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.

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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.

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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

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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.

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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

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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

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Common Operation Flow

Every step follows the same 6-step pattern:

1. Read JSONL -- Parse the corresponding data file, extract existing unique key set for dedup
2. Read context -- Load relevant reference files (product-context.md, etc.)
3. Execute search/generate -- Use Exa to search Reddit or generate new entries from existing data
4. Dedup-append -- Filter out entries with existing unique keys, append only new lines to JSONL
5. Render Markdown -- Run python3 SCRIPT render <type>; if script unavailable, manually rewrite .md
6. Output statistics -- Report the number of new entries added and current totals

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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:

1. Use wc -l to count lines in each JSONL file
2. Show 4 available steps with aliases
3. 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

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init

Goal: First-time setup. Creates config file and data directory.

Flow:

1. Ask user for data_dir (default: ./reddit-intel/data), product_name, and lang (en/zh)
2. 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
3. Tell user to fill in product-context.md with their product details

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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:

1. Read existing keywords, group by category
2. Read references/step-details.md Step 1 section for detailed logic
3. For each category, use mcp__exa__web_search_exa (type="deep") to search Reddit for new trending terms
4. Generate associations from existing words: synonyms, long-tail variants, Reddit vernacular
5. Dedup by keyword, append new entries
6. 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:

1. Read existing subreddit list
2. Read references/step-details.md Step 2 section for detailed logic
3. Use new keywords via mcp__exa__web_search_exa (type="deep") to discover new communities
4. Newly discovered communities default to tier=4 (observing), upgrade manually later
5. Dedup by name, append new entries
6. Render subreddits.md, display grouped by tier

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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:

1. Read existing post URL set
2. Read references/step-details.md Step 3 section for detailed logic
3. In tier 1/2 subreddits, use mcp__exa__web_search_exa (type="deep") + keywords to search for new posts
4. Filter criteria: within last 30 days, has engagement (upvotes > 10 or comments > 5)
5. Analyze pain category, map to product solution (user-defined pain categories from product-context.md)
6. Dedup by url, append new posts
7. Mark existing posts older than 30 days as archived
8. Render posts.md

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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:

1. Find unanalyzed active posts (post_url not in existing comments.jsonl list)
2. Read references/step-details.md Step 4 section for detailed logic
3. Use mcp__exa__web_search_exa (type="deep") to search post title + subreddit to fetch comment content
4. Extract three types of information: competitor (competitor mentions), language (real user expressions), need (unmet needs)
5. Dedup by post_url + content[:50], append new entries
6. Render comments.md

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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