| name | reddit-intel |
| description | Reddit competitive intelligence for any product. Discovers keywords, finds relevant
communities, searches for posts with pain points, and analyzes comments to extract
competitor mentions, real user language, and unmet needs.
Runs 4 incremental steps: expand-keywords / expand-subreddits / find-posts /
analyze-comments. Also: init (first-time setup).
Each run: read JSONL → search → dedup-append → re-render Markdown.
Requires: Exa MCP server for Reddit search.
Usage: /reddit-intel step-name
|
| allowed-tools | ["Read","Write","Edit","Bash","Glob","Grep","WebSearch","WebFetch","mcp__exa__web_search_exa","AskUserQuestion"] |
| metadata | {"trigger":"When user asks to search Reddit posts, analyze comments, expand keywords, discover communities, or do Reddit competitive intelligence"} |
Reddit Competitive Intelligence (reddit-intel)
Incremental intelligence gathering: each step runs independently, idempotently. Each execution reads existing JSONL data, searches for new entries, dedup-appends, and re-renders display Markdown. Data only grows, never deletes. Accumulates over time.
Bilingual: Set "language": "en" or "zh" in config. All rendered Markdown labels and stats output adapt automatically. The init command picks the matching product-context template.
Prerequisites
- Exa MCP Server: Required for Reddit search. Add Exa MCP to your Claude Code config.
- Config File: Run
/reddit-intel init to create .reddit-intel.json in your project root.
- Product Context: Fill in
product-context.md with your product info after init.
Tool Selection Rules (must follow)
- When searching Reddit content, DO NOT use built-in WebSearch (reddit.com is blocked by Anthropic's crawler, causing empty retries and wasted tokens)
- MUST use
mcp__exa__web_search_exa (type="deep") for Reddit posts and comments
- Non-Reddit web pages can use built-in WebSearch or Exa
- Use WebFetch for specific URLs
- Max 3 query attempts per search intent; skip if no results
- Target: keep tool calls under 30 per step
Configuration
The skill uses .reddit-intel.json in your project root:
{
"data_dir": "./reddit-intel/data",
"product_name": "MyProduct",
"product_context": "./reddit-intel/product-context.md",
"language": "en"
}
Created by init. The script (jsonl_ops.py) locates this by walking up from CWD.
Data Directory
SCRIPT = scripts/jsonl_ops.py (relative to this skill directory)
| JSONL Source | Markdown Display |
|---|
{data_dir}/keywords.jsonl | {data_dir}/keywords.md |
{data_dir}/subreddits.jsonl | {data_dir}/subreddits.md |
{data_dir}/posts.jsonl | {data_dir}/posts.md |
{data_dir}/comments.jsonl | {data_dir}/comments.md |
JSONL Schema
keywords.jsonl (unique key: keyword)
{"keyword": "context loss", "category": "pain", "reddit_usage": "AI keeps forgetting my project context", "source": "r/LocalLLaMA", "added": "2026-02-06"}
category values: pain | tool | scenario | solution
subreddits.jsonl (unique key: name)
{"name": "r/LocalLLaMA", "tier": 1, "members": "451K", "activity": "high", "promo_policy": "discussion OK", "relevance": "primary", "strategy": "Pain-point replies, technical discussions", "added": "2026-02-06"}
tier values: 1 (core) / 2 (high relevance) / 3 (general developer) / 4 (observing)
posts.jsonl (unique key: url)
{"url": "https://www.reddit.com/...", "title": "...", "subreddit": "r/LocalLLaMA", "upvotes": 85, "comments": 47, "post_date": "2025-06-01", "pain_category": "context_loss", "product_solution": "Auto-inject context on each session", "status": "active", "added": "2026-02-06"}
status values: active | archived
comments.jsonl (unique key: post_url + content[:50])
{"post_url": "https://www.reddit.com/...", "type": "competitor", "content": "I use Cursor when I exceed the token limit", "competitor": "Cursor", "sentiment": "positive", "insight": "Cursor used as fallback", "added": "2026-02-06"}
type values: competitor | language | need
Common Operation Flow
Every step follows the same 6-step pattern:
- Read JSONL -- Parse the corresponding data file, extract existing unique key set for dedup
- Read context -- Load relevant reference files (product-context.md, etc.)
- Execute search/generate -- Use Exa to search Reddit or generate new entries from existing data
- Dedup-append -- Filter out entries with existing unique keys, append only new lines to JSONL
- Render Markdown -- Run
python3 SCRIPT render <type>; if script unavailable, manually rewrite .md
- Output statistics -- Report the number of new entries added and current totals
Step Dispatch
Route to the corresponding step based on user arguments.
No arguments
When no step is specified, display current data stats and show menu:
- Use
wc -l to count lines in each JSONL file
- Show 4 available steps with aliases
- Wait for user selection
Output example:
Current data stats:
keywords: 45 entries
subreddits: 53 entries
posts: 36 entries
comments: 31 entries
Available Steps:
1. expand-keywords (kw) -- Discover pain-point keywords from Reddit
2. expand-subreddits (sub) -- Find relevant Reddit communities
3. find-posts (fp) -- Search for posts matching your product's pain points
4. analyze-comments (ac) -- Extract competitors, user language, unmet needs
init
Goal: First-time setup. Creates config file and data directory.
Flow:
- Ask user for
data_dir (default: ./reddit-intel/data), product_name, and lang (en/zh)
- Run
python3 SCRIPT init --data-dir <dir> --product-name <name> --lang <en|zh>
- Template auto-selected:
--lang en → product-context.template.md, --lang zh → product-context.template.zh.md
- Tell user to fill in
product-context.md with their product details
expand-keywords (aliases: keywords, kw)
Goal: Expand keyword library, discover how Reddit users actually describe pain points related to your product domain.
Input: keywords.jsonl + product-context.md (from config)
Flow:
- Read existing keywords, group by category
- Read
references/step-details.md Step 1 section for detailed logic
- For each category, use
mcp__exa__web_search_exa (type="deep") to search Reddit for new trending terms
- Generate associations from existing words: synonyms, long-tail variants, Reddit vernacular
- Dedup by
keyword, append new entries
- Render
keywords.md, display as tables grouped by category
expand-subreddits (aliases: subreddits, sub)
Goal: Discover new relevant Reddit communities.
Input: subreddits.jsonl + keywords.jsonl
Flow:
- Read existing subreddit list
- Read
references/step-details.md Step 2 section for detailed logic
- Use new keywords via
mcp__exa__web_search_exa (type="deep") to discover new communities
- Newly discovered communities default to tier=4 (observing), upgrade manually later
- Dedup by
name, append new entries
- Render
subreddits.md, display grouped by tier
find-posts (aliases: posts, fp)
Goal: Search target communities for posts containing pain points relevant to your product.
Input: posts.jsonl + keywords.jsonl + subreddits.jsonl
Flow:
- Read existing post URL set
- Read
references/step-details.md Step 3 section for detailed logic
- In tier 1/2 subreddits, use
mcp__exa__web_search_exa (type="deep") + keywords to search for new posts
- Filter criteria: within last 30 days, has engagement (upvotes > 10 or comments > 5)
- Analyze pain category, map to product solution (user-defined pain categories from product-context.md)
- Dedup by
url, append new posts
- Mark existing posts older than 30 days as
archived
- Render
posts.md
analyze-comments (aliases: comments, ac)
Goal: Analyze comment sections of active posts, extract competitor info, real user language, and unmet needs.
Input: posts.jsonl (status=active) + comments.jsonl
Flow:
- Find unanalyzed active posts (post_url not in existing comments.jsonl list)
- Read
references/step-details.md Step 4 section for detailed logic
- Use
mcp__exa__web_search_exa (type="deep") to search post title + subreddit to fetch comment content
- Extract three types of information: competitor (competitor mentions), language (real user expressions), need (unmet needs)
- Dedup by
post_url + content[:50], append new entries
- Render
comments.md
Reference Files
| File | Purpose | When to Read |
|---|
references/step-details.md | Detailed execution logic per step | Before executing each step |
| product-context.md (from config) | Product positioning and feature summary | expand-keywords, find-posts |
