Subreddit Topic Discovery

Analyzes a specified subreddit, extracts high-performing posts, identifies recurring themes, ranks topics by popularity, and delivers actionable community insights.

redditcommunity-analysistopic-modelinginsights

8 Agentic Workers

Premium Prompt

Expertly crafted & tested

What the agent does

These are the instructions your agent follows. It asks you for what it needs, then does the work in chat.

Goal

Deliver a structured report that:

retrieves and tabulates the top N posts from a subreddit for a chosen timeframe,
extracts and clusters recurring themes, ranks them by a quantitative popularity score, and
summarizes key insights and recommendations for engaging that community.

Inputs to gather

• subreddit – the exact subreddit name to analyze (defines data source).
• num_posts – how many top posts to fetch (controls sample size).
• time_period – one of day / week / month / year / all (limits timeframe).

Before doing any work, ask the user for these inputs in ONE message. Skip anything they already provided. If they tell you to decide, choose sensible defaults and say what you chose.

Workflow

Act as a Reddit data collector.
a. Retrieve the top num_posts posts from subreddit within time_period.
b. For each post capture: Rank, Title, Upvotes, Number_of_Comments, Award_Count, Date_Posted, Permalink.
c. Present these in a table sorted by Rank.
d. Pause and ask the user if the dataset looks correct before proceeding.
Act as a text pre-processor and word-frequency analyst.
a. Extract all post titles.
b. Clean the text: lowercase, remove punctuation, stopwords, and any subreddit-specific jargon; lemmatize.
c. Produce a frequency table of the 50 most significant words/phrases with counts.
d. Share the frequency table; confirm with the user to continue.
Act as a topic extractor.
a. Using cleaned titles and the frequency table, cluster posts into 5–10 distinct thematic topics.
b. For each topic provide:
• Topic_Label (human-readable)
• Representative_Words/Phrases (3–5)
• Example_Post_Titles (2)
• Post_IDs_Matching (list of Rank numbers)
c. Ensure minimal overlap between topics.
d. Check in with the user for validation or adjustments.
Act as a quantitative popularity assessor.
a. For each topic calculate Popularity_Score = Σ(Upvotes + 0.5 × Comments + 2 × Award_Count) of its posts.
b. Rank topics by Popularity_Score descending and present a table.
c. Explain the formula and why each weight was chosen.
Act as a community insight strategist.
a. Summarize the 3–5 most popular topics and what they reveal about the community.
b. Provide three actionable recommendations for content creators, brands, or researchers seeking to engage subreddit, each tied to earlier data.
c. Highlight any surprising or emerging niche topics worth monitoring.
Review outputs against all steps and inputs. If anything is missing or unclear, tell the user exactly which step needs rerunning or clarification before finalizing.

Output

A multi-section report containing:

Table of top posts.
Frequency table of key words/phrases.
Topic cluster details.
Popularity-ranking table with formula explanation.
Insight summary with recommendations and emerging niches.
Provide the final deliverable either as formatted Markdown or a readable table-rich text, suitable for direct sharing or further analysis.

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Key Benefits

Discover how our intelligent prompt chain enhances your workflow

Comprehensive Data Collection

By systematically fetching the top posts based on user-defined parameters, users can access a wealth of relevant and up-to-date content, making it easier to understand trends and popular discussions within a specific subreddit.

Text Pre-processing for Clarity

The cleaning and normalization of post titles ensure that the analysis is based on clear, concise data, allowing users to identify significant themes without the noise of irrelevant jargon or formatting, thus enhancing the accuracy of insights derived from the data.

In-depth Thematic Analysis

Clustering posts into distinct topics helps users to pinpoint specific interests and areas of engagement within the community, providing a structured way to interpret user behavior and content preferences, which is invaluable for content strategizing.

Data-Driven Community Insights

By computing and ranking popularity scores, users gain a quantitative measure of what resonates within the subreddit, allowing for informed decisions and targeted strategies for content creation, marketing, or research tailored to community interests.

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Frequently Asked Questions

One click and your agent runs it. Sign in, pick which of your agents should take it on, and it starts the skill in chat — asking you for whatever it needs along the way. No copying, pasting, or browser extension.

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Our prompt chains are expertly crafted and tested across multiple AI models. Unlike single prompts, our chains combine multiple optimized prompts that work together, including specific instructions, context layers, and output formatting. Teams using our prompt chains report 3x better results and seamless collaboration.

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Users report saving 4-6 hours per week using this optimized prompt compared to traditional methods. The structured output and refined instructions eliminate the need for multiple iterations.

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