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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.
TL;DR — I built a Reddit content research skill for Claude Code that takes a single topic, scrapes Reddit for relevant discussions, extracts non-obvious content angles, and produces platform-ready posts. One test run turned 125 Reddit posts into 24 publishable pieces in about 10 minutes. Here is the full 7-step pipeline and the design principle behind it.
Most content creators hit the same wall. The deadline is tomorrow, and there is nothing to write about.
You open a search engine, browse for thirty minutes, and find the same recycled takes everywhere. You check what competitors published last week — they beat you to every angle. The problem is not your writing. The problem is your supply chain.
I was stuck in this loop for months. Then I found a source most English-speaking creators underuse for original content research: Reddit.
Reddit is not just a forum — it is an unstructured primary source database, and the foundation of any good Reddit content research skill. People post real questions, real frustrations, real workarounds. Comment sections routinely contain more insight than the original posts. And because these discussions are scattered across thousands of subreddits, most of the material never makes it into polished blog posts or newsletters.
I decided to build an automated pipeline that goes from Reddit research to finished content — and package it as a Reddit content research skill for Claude Code so I can rerun it with a single command.
The value of Reddit for content research is not its size. It is the information asymmetry.
Most creators look for topics on Twitter, Hacker News, or Product Hunt. These platforms are valuable, but everything you find there has already been seen by thousands of other creators. By the time you write about it, the angle is stale.
Reddit operates differently. It has deeply specialized subreddits covering AI tooling, developer workflows, side projects, SaaS marketing, freelancing, and hundreds of other niches. Discussions in these communities surface real pain points weeks or months before they show up in mainstream content.
Two qualities make Reddit particularly useful for content research.
First, anonymity produces honesty. Users have no personal brand to protect. Instead of polished takes like "here is my framework for scaling revenue," you get raw reports like "I tried this tool, step three broke, has anyone figured out the workaround?" That kind of specificity — real problems described by real users — is exactly what good content is built on.
Second, comment sections are more valuable than posts. A single Reddit post represents one person's perspective. The comment section is where dozens of people push back, add context, share alternatives, and surface edge cases. The real content gold is not in the headline. It is in the collisions happening below it.
Search engines are libraries. Reddit is a marketplace. Libraries are useful when you know what you are looking for. Marketplaces let you hear what real people are arguing about, asking for, and complaining about — before anyone else packages those insights into content.
The manual version of this workflow is simple. It is also brutal.
You think of keywords. Search Reddit. Open posts one by one. Read the body text, then the comments. Write down anything useful. Switch to a different keyword and repeat.
One topic takes three to five hours. That is before you write a single word.
If you publish across multiple platforms — a newsletter, a blog, and social media — the workload multiplies further because each platform needs a different structure and tone.
Most creators who quit publishing regularly do not quit because they cannot write. They quit because the research and ideation phase drains them before they ever reach the writing phase.
I stopped trying to optimize "how to write faster" and instead optimized "how material arrives consistently." That shift changed everything.
The approach: package the entire Reddit content research skill — collection, deduplication, ranking, angle extraction, multi-platform writing — into a single Claude Code Skill file. The agent executes the pipeline. The Skill file is the operations manual. Once the manual is clear enough, the agent runs the entire process end to end.
The Reddit content research skill handles seven discrete steps. Each step has defined inputs and outputs. Here is how they connect.
You provide one topic. That is it. Something like Claude Code Skill or AI SEO automation.
The agent initializes a run directory, creates a state file, and records the topic, output path, and current progress.
This sounds trivial, but the state file is critical. Without it, if the pipeline crashes mid-run, recovery means guessing where things stopped. With the state file, the agent knows exactly which step completed and which file to read next.
The agent produces five English search keywords automatically, covering five directions:
Core term: Claude Code skill
Synonym: Claude Code custom slash command
Tutorial: Claude Code skill setup create
Experience: Claude Code skill workflow tips
Pain point: Claude Code skill not working issue
Core terms prevent drift. Synonyms expand coverage. Tutorial and experience keywords surface positive material. Pain-point keywords surface frustrations — often the richest source of content angles.
One rule is non-negotiable: every keyword must retain the anchor phrase. If the topic is Claude Code Skill, the keywords can vary, but none of them can drop the anchor entirely. Without this constraint, searches drift into generic AI tool discussions.
A script queries the Reddit data endpoint — not an LLM — for each keyword. Five rounds of 25 posts each yield 125 candidates. The script deduplicates by post ID, then ranks by a composite score (a deterministic calculation — meaning the same inputs always produce the same output, no AI judgment involved):
score = upvotes + (comment_count × 2)
Comment count gets double weight. Posts with many comments usually mean deeper discussion. Content research needs debate, not consensus. A post with 50 upvotes and 200 comments is more useful than one with 500 upvotes and 3 comments.
I was wrong about one thing early on: I initially let the AI handle deduplication. It was slow, expensive, and occasionally missed duplicates. Switching to a deterministic script made this step both faster and cheaper.
The pipeline takes the top 10 ranked posts and fetches their full text plus comments.
Raw comment threads can contain hundreds of replies. Feeding all of them to an LLM causes context overflow and degrades output quality. So the pipeline uses layered sampling: it takes a portion of top-voted comments, a portion of controversial replies, and a portion of recent comments. Each comment is truncated to a reasonable length. Post bodies get a length cap too.
The principle is not "give the AI all the data." The principle is "give the AI the most valuable data."
Information overload is the most common failure mode in AI-assisted workflows. Effective pipeline design is not about stuffing everything into the context window. It is about controlling input so the model can judge, process, and reuse what it receives.
A dedicated sub-agent reads the top 10 post summaries. It first filters out irrelevant posts, then extracts non-overlapping content directions from the remaining ones.
I deliberately avoid running multiple agents in parallel for angle extraction. The reason is straightforward: five interns brainstorming independently will overlap on angles. One editor reviewing everything at once produces more diverse, strategically coherent directions.
The sub-agent acts as that editor.
From the Claude Code Skill test run, it produced eight angles:
Once angles are confirmed, multiple sub-agents produce content in parallel — one batch per angle.
Each angle yields three outputs:
Same source material. Three different structures. Multi-platform distribution is not copy-pasting the same text three times. It is one insight expressed in three formats, each respecting platform conventions.
A script — not an AI — combines the content fragments into a single document organized by angle, with a source attribution table appended.
Why not let the AI merge? Because merging files, sorting sections, concatenating text, and writing a table of contents are deterministic operations. They do not need creativity. They need reliability.
This is the core design principle behind the entire pipeline.
I ran the full pipeline with Claude Code Skill as the input topic.
Here is what happened:
| Stage | Output |
|---|---|
| Keywords generated | 5 |
| Posts collected | 125 |
| After deduplication | ~80 |
| Deep-analyzed (top ranked) | 10 |
| Content angles extracted | 8 |
| Platform-ready pieces produced | 24 |
| Total time | ~10 minutes |
The time savings are real, but they are not the main point. What matters is that the pipeline ran the entire chain — from raw Reddit discussions to publishable content — without me touching any intermediate step.
The 24 pieces were not all perfect. Some needed editing. But every single one had a defensible angle grounded in real community discussion, not generated from thin air.
If you look at the pipeline, a pattern emerges: some steps use scripts, and some steps use AI agents. The split is intentional.
| Task | Handler | Why |
|---|---|---|
| Search Reddit | Script | Deterministic query, stable API |
| Deduplicate posts | Script | Exact ID matching, no judgment needed |
| Rank by score | Script | Arithmetic, not opinion |
| Filter irrelevant posts | AI Agent | Requires reading comprehension |
| Extract content angles | AI Agent | Requires creative synthesis |
| Write platform-specific content | AI Agent | Requires style adaptation |
| Merge final output | Script | File concatenation, no creativity |
Scripts handle certainty. Agents handle creativity. Searching, deduplicating, sorting, and merging are operations where you want zero variance. Judging relevance, extracting angles, and adapting tone are tasks where you want the model's reasoning capability.
Mixing these up — letting AI handle deterministic tasks or forcing scripts to handle judgment calls — is how most automation pipelines break. I made this mistake early: I had the AI do deduplication. It was three times slower and occasionally hallucinated duplicate IDs.
The design principle extends beyond this pipeline. Any AI workflow that tries to hand everything to the model will be slower, more expensive, and less reliable than one that draws a clear line between what needs judgment and what needs stability.
On the surface, this Reddit content research skill is just a research tool.
Under the surface, it replicates the workflow of a publishing editor.
How does an editor work? Topic selection. Source gathering. Filtering. Writing. Final review. Every step has defined inputs, outputs, and quality gates.
A Skill works the same way: it codifies expert workflows into standard operating procedures that an agent can follow.
The agent is the executor. The Skill is the operations manual. When a complex task gets broken into steps that are clear enough, the agent runs the entire process for you.
This has a consequence for how content creation will evolve. Creators will increasingly split into two groups: those who build automated research and production pipelines, and those who do not. The first group is not lazier. They are better at decomposition — breaking complex work into steps precise enough for an AI agent to execute.
The most important capability in the AI era is not knowing how to use a specific tool. It is knowing how to break a complex process into steps clear enough that the tool can follow.
The more precisely you decompose, the more work you can delegate.
Even if you have never written a line of code, you can replicate the core of this workflow manually.
Open Reddit. Search for your topic using English keywords. Sort by "Top" and filter to "Past Month." Read the first five posts. Then — this is critical — read the comment sections, not just the posts.
Do not start writing immediately.
First, record three things:
An afternoon's worth of topic ideas often takes fifteen minutes to find this way.
Once you have confirmed that Reddit research produces valuable material for your content, automate it. Build your own Reddit content research skill as a Claude Code Skill that runs the full pipeline. Start simple — even a two-step Skill that searches and summarizes is better than doing everything manually.
If you are new to Claude Code, the complete Claude Code guide covers everything from installation to advanced workflows. The 10 mistakes I made when starting out might save you some time. And if you want to understand how Skills fit into the broader AI tooling stack, this overview of AI stack layers gives useful context.
Yes. Start with the manual version described above: search Reddit with English keywords, sort by Top from the past month, read the top five threads and their comment sections. Record recurring problems, high-upvote quotes, and disagreements. Once you confirm this workflow produces valuable content ideas, automate it with a Claude Code Skill or any agent framework.
Searching, deduplicating, and sorting are deterministic operations. They need stability, not creativity. Scripts execute these steps faster, cheaper, and more reliably than a language model. Reserve the AI for tasks requiring judgment — filtering irrelevant posts, extracting content angles, adapting writing style for different platforms.
A scraper collects data. This Skill runs a complete pipeline: scraping, deduplication, ranking, smart comment sampling, AI-driven angle extraction, and multi-platform content production. The output is not raw data but structured, platform-ready content grounded in real community discussions.
In a test run on the topic "Claude Code Skill," the pipeline collected 125 candidate posts, deduplicated and ranked them, deep-analyzed the top 10, extracted 8 unique content angles, and produced 24 platform-ready pieces. Total time was approximately 10 minutes.
— Leo
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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.