only) Chart — own-domain site: query volume over time Table — top 10 brands/domains by site: mentions in the period Line chart — top 10 brands/domains' site: query volume over time Short written summary Data Source Fan-out / web-search sub-query data and brand/domain mapping from the project's tracked search-query and brand data (project profile, search query log, tracked brand list). Notes ChatGPT-only by design — this audits ChatGPT's specific fan-out behaviour, not a cross-engine comparison. If a future version needs Gemini/Perplexity, add an explicit platform parameter, but keep the default ChatGPT-only until asked otherwise. site: matching is a substring match on the raw query text — a fan-out query can combine site: with other terms and should still be flagged; it doesn't need to be the entire query. The top 10 count is fixed for both the table (Step 8) and the line chart (Step 9) — don't let these drift apart. Same process applies across all clients — no client-specific variants for now. 58:T1ad5,Fan-Out Site: Audit Purpose Measures how often ChatGPT's fan-out (web-search sub-query) decomposition explicitly targets a website using the site: operator, and how often that targeting lands on the tracked project's own domain versus other domains/brands. This surfaces whether ChatGPT is treating a site as a directly-searchable, authoritative source — and which brands it's doing that for most. Who is this for? GEO/AI-search team members auditing a tracked project's visibility inside ChatGPT's fan-out search behaviour. Default parameters Data period: trailing 3 months (90 days) ending today, unless the user specifies otherwise. Platform: ChatGPT only. Chart view: daily, unless the user asks for weekly or monthly instead (see Inputs). Domain: the project's own tracked domain, resolved automatically from the project profile (e.g. example.com for that project). Not asked per run. Inputs Ask for (one or two questions at a time, don't dump them all at once): Project/client — which project to run this against. Confirm the match if the name is ambiguous. Date range — confirm the default 3-month window, or take an explicit override if given. Chart view — ask whether the user wants the charts shown daily, weekly, or monthly. Default to daily if they don't have a preference. Platform and domain are fixed by the defaults above — don't ask about these unless the user wants to change them for a specific run. Instructions Step 1 — Resolve the project and its own domain Find/confirm the project. Pull the project profile to confirm its own tracked root domain — this is the domain used for the "your domain" chart (Step 7). If the profile doesn't expose a clean root domain, confirm it with the user once and use that for the rest of the run. Step 2 — Pull all ChatGPT fan-out queries for the period Pull fan-out/search queries for the project across the confirmed date window, filtered to ChatGPT's model channels/model IDs only (see Default parameters). Request at minimum: date, query text (plus chat/prompt IDs if useful for dedupe). Paginate through the full result set — do not truncate. A truncated pull will under-report site: counts, which are often small in absolute terms and easy to lose if you stop early. Step 3 — Flag site: queries For every query, flag it as a "site query" if its text contains the substring site: (case-insensitive), allowing for a following space (site: example.com) as well as no space (site:example.com). For each flagged query, extract the domain that follows site: (strip whitespace, protocol, and trailing punctuation) to identify which domain was targeted. Separately flag whether the extracted domain matches the project's own domain from Step 1, allowing for www./no-www and subdomain variants — but do NOT fuzzy-match genuinely different domains (e.g. example.co.uk and example.com must stay distinct). Step 4 — Check: does reddit.com (and specific subreddits) appear? From the flagged site: queries, check separately whether reddit.com appears as the targeted domain at all. Within those, check whether the query goes further and targets a specific subreddit path (e.g. site:reddit.com/r/london) rather than the bare domain. Report as a short callout: total site:reddit.com count, and a breakdown of which subreddits (if any) were specifically targeted, with counts. Step 5 — Check: do any site: commands target a folder/path? From the flagged site: queries, check whether the domain is followed by a folder or path (e.g. site:example.com/folder) rather than just the bare domain. Report as a short callout: count and % of site: queries that included a folder/path, plus the most common folders seen (if any pattern emerges). Step 6 — Chart 1: % of fan-out queries containing site:, over time Group all queries (flagged and unflagged) by the confirmed chart view (daily/weekly/monthly — see Inputs). For each period: (count of site: queries) ÷ (total fan-out queries) × 100. Render as a single-series line chart — x-axis = date, y-axis = % of queries. Step 7 — Chart 2: own-domain site: queries, over time From the site: queries only, filter to those matching the project's own domain. Group by the same period granularity as Step 6. Render as a line chart (use a bar chart instead if counts are mostly zero/very sparse — it reads more honestly than a flat line at zero). If the count is consistently very low or zero, say so plainly in the summary — that's a real and common finding, not a data gap. Step 8 — Top 10 brands table From all site: queries in the window, group by extracted domain and map each domain to a tracked brand/competitor where possible; label any unmapped domain by its raw domain string. Count occurrences per brand/domain, rank descending, take the top 10. Render as a table: Brand/Domain | Number of site: mentions | % of all site: queries in the window. Step 9 — Chart 3: top 10 brands, over time For the same top 10 brands/domains identified in Step 8, compute their site: query counts per period (same granularity as Steps 6–7). Render as a multi-series line chart, one line per brand (10 series max). Step 10 — Write a short summary Cover, in plain English: The overall % of fan-out queries using site: over the period, and whether that's trending up, down, or flat. How the project's own domain compares — a meaningful share of site: queries, negligible, or effectively absent. Which brand leads the top 10, and how the tracked brand's own line looks against it (including if the tracked brand doesn't make the top 10 at all). The reddit.com/subreddit finding from Step 4, and the folder/path finding from Step 5. Outputs Callout — reddit.com / subreddit-level site: targeting Callout — folder/path-level site: targeting Line chart — % of fan-out queries containing site: over time (ChatGPT only) Chart — own-domain site: query volume over time Table — top 10 brands/domains by site: mentions in the period Line chart — top 10 brands/domains' site: query volume over time Short written summary Data Source Fan-out / web-search sub-query data and brand/domain mapping from the project's tracked search-query and brand data (project profile, search query log, tracked brand list). Notes ChatGPT-only by design — this audits ChatGPT's specific fan-out behaviour, not a cross-engine comparison. If a future version needs Gemini/Perplexity, add an explicit platform parameter, but keep the default ChatGPT-only until asked otherwise. site: matching is a substring match on the raw query text — a fan-out query can combine site: with other terms and should still be flagged; it doesn't need to be the entire query. The top 10 count is fixed for both the table (Step 8) and the line chart (Step 9) — don't let these drift apart. Same process applies across all clients — no client-specific variants for now.59:T1cad,Just to add to above, here is a skill I've been running inside of Profound to get some similar data to that screenshot above, - it struggles with longer period analysis but ok for snapshots ```Fan-Out Site: Audit Purpose Measures how often ChatGPT's fan-out (web-search sub-query) decomposition explicitly targets a website using the site: operator, and how often that targeting lands on the tracked project's own domain versus other domains/brands. This surfaces whether ChatGPT is treating a site as a directly-searchable, authoritative source — and which brands it's doing that for most. Who is this for? GEO/AI-search team members auditing a tracked project's visibility inside ChatGPT's fan-out search behaviour. Default parameters Data period: trailing 3 months (90 days) ending today, unless the user specifies otherwise. Platform: ChatGPT only. Chart view: daily, unless the user asks for weekly or monthly instead (see Inputs). Domain: the project's own tracked domain, resolved automatically from the project profile (e.g. for that project). Not asked per run. Inputs Ask for (one or two questions at a time, don't dump them all at once): Project/client — which project to run this against. Confirm the match if the name is ambiguous. Date range — confirm the default 3-month window, or take an explicit override if given. Chart view — ask whether the user wants the charts shown daily, weekly, or monthly. Default to daily if they don't have a preference. Platform and domain are fixed by the defaults above — don't ask about these unless the user wants to change them for a specific run. Instructions Step 1 — Resolve the project and its own domain Find/confirm the project. Pull the project profile to confirm its own tracked root domain — this is the domain used for the "your domain" chart (Step 7). If the profile doesn't expose a clean root domain, confirm it with the user once and use that for the rest of the run. Step 2 — Pull all ChatGPT fan-out queries for the period Pull fan-out/search queries for the project across the confirmed date window, filtered to ChatGPT's model channels/model IDs only (see Default parameters). Request at minimum: date, query text (plus chat/prompt IDs if useful for dedupe). Paginate through the full result set — do not truncate. A truncated pull will under-report site: counts, which are often small in absolute terms and easy to lose if you stop early. Step 3 — Flag site: queries For every query, flag it as a "site query" if its text contains the substring site: (case-insensitive), allowing for a following space (site: ) as well as no space (site:). For each flagged query, extract the domain that follows site: (strip whitespace, protocol, and trailing punctuation) to identify which domain was targeted. Separately flag whether the extracted domain matches the project's own domain from Step 1, allowing for www./no-www and subdomain variants — but do NOT fuzzy-match genuinely different domains (e.g. and must stay distinct). Step 4 — Check: does (and specific subreddits) appear? From the flagged site: queries, check separately whether appears as the targeted domain at all. Within those, check whether the query goes further and targets a specific subreddit path (e.g. site:) rather than the bare domain. Report as a short callout: total site: count, and a breakdown of which subreddits (if any) were specifically targeted, with counts. Step 5 — Check: do any site: commands target a folder/path? From the flagged site: queries, check whether the domain is followed by a folder or path (e.g. site:) rather than just the bare domain. Report as a short callout: count and % of site: queries that included a folder/path, plus the most common folders seen (if any pattern emerges). Step 6 — Chart 1: % of fan-out queries containing site:, over time Group all queries (flagged and unflagged) by the confirmed chart view (daily/weekly/monthly — see Inputs). For each period: (count of site: queries) ÷ (total fan-out queries) × 100. Render as a single-series line chart — x-axis = date, y-axis = % of queries. Step 7 — Chart 2: own-domain site: queries, over time From the site: queries only, filter to those matching the project's own domain. Group by the same period granularity as Step 6. Render as a line chart (use a bar chart instead if counts are mostly zero/very sparse — it reads more honestly than a flat line at zero). If the count is consistently very low or zero, say so plainly in the summary — that's a real and common finding, not a data gap. Step 8 — Top 10 brands table From all site: queries in the window, group by extracted domain and map each domain to a tracked brand/competitor where possible; label any unmapped domain by its raw domain string. Count occurrences per brand/domain, rank descending, take the top 10. Render as a table: Brand/Domain | Number of site: mentions | % of all site: queries in the window. Step 9 — Chart 3: top 10 brands, over time For the same top 10 brands/domains identified in Step 8, compute their site: query counts per period (same granularity as Steps 6–7). Render as a multi-series line chart, one line per brand (10 series max). Step 10 — Write a short summary Cover, in plain English: The overall % of fan-out queries using site: over the period, and whether that's trending up, down, or flat. How the project's own domain compares — a meaningful share of site: queries, negligible, or effectively absent. Which brand leads the top 10, and how the tracked brand's own line looks against it (including if the tracked brand doesn't make the top 10 at all). The finding from Step 4, and the folder/path finding from Step 5. Outputs Callout — / subreddit-level site: targeting Callout — folder/path-level site: targeting Line chart — % of fan-out queries containing site: over time (ChatGPT only) Chart — own-domain site: query volume over time Table — top 10 brands/domains by site: mentions in the period Line chart — top 10 brands/domains' site: query volume over time Short written summary Data Source Fan-out / web-search sub-query data and brand/domain mapping from the project's tracked search-query and brand data (project profile, search query log, tracked brand list). Notes ChatGPT-only by design — this audits ChatGPT's specific fan-out behaviour, not a cross-engine comparison. If a future version needs Gemini/Perplexity, add an explicit platform parameter, but keep the default ChatGPT-only until asked otherwise. site: matching is a substring match on the raw query text — a fan-out query can combine site: with other terms and should still be flagged; it doesn't need to be the entire query. The top 10 count is fixed for both the table (Step 8) and the line chart (Step 9) — don't let these drift apart. Same process applies across all clients — no client-specific variants for now.```5a:T567,Recently my company acquired a data provider, of sorts. The data they provide is complimentary to the larger product we sell to private/non-bank lenders. It's things like loan origination information, delinquency rates, and borrower intelligence nationwide, by zip code. We want to use this data to write a quarterly news letter which would be high in EEAT signals and loaded original stats/insights. The agent I have working right now is behemoth, consuming about 3x the credits I want it to, so working on that part. But it works like this: Access the data via 8 unique API calls Turn raw payloads into one verified metrics brief Condense that into a search targeting note Deep research pass Parallel web search scoped to the reporting window across rates and funding costs, capital markets and securitizations, regulatory, and originator news. Editorial angle memo Cross-references the proprietary data against the news to decide what the story is. Fact check Human review gate Then it fans the approved report out into the full publication package • Newsletter issue, finished and pasteable into HubSpot • Notable market callouts for the metros that actually earned a mention • Schema.org (http://Schema.org) JSON-LD • Copy for LinkedIn I can clear out the API keys and share it if anyone is interested in the file but here's the screenshot (no Banana for scale)5b:T576,Recently my company acquired a data provider, of sorts. The data they provide is complimentary to the larger product we sell to private/non-bank lenders. It's things like loan origination information, delinquency rates, and borrower intelligence nationwide, by zip code. We want to use this data to write a quarterly news letter which would be high in EEAT signals and loaded original stats/insights. The agent I have working right now is behemoth, consuming about 3x the credits I want it to, so working on that part. But it works like this: *Access the data via 8 unique API calls* *Turn raw payloads into one verified metrics brief* *Condense that into a search targeting note* *Deep research pass* Parallel web search scoped to the reporting window across rates and funding costs, capital markets and securitizations, regulatory, and originator news. *Editorial angle memo* Cross-references the proprietary data against the news to decide what the story is. *Fact check* *Human review gate* *Then it fans the approved report out into the full publication package* • Newsletter issue, finished and pasteable into HubSpot • Notable market callouts for the metros that actually earned a mention • JSON-LD • Copy for LinkedIn I can clear out the API keys and share it if anyone is interested in the file but here's the screenshot (no Banana for scale)33:["$","$L50",null,{"tenantId":"fd66edac-5453-44bb-82c3-35d19c186d24","feedIdOrName":"f4e3fb84-5d2a-4634-9594-7936ace0f480","siteLogoUrl":"https://cdn.tightknit.ai/fd66edac-5453-44bb-82c3-35d19c186d24/d4eac052-457c-4653-9a58-a796a057cfce/site-combination-logo/file_SSEMTgHUGyRs/Logo-Community-Dark-Theme-md.png","defaultFeedPostUiFormat":"card","defaultSortMethod":"newest","defaultPageSize":15,"fallbackData":{"page":0,"pageSize":15,"records":[{"id":"jq4a0qxypo1v","message_id":"msg_oCgSNXxbHQAY","created_at":"2026-09-11T23:03:05.176357+00:00","content_created_at":"2026-09-11T23:03:02.029+00:00","latest_activity_at":"2026-09-11T23:03:02.029+00:00","title":"Building a Biweekly Baseline Report Agent for Profound to Track Key Metrics and Competitor Spikes","slug":"building-a-biweekly-baseline-report-agent-for-prof","description":"Introducing a biweekly Profound agent generating baseline reports with key metrics, topic visibility, and competitor alerts to improve client repor...","feed_id":"f4e3fb84-5d2a-4634-9594-7936ace0f480","post_detail_display_format":"discussion","author_profile_id":"b09c9e72-02b4-4c2f-8337-6a85dde4be48","body_prosemirror":{"type":"doc","content":[{"type":"paragraph","content":[{"text":"Happy Friday ","type":"text"},{"text":"🍻","type":"text"},{"type":"hardBreak"},{"text":"Spent a good bit of time this week helping one of our enterprise clients build out their Profound space. ","type":"text"}]},{"type":"paragraph","content":[{"text":"Creating a good, accurate baseline report is the top priority, and it's a real pain point for a lot of clients we talk to.","type":"text"}]},{"type":"paragraph","content":[{"text":"We solved it by building an agent that runs every two weeks and generates a report directly in Profound","type":"text"},{"text":", with a baseline date of Sept 9 used.","type":"text"}]},{"type":"paragraph","content":[{"text":"It covers the essentials: key metrics, visibility across topics and prompts (branded and non-branded separated) and competitive comparison. Also has a spike alert, so we catch when a competitor gains 2 percentage points. ","type":"text"}]},{"type":"paragraph","content":[{"text":"The goal is one report that all weekly, monthly, and quarterly reports point to. This is v1. I'll share updates as we refine it over the next few weeks. ","type":"text"}]},{"type":"paragraph","content":[{"text":"Sharing here in case anyone wants to use it too.","type":"text"},{"text":" If anyone has any good benchmark reporting agents or tip","type":"text"},{"text":"…","type":"text"},{"text":" please share ","type":"text"},{"text":"🤝","type":"text"}]}]},"body_plaintext":"Happy Friday 🍻\nSpent a good bit of time this week helping one of our enterprise clients build out their Profound space. \n\nCreating a good, accurate baseline report is the top priority, and it's a real pain point for a lot of clients we talk to.\n\nWe solved it by building an agent that runs every two weeks and generates a report directly in Profound, with a baseline date of Sept 9 used.\n\nIt covers the essentials: key metrics, visibility across topics and prompts (branded and non-branded separated) and competitive comparison. Also has a spike alert, so we catch when a competitor gains 2 percentage points. \n\nThe goal is one report that all weekly, monthly, and quarterly reports point to. This is v1. I'll share updates as we refine it over the next few weeks. \n\nSharing here in case anyone wants to use it too. If anyone has any good benchmark reporting agents or tip… please share 🤝\n","source":"slack","edited_at":null,"version":0,"is_published":true,"file_attachments_count":1,"reactions_count":8,"comments_count":0,"slack_message":{"id":"8a6c563b-5bf6-4af4-96d8-1914295231c7","message_ts":"1789167782.029209","blocks":[{"type":"rich_text","block_id":"S9nxD","elements":[{"type":"rich_text_section","elements":[{"text":"Happy Friday ","type":"text"},{"name":"beers","type":"emoji","unicode":"1f37b"},{"text":"\nSpent a good bit of time this week helping one of our enterprise clients build out their Profound space. \n\nCreating a good, accurate baseline report is the top priority, and it's a real pain point for a lot of clients we talk to.\n\nWe solved it by building an agent that runs every two weeks and generates a report directly in Profound","type":"text"},{"text":", with a baseline date of Sept 9 used.\n\n","type":"text"},{"text":"It covers the essentials: key metrics, visibility across topics and prompts (branded and non-branded separated) and competitive comparison. Also has a spike alert, so we catch when a competitor gains 2 percentage points. \n\nThe goal is one report that all weekly, monthly, and quarterly reports point to. This is v1. I'll share updates as we refine it over the next few weeks. ","type":"text"},{"text":"\n\n","type":"text"},{"text":"Sharing here in case anyone wants to use it too.","type":"text"},{"text":" If anyone has any good benchmark reporting agents or tip","type":"text"},{"text":"…","type":"text"},{"text":" please share ","type":"text"},{"name":"handshake","type":"emoji","unicode":"1f91d"},{"text":"\n","type":"text"}]}]}],"markdown":"Happy Friday :beers:\nSpent a good bit of time this week helping one of our enterprise clients build out their Profound space. \n\nCreating a good, accurate baseline report is the top priority, and it's a real pain point for a lot of clients we talk to.\n\nWe solved it by building an agent that runs every two weeks and generates a report directly in Profound, with a baseline date of Sept 9 used.\n\nIt covers the essentials: key metrics, visibility across topics and prompts (branded and non-branded separated) and competitive comparison. Also has a spike alert, so we catch when a competitor gains 2 percentage points. \n\nThe goal is one report that all weekly, monthly, and quarterly reports point to. This is v1. I'll share updates as we refine it over the next few weeks. \n\nSharing here in case anyone wants to use it too. If anyone has any good benchmark reporting agents or tip… please share :handshake:\n","permalink":"https://tryprofound-community.slack.com/archives/C0BH60Q4X1S/p1789167782029209","parent_slack_message_id":null,"username":null,"icon":null,"is_shared":false},"cover_image_file_id":null,"cover_image_url":null,"author":{"id":"b09c9e72-02b4-4c2f-8337-6a85dde4be48","preferred_name":"David","preferred_image_original":"https://avatars.slack-edge.com/2026-08-18/11848475116614_ac30854e357cf6dcd811_original.png","preferred_image_1024":"https://avatars.slack-edge.com/2026-08-18/11848475116614_ac30854e357cf6dcd811_1024.png","preferred_image_512":"https://avatars.slack-edge.com/2026-08-18/11848475116614_ac30854e357cf6dcd811_512.png","preferred_image_192":"https://avatars.slack-edge.com/2026-08-18/11848475116614_ac30854e357cf6dcd811_192.png","preferred_image_72":"https://avatars.slack-edge.com/2026-08-18/11848475116614_ac30854e357cf6dcd811_72.png","preferred_image_48":"https://avatars.slack-edge.com/2026-08-18/11848475116614_ac30854e357cf6dcd811_48.png","preferred_image_32":"https://avatars.slack-edge.com/2026-08-18/11848475116614_ac30854e357cf6dcd811_32.png","preferred_image_24":"https://avatars.slack-edge.com/2026-08-18/11848475116614_ac30854e357cf6dcd811_24.png","slack_profile_id":"U0AJFQ2HL7J","is_team_member":false,"slack_profile":{"id":"U0AJFQ2HL7J","is_admin":false,"is_bot":false,"is_forgotten":false,"deleted":false},"group_flair":null},"best_answer_message_id":null,"selected_answer_message_ids":[],"selected_answer_count":0,"feed":{"id":"f4e3fb84-5d2a-4634-9594-7936ace0f480","public_id":"feed_TGBUr5saga4y","label":"Showcase","slug":"showcase","is_posting_from_site_allowed":true,"allow_selected_answer":false,"visibility":"public"},"references":{"mentioned_slack_users":[],"mentioned_slack_channels":[],"custom_emojis":[],"urls":[]}},{"id":"8mhidyhwbqta","message_id":"msg_9Sa3X7eoE6Mt","created_at":"2026-09-10T18:02:34.745555+00:00","content_created_at":"2026-09-10T18:02:31.057+00:00","latest_activity_at":"2026-09-11T14:22:11.303+00:00","title":"Introducing LMFAO Resources: Daily AI Insights and Weekly Summaries from LinkedIn Posts","slug":"introducing-lmfao-resources-daily-ai-insights-and","description":"Discover a daily AI insights tracker summarizing top LinkedIn posts and weekly local marketing recommendations.","feed_id":"f4e3fb84-5d2a-4634-9594-7936ace0f480","post_detail_display_format":"discussion","author_profile_id":"c70976b8-5c73-4e5d-bb14-c652073689c4","body_prosemirror":{"type":"doc","content":[{"type":"paragraph","content":[{"text":"Ok this is perhaps the simplest thing you will ever see in this channel. I call it my LMFAO Resources (Local Marketing for AI Overviews). It’s not really “local”. Every day my lil friend Claude checks to see any LinkedIN posts on AI that I’ve saved and adds a summary to the GSheet. It’s my way of keeping track of all the smart things people are saying. Every week, it summarizes the top recommendations.","type":"text"}]},{"type":"paragraph","content":[{"text":"It’s been running for about two months and has about 50 subscribers so far. Have fun!","type":"text"},{"type":"hardBreak"},{"text":"docs.google.com/spreadsheets/d/13Hh5kS0wPbE…/edit?gid=0#gid=0","type":"text","marks":[{"type":"link","attrs":{"href":"https://docs.google.com/spreadsheets/d/13Hh5kS0wPbEo0asB2keGUEmfnyMFJkSjL8OWlq056l8/edit?gid=0#gid=0"}}]}]}]},"body_plaintext":"Ok this is perhaps the simplest thing you will ever see in this channel. I call it my LMFAO Resources (Local Marketing for AI Overviews). It’s not really “local”. Every day my lil friend Claude checks to see any LinkedIN posts on AI that I’ve saved and adds a summary to the GSheet. It’s my way of keeping track of all the smart things people are saying. Every week, it summarizes the top recommendations.\n\nIt’s been running for about two months and has about 50 subscribers so far. Have fun!\ndocs.google.com/spreadsheets/d/13Hh5kS0wPbE…/edit?gid=0#gid=0 (https://docs.google.com/spreadsheets/d/13Hh5kS0wPbEo0asB2keGUEmfnyMFJkSjL8OWlq056l8/edit?gid=0#gid=0)","source":"slack","edited_at":null,"version":0,"is_published":true,"file_attachments_count":0,"reactions_count":3,"comments_count":5,"slack_message":{"id":"2a794f54-4471-423d-8c98-440df29caf61","message_ts":"1789063351.057729","blocks":[{"type":"rich_text","block_id":"7MpCA","elements":[{"type":"rich_text_section","elements":[{"text":"Ok this is perhaps the simplest thing you will ever see in this channel. I call it my LMFAO Resources (Local Marketing for AI Overviews). It’s not really “local”. Every day my lil friend Claude checks to see any LinkedIN posts on AI that I’ve saved and adds a summary to the GSheet. It’s my way of keeping track of all the smart things people are saying. Every week, it summarizes the top recommendations.\n\nIt’s been running for about two months and has about 50 subscribers so far. Have fun!\n","type":"text"},{"url":"https://docs.google.com/spreadsheets/d/13Hh5kS0wPbEo0asB2keGUEmfnyMFJkSjL8OWlq056l8/edit?gid=0#gid=0","text":"docs.google.com/spreadsheets/d/13Hh5kS0wPbE…/edit?gid=0#gid=0","type":"link","truncated":true}]}]}],"markdown":"Ok this is perhaps the simplest thing you will ever see in this channel. I call it my LMFAO Resources (Local Marketing for AI Overviews). It’s not really “local”. Every day my lil friend Claude checks to see any LinkedIN posts on AI that I’ve saved and adds a summary to the GSheet. It’s my way of keeping track of all the smart things people are saying. Every week, it summarizes the top recommendations.\n\nIt’s been running for about two months and has about 50 subscribers so far. Have fun!\n","permalink":"https://tryprofound-community.slack.com/archives/C0BH60Q4X1S/p1789063351057729","parent_slack_message_id":null,"username":null,"icon":null,"is_shared":false},"cover_image_file_id":null,"cover_image_url":null,"author":{"id":"c70976b8-5c73-4e5d-bb14-c652073689c4","preferred_name":"Andrew 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the Social Media Agent: AI-Powered Marketing Research to Quickly Answer Whether-To Questions","slug":"introducing-the-social-media-agent-ai-powered-mark","description":"Discover how the Social Media Agent uses AI and secondary research to quickly answer your marketing whether-to questions.","feed_id":"f4e3fb84-5d2a-4634-9594-7936ace0f480","post_detail_display_format":"discussion","author_profile_id":"bf3de231-c268-4edd-aca7-164c70255395","body_prosemirror":{"type":"doc","content":[{"type":"paragraph","content":[{"text":"Here's the n8n JSON file for the Social Media Agent we've built at my UK marketing technology startup Into-it with the marketing department at The London School of Economics.","type":"text"}]},{"type":"paragraph","content":[{"text":"marketingweek.com/provocative-ai-marketing-team","type":"text","marks":[{"type":"link","attrs":{"href":"https://www.marketingweek.com/provocative-ai-marketing-team/"}}]}]},{"type":"paragraph","content":[{"text":"Here's a video demo of it (go to 19 minutes in)","type":"text"}]},{"type":"paragraph","content":[{"text":"maven.com/p/ffacd9/what-is-a-marketing-engineer-and-how-do-i-become-one","type":"text","marks":[{"type":"link","attrs":{"href":"https://maven.com/p/ffacd9/what-is-a-marketing-engineer-and-how-do-i-become-one"}}]}]},{"type":"paragraph","content":[{"text":"And I've attached a PDF with screen grabs of the output if you'd like to see it in action.","type":"text"}]},{"type":"paragraph","content":[{"text":"The purpose of this agent is to help you answer a whether-to question.","type":"text"}]},{"type":"paragraph","content":[{"text":"We have these all the time in marketing.","type":"text"}]},{"type":"paragraph","content":[{"text":"Whether to spend money on TV or outdoor, for example; where to start targeting a new audience segment; whatever it is.","type":"text"}]},{"type":"paragraph","content":[{"text":"The best people to answer a question like this are your customers, of course.","type":"text"}]},{"type":"paragraph","content":[{"text":"Or, the next best thing, synthetic versions of them.","type":"text"}]},{"type":"paragraph","content":[{"text":"The first thing you do with this agent is have a conversation with in in Telegram to get to your whether-to question.","type":"text"}]},{"type":"paragraph","content":[{"text":"In the PDF, you'll see one of our whether-to questions: should we sell our technology directly to cinema chain CMOs or approach their media agencies instead?","type":"text"}]},{"type":"paragraph","content":[{"text":"In Telegram, you also establish the audience segment you want to talk to by sharing, for example, what you already know about their attitudes and behaviours.","type":"text"}]},{"type":"paragraph","content":[{"text":"The agent then emails you with a set of questions you need to ask your audience.","type":"text"}]},{"type":"paragraph","content":[{"text":"It then does your secondary marketing research for you.","type":"text"}]},{"type":"paragraph","content":[{"text":"All good market research starts with secondary market research.","type":"text"}]},{"type":"paragraph","content":[{"text":"You are one of many companies competing in a market, which means there's bound to be some secondary data out there that will help you - articles in the trade press, academic papers, case studies on competitor websites, that kind of stuff.","type":"text"}]},{"type":"paragraph","content":[{"text":"You'll see the agent uses a Perplexity node for this work, so when you wire it up, you will need a few dollars of Perplexity credits.","type":"text"}]},{"type":"paragraph","content":[{"text":"Now, not only does it email you the useful data it finds on the open web, it also tells you what it didn't find and therefore what questions you need to ask the focus group.","type":"text"}]},{"type":"paragraph","content":[{"text":"It then engineers the focus group respondents using a sophisticated method (I wont share it here because it would take too long, but if you do download the JSON file, you'll find it all in the nodes).","type":"text"}]},{"type":"paragraph","content":[{"text":"The agent then conducts the focus group, shares their answers with you and produces a final report that answers your whether-to question.","type":"text"}]},{"type":"paragraph","content":[{"text":"In the olden days, this would have taken months and cost you several thousand dollars.","type":"text"}]},{"type":"paragraph","content":[{"text":"Today you're looking at half an hour of your time.","type":"text"}]},{"type":"paragraph","content":[{"text":"You're welcome:)","type":"text"}]},{"type":"paragraph","content":[{"text":"Lee","type":"text"}]}]},"body_plaintext":"$51","source":"slack","edited_at":null,"version":0,"is_published":true,"file_attachments_count":2,"reactions_count":6,"comments_count":2,"slack_message":{"id":"66b45277-9234-435d-aab8-0b848836da86","message_ts":"1788981559.252669","blocks":[{"type":"rich_text","block_id":"l3CrA","elements":[{"type":"rich_text_section","elements":[{"text":"Here's the n8n JSON file for the Social Media Agent we've built at my UK marketing technology startup Into-it with the marketing department at The London School of Economics.\n\n","type":"text"},{"url":"https://www.marketingweek.com/provocative-ai-marketing-team/","text":"marketingweek.com/provocative-ai-marketing-team","type":"link","unsafe":true,"truncated":true},{"text":"\n\nHere's a video demo of it (go to 19 minutes 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My goal was to rank higher as a source for things we've written about- for example, if someone looks up ‘Richard Nadler’, I want our interview cited by agents first.","type":"text"}]},{"type":"paragraph","content":[{"text":"It uses LLM-as-judge to gauge performance and review article drafts to improve AEO before publishing. Started with Profound to see our overall visibility as a publisher, then got curious how agents cited our interviews against other publications. Happy to share the link if anyone wants to play around.","type":"text"}]},{"type":"paragraph","content":[{"text":"Also, excited to meet some of you at the meetup on Thursday! Happy building. ","type":"text"},{"text":"✨","type":"text"}]}]},"body_plaintext":"This weekend, I decided to build an AEO tool that helps with citation as a publisher. My goal was to rank higher as a source for things we've written about- for example, if someone looks up ‘Richard Nadler’, I want our interview cited by agents first.\n\nIt uses LLM-as-judge to gauge performance and review article drafts to improve AEO before publishing. Started with Profound to see our overall visibility as a publisher, then got curious how agents cited our interviews against other publications. Happy to share the link if anyone wants to play around.\n\nAlso, excited to meet some of you at the meetup on Thursday! Happy building. ✨","source":"slack","edited_at":null,"version":0,"is_published":true,"file_attachments_count":3,"reactions_count":12,"comments_count":2,"slack_message":{"id":"5df36237-4bed-448d-9596-c95e8a7bdd42","message_ts":"1788881963.221999","blocks":[{"type":"rich_text","block_id":"3bCMb","elements":[{"type":"rich_text_section","elements":[{"text":"This weekend, I decided to build an AEO tool that helps with citation as a publisher. My goal was to rank higher as a source for things we've written about- for example, if someone looks up ‘Richard Nadler’, I want our interview cited by agents first.\n\nIt uses LLM-as-judge to gauge performance and review article drafts to improve AEO before publishing. Started with Profound to see our overall visibility as a publisher, then got curious how agents cited our interviews against other publications. Happy to share the link if anyone wants to play around.\n\nAlso, excited to meet some of you at the meetup on Thursday! Happy building. ","type":"text"},{"name":"sparkles","type":"emoji","unicode":"2728"}]}]}],"markdown":"This weekend, I decided to build an AEO tool that helps with citation as a publisher. My goal was to rank higher as a source for things we've written about- for example, if someone looks up ‘Richard Nadler’, I want our interview cited by agents first.\n\nIt uses LLM-as-judge to gauge performance and review article drafts to improve AEO before publishing. Started with Profound to see our overall visibility as a publisher, then got curious how agents cited our interviews against other publications. Happy to share the link if anyone wants to play around.\n\nAlso, excited to meet some of you at the meetup on Thursday! Happy building. :sparkles:","permalink":"https://tryprofound-community.slack.com/archives/C0BH60Q4X1S/p1788881963221999","parent_slack_message_id":null,"username":null,"icon":null,"is_shared":false},"cover_image_file_id":"c6be0b53-bcd1-4401-a13b-31a0a5a88a76","cover_image_url":"https://pthsiwxwiiowghdtkwlv.supabase.co/storage/v1/object/public/slack_files/T08NL2JBAGY/1acf49b6-1600-48cd-9aa2-b442bda1c022/F0C0C2MFGLA/Screenshot%202026-09-08%20at%2011.19.09_AM.png","author":{"id":"1acf49b6-1600-48cd-9aa2-b442bda1c022","preferred_name":"Joe","preferred_image_original":"https://avatars.slack-edge.com/2026-08-28/11963233174928_e269eb167ce78192a0a1_original.png","preferred_image_1024":"https://avatars.slack-edge.com/2026-08-28/11963233174928_e269eb167ce78192a0a1_1024.png","preferred_image_512":"https://avatars.slack-edge.com/2026-08-28/11963233174928_e269eb167ce78192a0a1_512.png","preferred_image_192":"https://avatars.slack-edge.com/2026-08-28/11963233174928_e269eb167ce78192a0a1_192.png","preferred_image_72":"https://avatars.slack-edge.com/2026-08-28/11963233174928_e269eb167ce78192a0a1_72.png","preferred_image_48":"https://avatars.slack-edge.com/2026-08-28/11963233174928_e269eb167ce78192a0a1_48.png","preferred_image_32":"https://avatars.slack-edge.com/2026-08-28/11963233174928_e269eb167ce78192a0a1_32.png","preferred_image_24":"https://avatars.slack-edge.com/2026-08-28/11963233174928_e269eb167ce78192a0a1_24.png","slack_profile_id":"U0BTESA238W","is_team_member":false,"slack_profile":{"id":"U0BTESA238W","is_admin":false,"is_bot":false,"is_forgotten":false,"deleted":false},"group_flair":null},"best_answer_message_id":null,"selected_answer_message_ids":[],"selected_answer_count":0,"feed":{"id":"f4e3fb84-5d2a-4634-9594-7936ace0f480","public_id":"feed_TGBUr5saga4y","label":"Showcase","slug":"showcase","is_posting_from_site_allowed":true,"allow_selected_answer":false,"visibility":"public"},"references":{"mentioned_slack_users":[],"mentioned_slack_channels":[],"custom_emojis":[],"urls":[]}},{"id":"nkcnrx19xsvu","message_id":"msg_I08iHhPxsl36","created_at":"2026-09-04T16:14:17.418176+00:00","content_created_at":"2026-09-04T16:14:14.402+00:00","latest_activity_at":"2026-09-05T00:36:07.101+00:00","title":"Introducing a YouTube Agent for Smart Content Briefs Based on Live Data and AI Search Demand","slug":"introducing-a-youtube-agent-for-smart-content-brie","description":"Discover a YouTube agent that generates AI-driven content briefs based on live data to boost citation share and guide video content creation.","feed_id":"f4e3fb84-5d2a-4634-9594-7936ace0f480","post_detail_display_format":"discussion","author_profile_id":"b09c9e72-02b4-4c2f-8337-6a85dde4be48","body_prosemirror":{"type":"doc","content":[{"type":"paragraph","content":[{"text":"Here's a","type":"text"},{"text":" YouTube","type":"text","marks":[{"type":"bold"}]},{"text":" agent I created. It's about to enter the marketplace but there's a platform issue holding that up","type":"text"}]},{"type":"paragraph","content":[{"text":"What it does:","type":"text","marks":[{"type":"bold"}]},{"type":"hardBreak"},{"text":"Each week, the agent pulls YouTube citation share and AI search demand for your topics, identifies where citation share is dropping while demand is rising, and produces a ready-to-use content brief for each gap: recommended format, title options, talking points, description, and tags.","type":"text"}]},{"type":"paragraph","content":[{"text":"Where it fits:","type":"text","marks":[{"type":"bold"}]},{"type":"hardBreak"},{"text":"Research and content creation stages of GEO. The outcome is recovered citation share on YouTube, with clear priority on where you're losing ground fastest.","type":"text"}]},{"type":"paragraph","content":[{"text":"How accuracy is maintained:","type":"text","marks":[{"type":"bold"}]}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"text":"All citation share, search demand, and sub-topic data comes from live Profound and DataForSEO data. Nothing is estimated.","type":"text"}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"text":"Before writing a brief, the agent checks your knowledge base for existing video content and brand guidelines, so it can flag likely duplicates and keep tone consistent. If the knowledge base has no relevant content yet, the brief notes that so nothing is assumed.","type":"text"}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"text":"Every brief is routed for human review before it's shared.","type":"text"}]}]}]},{"type":"paragraph","content":[{"type":"hardBreak"},{"text":"Inputs needed:","type":"text","marks":[{"type":"bold"}]},{"type":"hardBreak"},{"text":"Topic keywords (required) and brand name (optional, used to tailor tone and positioning).","type":"text"}]},{"type":"paragraph","content":[{"text":"If you're looking for a quick and effective way to build out YouTube content...the right content...this could help","type":"text"}]},{"type":"paragraph","content":[{"text":"I used the output this week to pitch 10 YouTube ideas to a potential client","type":"text"}]},{"type":"paragraph","content":[{"text":"If you have something similar, or feedback on ways to improve it, can you tag me here? Cheers","type":"text"},{"text":"🍻","type":"text"}]}]},"body_plaintext":"$54","source":"slack","edited_at":null,"version":0,"is_published":true,"file_attachments_count":2,"reactions_count":14,"comments_count":5,"slack_message":{"id":"d0e5c36e-ef03-441d-9698-782f12074612","message_ts":"1788538454.402209","blocks":[{"type":"rich_text","block_id":"sRmyu","elements":[{"type":"rich_text_section","elements":[{"text":"Here's a","type":"text"},{"text":" YouTube","type":"text","style":{"bold":true}},{"text":" agent I created. 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The outcome is recovered citation share on YouTube, with clear priority on where you're losing ground fastest.\n\n","type":"text"},{"text":"How accuracy is maintained:","type":"text","style":{"bold":true}},{"text":"\n","type":"text"}]},{"type":"rich_text_list","style":"bullet","border":0,"indent":0,"elements":[{"type":"rich_text_section","elements":[{"text":"All citation share, search demand, and sub-topic data comes from live Profound and DataForSEO data. Nothing is estimated.","type":"text"}]},{"type":"rich_text_section","elements":[{"text":"Before writing a brief, the agent checks your knowledge base for existing video content and brand guidelines, so it can flag likely duplicates and keep tone consistent. 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Devon\n\nlinkedin.com/feed/update/urn:li:activity:7490868362256994304 (https://www.linkedin.com/feed/update/urn:li:activity:7490868362256994304/)","source":"slack","edited_at":null,"version":0,"is_published":true,"file_attachments_count":0,"reactions_count":1,"comments_count":0,"slack_message":{"id":"95dd1a3d-c15e-41b9-b350-c31d3f54f81c","message_ts":"1787818908.498809","blocks":[{"type":"rich_text","block_id":"D0R/i","elements":[{"type":"rich_text_section","elements":[{"text":"And this one sends me a daily surf report from my favourite beach in the UK, Saunton Sands in Devon\n\n","type":"text"},{"url":"https://www.linkedin.com/feed/update/urn:li:activity:7490868362256994304/","text":"linkedin.com/feed/update/urn:li:activity:7490868362256994304","type":"link","unsafe":true,"truncated":true}]}]}],"markdown":"And this one sends me a daily surf report from my favourite beach in the UK, Saunton Sands in 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Also -- it will also remind me that the weekend is here .. that doesn't hurt 😎\n\nWe do a ton of Reddit work, so one of the first agents I submitted was a Reddit sentiment analyzer. It lets you score Reddit threads before and after, so it gives our strategists a simple way of showing \"here's what we identified, here's what we did, here's how we measured our impact.\"\n\nIt's easy to get buried in a lot of reporting and metrics, but when a brand comes to us with sentiment as a north star metric, we want a simple way to show the impact of our work.\n\nIt's in the marketplace. 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