AI-Cited Threads

You find the Reddit threads that AI assistants already link to when a buyer asks a question, decide which of those threads deserve a reply, and write the reply. AI answers keep citing a thread long after its last comment, so one good reply on the right old thread can be read by buyers for months. Reddit's own rules and each subreddit's rules come first. Every reply says who you are.

Before you start

Read .agents/reddit-marketing.md if it exists (the reddit-context skill writes it). Use its buyer prompts, competitor list, subreddit table, accounts and disclosure line. Ask only for what it does not cover. If the file is missing, ask for: the product in one line, 5 questions a buyer would type into ChatGPT, 3 competitors, and the disclosure line the account will use. Offer to run reddit-context first.

The data this skill uses

The CommunityMentions MCP is public, no sign-in: https://communitymentions.com/api/mcp.

• find_subreddits takes a short description ("cold email software for B2B outbound") and returns up to 10 subreddits with AI citations, members and threads cited.
• get_subreddit takes one subreddit and returns its AI citations by engine (OpenAI models, Perplexity, Gemini), the question types behind the citations (comparing products, looking to buy, describing a problem), the date range, and its most-cited threads with a citation count each.

Totals in the dataset as of 2026-10-28 (CommunityMentions data): 34,493 citations across 75,343 AI answers. get_subreddit returns the top 5 threads per subreddit, so it shows the head of the list, not all of it. Read references/citation-capture.md for how to find threads beyond the top 5.

Workflow

1. Pick the questions. Take 5-10 buyer prompts from the context file. Sort them by the three question types the data uses: comparing products, looking to buy, describing a problem. Buyers who compare or look to buy are closest to paying. In r/SaaS, 128 of 300 citations were tagged "looking to buy" and 66 "comparing products" (CommunityMentions data, 2026-10-28).
2. Find the subreddits. Run find_subreddits with the product description. Keep the ones where ai_citations is above the median the tool reports (96 for the query "cold email software for B2B outbound", CommunityMentions data, 2026-10-28), then call get_subreddit on each.
3. Collect candidate threads. Start with most_cited_threads from each subreddit. Add threads you capture by running your buyer prompts in ChatGPT, Perplexity and Gemini and noting which Reddit URLs appear (method in references/citation-capture.md). Log each thread in the sheet from references/thread-log.md.
4. Open each thread and read all of it. Note the post date, comment count, whether it is locked or archived, the top answers, the rules in the sidebar, and what the thread does not say. Reddit archives threads after a set time, which blocks new comments. Check the thread page itself and drop archived or locked ones.
5. Score and decide. Use the rubric in references/thread-triage.md: question fit, gap, room (can a new comment still be seen), honest fit for your product, rule risk. Answer the 3-5 best threads this week, not all of them. Say "skip" for a thread where the honest answer is a competitor or where self-promotion is banned and your reply would be only a plug.
6. Find the gap. Write one line: "The thread has X, it does not have Y." Typical gaps: answers are from 2023 and prices changed, nobody states a number, nobody names who the tool is wrong for, nobody tested it, the top answer is a plug with no disclosure. Your reply fills that one gap and nothing else.
7. Write the reply with references/reply-patterns.md. Disclose in the first lines, answer the question asked, give the evidence (numbers, dates, what you tried), name the case where you are the wrong choice, link only if the subreddit allows it. Pass the draft to reddit-comments for voice and subreddit-rules for the rule check.
8. Post by hand from the real account after the user approves the text. Log the URL and date in the thread log.
9. Re-check. After 14 days and again after 60, re-run the buyer prompt in each engine and note whether the thread is still cited, whether your comment is visible, and whether it was removed. Hand the numbers to reddit-ai-search. A citation of the thread does not prove the engine used your comment. Say so in the log.

A worked example (CommunityMentions data, 2026-10-28)

get_subreddit for coldemail returns 96 AI citations from 90 answers across 44 threads, cited from 2026-02-20 to 2026-10-09. The top thread, "Recommendations on best cold email tools for…", has 13 citations, 14% of the subreddit's 96. The second, "Looking for alternatives to rocketreach but…", has 9. A thread titled "Very disappointed with instantly any good" has 7, so a complaint thread about one tool gets cited as often as a ranking thread.

What an agent does with that: open all three, read the comments, and find the gap. For a complaint thread, the gap is often a fix the complainer did not try, with settings and results. For a "best tools" thread, the gap is often "who each tool is wrong for". More worked examples, with r/SaaS, r/Entrepreneur and r/smallbusiness, are in references/worked-examples.md.

Choosing between answering and skipping

A quick test before you spend time scoring: say the thread's question out loud, then say your honest answer. If your honest answer is "use something else", skip, or answer with the other product's name and a fair reason. A reply that sends a buyer to the right tool earns more trust than a stretch, and readers and mods can tell.

If the honest answer is you, check three more things: the account is old enough for the subreddit (see reddit-account-health), the rules allow brand mentions with disclosure (see subreddit-rules), and you can add a fact nobody in the thread has. Without all three, skip it and log why.

What a thread read looks like

Read the whole thread before you score it. Fill these six lines for each one:

1. Ask. What the original poster wants, in their words. Copy the sentence.
2. Stage. Comparing products, looking to buy, or describing a problem.
3. Winning answers. The 2-3 top comments and what they recommend.
4. Missing. What a buyer reading this thread would still not know. This is the gap.
5. Vibe. Do people post links and brand names here, or are they removed? Look for mod comments.
6. Date check. Posted date, last comment date, and any named tool or price that has changed since.

If you cannot fill line 4, skip the thread. A reply with no gap repeats what the thread already says.

Where the engines differ in this data

In the eight subreddits pulled on 2026-10-28 (r/SaaS, r/coldemail, r/marketing, r/Entrepreneur, r/smallbusiness, r/ecommerce, r/sales, r/b2bmarketing; CommunityMentions data), Perplexity made 944 of 1,312 citations (72%), Gemini 200 (15%) and OpenAI models 168 (13%). The split is uneven: in r/coldemail OpenAI models made 52 of 96, and in r/marketing they made 0 of 52. This is eight subreddits, not the whole dataset, so test your own category before you pick an engine to target. The per-subreddit table and how to repeat the pull are in reddit-ai-search.

What you hand back to the user

A short report, in this order:

1. The 3-5 threads to answer, each with: URL, subreddit, citation count with the date pulled, the gap line, and the draft reply.
2. The threads you skipped, one line each with the reason (archived, banned promotion, honest answer is a competitor).
3. The rules found for each subreddit (from subreddit-rules) and whether the account meets them.
4. The re-check dates (14 and 60 days out).
5. One thing you could not verify, stated plainly.

Do not post anything. The user posts from their own account after reading the text.

Time estimates

• Pull data and build the candidate list for one product: 30-45 minutes.
• Read and score 10 threads: about an hour.
• Draft 3 replies with review: about an hour.
• Re-checks: 20 minutes per month for 10 replies. These are planning guesses. Time your first run and write the real numbers in the log.

Rules for honest replies

• Disclose your tie in the first two lines: "I work on X." The wording is in reddit-context's disclosure notes.
• One account, your own, with real history. No second accounts, no asking friends to upvote, no bought accounts.
• Do not reply to a thread only because it is cited. If your honest answer is another product, say so or skip.
• Do not edit an old comment to swap in a link after it has votes.
• Respect removals. If a mod removes the comment, read the rule, and do not repost.

Quality check

• Each chosen thread has the post date, comment count, citation count, source and date of that count.
• Each reply has a one-line gap statement above it in the log.
• Each reply discloses the tie in its first two lines and gives at least one specific fact (number, date, test).
• Each reply names a case where the product is a poor fit.
• No two replies share a sentence.
• The log has a re-check date for every posted reply.

What to avoid

• Copy-pasting one reply across threads. Moderators and readers notice, and the gap differs in each thread.
• Answering with a link and one line.
• Claiming a citation count you did not pull. Say "unverified" and show the date.
• Treating the top-5 list as the full picture of a subreddit.
• Promising that a reply will be cited. No data supports a promise. Test it and report the result.

Related Skills

• reddit-context: supplies buyer prompts, competitors, accounts and the disclosure line.
• subreddit-research: picks which subreddits to pull threads from.
• subreddit-rules: reads the sidebar and wiki so the reply breaks no rule.
• reddit-comments: sets the voice and checks the finished draft.
• reddit-ai-search: measures whether citations rise across the whole subreddit and each engine over time.
• reddit-account-health: confirms the account is old enough and clean enough to post in the chosen subreddits.