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Most content creators struggle with topic ideas, not writing. This tutorial walks through building a Claude Code Skill that automates Reddit content research — from scraping discussions to producing platform-ready posts.
Content creators hit a wall that has nothing to do with writing ability. The real bottleneck is a steady supply of raw material — pain points, fresh angles, real user opinions. Without a reliable source, you end up staring at a blank page or recycling the same ideas your competitors already published.
Reddit solves this problem. I built a Claude Code Skill that turns Reddit discussions into platform-ready content, and one test run produced 24 publishable pieces from 125 posts in about 10 minutes. This tutorial breaks down exactly how the reddit content research automation works and how you can build your own.
Reddit holds a unique position among content sources. Every subreddit is a niche community where users post under pseudonyms, which means they share real problems instead of polished personal brands. The comment threads often matter more than the original post — dozens of people correcting, extending, and debating a single idea.
I rely on Reddit for two reasons I have not found elsewhere.
Anonymous posting produces honest signal. On most social platforms, users protect a personal brand. On Reddit, someone writes "I tried this tool and step 3 broke — anyone else?" instead of "Here's my 10-step success framework." That raw honesty is exactly the material content creators need.
Comments outperform posts. A single post contains one person's take. The comment section contains 50 people arguing, correcting, and adding context. When I ran a research session on Claude Code Skills, the highest-value insights came from comment threads where developers disagreed about whether Skills are just advanced prompts — not from any single post.
Google is a library. Reddit is a market where people argue out loud about what actually works and what does not.
The manual workflow looks simple: pick keywords, search Reddit, open posts, read comments, jot down useful bits, repeat with new keywords. In practice, one topic eats 3 to 5 hours. If you publish across multiple platforms — X, Instagram, Newsletter — and each requires a different format, the workload triples.
I spent an entire afternoon manually mining Reddit for one topic. The output was a single draft I was not even satisfied with. That session made the problem clear: the constraint was not writing speed. It was the supply chain feeding raw material into the writing process.
So I stopped optimizing how fast I write and started optimizing how material flows in. The solution was a Claude Code Skill — a structured instruction set that an AI agent follows step by step, like handing an operations manual to a new team member.
Here are the numbers from one test run using "Claude Code Skill" as the input topic.
The skill generated 5 English search keywords covering the core term, synonyms, tutorials, experience reports, and pain-point discussions:
Claude Code skill
Claude Code custom slash command
Claude Code skill setup create
Claude Code skill workflow tips
Claude Code skill not working issue
Five search rounds collected 125 candidate posts. After deduplication by post ID and ranking by a heat score (upvotes + comments x 2), the top 10 posts went through deep analysis. The agent pulled 66 curated comments, filtered out 2 irrelevant posts (a gift card thread and a meme), and distilled 8 content angles from the remaining 8.
The angles ranged from "Skill activation rates below 20% — naming is the key variable" to "Non-programmers turning Claude Code into a business brain." Each angle then produced content for three platforms: X posts, Instagram notes, and Newsletter articles.
Final output: 8 angles x 3 platforms = 24 pieces of ready-to-edit content in roughly 10 minutes.
The time savings are not about AI writing a few paragraphs. The real gain is that the entire chain — from raw Reddit data to publishable multi-platform content — runs end to end without manual intervention.
The skill is called reddit-content-researching. It takes one input — a topic — and produces multi-platform content through 7 sequential steps. The core design principle: deterministic operations run as scripts, creative operations run as AI agents.
You give the agent a single topic, like "Claude Code Skill." The agent creates a run directory, initializes a progress.json state file, and records the topic, output path, and current step.
This state file is not cosmetic. Without it, an interrupted run has no way to resume. With it, the agent knows exactly which step failed and which file to read next.
The agent generates 5 English search keywords, each covering a different search intent:
| Intent | Purpose | Example |
|---|---|---|
| Core term | Exact match anchor | Claude Code skill |
| Synonym | Broaden coverage | Claude Code custom slash command |
| Tutorial | Surface how-to content | Claude Code skill setup create |
| Experience | Find first-person reports | Claude Code skill workflow tips |
| Pain point | Capture frustrations | Claude Code skill not working issue |
One rule is non-negotiable: every keyword must retain the anchor term. Drop the anchor and searches drift into generic AI tool discussions that waste the entire pipeline.
A Python script queries the Reddit data API — 5 rounds, 25 posts per round, 125 total. The script deduplicates by post ID, then ranks by heat score:
heat_score = upvotes + (comment_count * 2)
Comment count gets double weight because high-comment posts signal deeper discussion, which means richer material for content angles.
This step uses a script, not an AI agent. Search, deduplication, and sorting are deterministic operations. Scripts handle them faster, cheaper, and more reliably.
The top 10 posts by heat score go through detail extraction. This is where data diet matters most.
A raw Reddit post can have hundreds of comments. Feeding everything to an AI agent causes context window bloat and degrades output quality. The script uses layered sampling:
Each comment is truncated to 200 characters. Post bodies cap at 3,000 characters. Total token budget for 10 posts stays around 83K — enough for the AI to work with, lean enough to avoid noise.
The principle is not "show the AI everything." It is "show the AI the most valuable data." Information overload is the number-one killer of AI collaboration quality.
A dedicated SubAgent reads all 10 post detail files and performs two tasks: filter irrelevant posts, then extract up to 10 non-overlapping content angles from the relevant ones.
Why one agent instead of five? For the same reason a publication has one editor-in-chief instead of five interns each picking topics independently. Five interns produce three overlapping pitches. One editor produces a coherent editorial calendar.
Each angle maps to a content type — deep dive, opinion piece, experience summary, controversy analysis, tutorial — so the final output covers diverse formats.
Five SubAgents split the angles and run in 3 rounds (2 + 2 + 1). Each SubAgent produces three pieces per angle:
Same source material, three completely different expressions. Multi-platform distribution is not copy-pasting one draft three times. It is one insight, multiple native formats.
A Python script — not an AI agent — merges all content fragments into a single content.md file with a source attribution table.
File merging, sorting, and concatenation are deterministic. They require zero creativity and total reliability. Scripts win here every time.
On the surface, this is a Reddit scraping tool. Under the surface, it replicates how a publishing editor works: topic selection, research, filtering, writing, final assembly. Each step has defined inputs, outputs, and quality gates.
The core principle is simple: deterministic operations use scripts, creative operations use AI.
| Operation | Type | Executor |
|---|---|---|
| Search, deduplicate, sort, merge | Deterministic | Python scripts |
| Angle extraction, content writing, format adaptation | Creative | AI agents |
This separation is not a preference. It is an engineering decision. Scripts are cheaper, faster, and reproducible. AI agents are flexible, contextual, and capable of judgment. Mixing them up — using AI for file merging or scripts for angle detection — degrades both cost and quality.
Skills are operations manuals for AI agents. When you decompose a complex workflow into steps clear enough for an agent to follow, you turn your expertise into a reusable, zero-marginal-cost asset.
You do not need to build the full skill to capture value from this approach. Start with the manual minimum viable workflow:
Fifteen minutes of this process will surface more angles than an hour of scrolling Google. Once you validate that Reddit research feeds your content pipeline, automate it into a Skill and let an agent run it on repeat.
Copy this prompt into Claude Code to scaffold the entire Reddit content research skill:
Build a Reddit topic insight Skill. Input: research topic. Output:
multi-platform content (X posts, Instagram notes, Newsletter articles).
7-step pipeline:
1. Requirements (init + progress.json for resume)
2. Keyword design (5 English keywords covering core/synonym/tutorial/
experience/pain-point, must retain anchor term)
3. Data collection (Python script, Reddit API search, rank by
score + comments * 2, deduplicate by post ID)
4. Detail extraction (Top 10 posts, layered comment sampling:
top-voted 8 + hot 4 + newest 3, body cap 3000 chars,
comment cap 200 chars)
5. Angle planning (single SubAgent, up to 10 non-overlapping angles,
matched to content types)
6. Content production (5 SubAgents in 3 rounds 2+2+1,
each angle produces 3 platform pieces)
7. Merge output (Python script, deterministic concatenation into
content.md + source table)
Design principles: deterministic ops use scripts, creative ops use AI.
SubAgent context isolation — return minimal results only.
Main agent never reads piece files to protect context window.
Reddit's subreddit structure organizes discussions by niche topic. Anonymous posting encourages honest pain points and real tool feedback. Comment threads surface multiple perspectives on a single issue. These qualities make Reddit ideal for automated content research — the data is structured, authentic, and rich in angles that other platforms lack.
You can start without code. Search Reddit manually using English keywords, sort by Top (Past Month), and read the top 5 comment threads. Record recurring questions, high-upvote quotes, and heated disagreements. Once you validate the workflow, use Claude Code to automate it into a reusable Skill with Python scripts for deterministic steps and AI agents for creative steps.
The skill runs 5 search rounds with 25 results each, then deduplicates by post ID. For the top 10 posts, it uses layered comment sampling — 8 top-voted, 4 hot, 3 newest — with each comment truncated to 200 characters and post bodies capped at 3,000. This keeps token usage under 83K while preserving the highest-signal content for AI analysis.
Yes. The architecture separates data collection (scripts) from creative analysis (AI agents). You can swap the Reddit scraper for any structured data source — Hacker News, Stack Overflow, Product Hunt, or niche forums — while keeping the angle extraction and content production pipeline intact.
— Leo
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Content creators don't struggle with writing — they struggle with finding good material. I built a Claude Code Skill that automates Reddit research, extracts content angles, and produces platform-ready posts. Here's the full 7-step pipeline.