reddit-research

name

reddit-research

description

Research Reddit communities using Reddit JSON first, then Reddit RSS fallback when JSON/browser routes are blocked or rate-limited. Use this skill whenever the user wants to gather information FROM Reddit — posts, comments, subreddit stats, user demographics, sentiment, complaints, or community analysis. PROACTIVELY activate on any request to investigate, analyze, research, or explore Reddit opinions, discussions, or community feedback — even if the user does not explicitly say 'Reddit research'. Triggers include: 'search Reddit for', 'what does Reddit say about', 'check Reddit posts', 'Reddit sentiment', 'community analysis', 'subreddit investigation', 'user opinions on Reddit', 'Redditで調べて', 'Redditの評判', or any request about understanding audience reactions on Reddit. Works across all languages.

Reddit Research via JSON API + RSS fallback

Reddit's old.reddit.com endpoint often serves raw JSON — no browser, no auth. When JSON returns 403 Blocked, 429 Too Many Requests, or a network-security block page, use Reddit's Atom/RSS feeds instead. This skill provides a structured workflow to mine posts, comments, subreddit stats, and user behavior patterns from any topic.

Why this approach

• reddit.com blocks headless browsers and WebFetch tools
• old.reddit.com/.json sometimes returns clean, parseable data via curl
• www.reddit.com/.../search.rss often keeps working when JSON is blocked
• Works from any terminal — no browser, no API key, no OAuth
• Rate-limit friendly if you keep JSON requests under ~1/second and RSS requests under ~1 per 10-20 seconds

Core workflow

Follow these steps in order. Each step builds on the previous one.

Step 0: Choose JSON or RSS

Try JSON first for structured metadata and comment access. If the response contains an HTML block page, 403, or repeated 429, switch to RSS immediately and do not keep hammering JSON.

JSON smoke test:

curl -L -s -A "topic-research/1.0 (research tool)" \
  "https://old.reddit.com/search.json?q={QUERY}&sort=relevance&t=year&limit=5" | \
  python3 -c '
import sys, json
data = sys.stdin.read()
try:
    j = json.loads(data)
    print("JSON_OK", len(j.get("data", {}).get("children", [])))
except Exception:
    print("JSON_FAILED_USE_RSS")
    print(data[:200])
'

RSS fallback smoke test:

curl -L -s -A "topic-research/1.0 (RSS research)" \
  "https://www.reddit.com/r/{subreddit}/search.rss?q={QUERY}&restrict_sr=1&sort=relevance&t=all" | \
  python3 -c '
import sys, xml.etree.ElementTree as ET
root = ET.fromstring(sys.stdin.read())
ns = {"a": "http://www.w3.org/2005/Atom"}
print("RSS_OK", len(root.findall("a:entry", ns)))
'

Step 1: Discover subreddits

Search for the topic across Reddit to find where discussion happens.

curl -s -H "User-Agent: topic-research/1.0 (research tool)" \
  "https://old.reddit.com/search.json?q={QUERY}&sort=relevance&t=year&limit=20" | \
  jq -r '.data.children[]? | "\(.data.subreddit)|\(.data.title)|\(.data.ups)|\(.data.num_comments)"'

Extract unique subreddit names from results. These become your investigation targets.

Also try searching within known communities:

curl -s -H "User-Agent: topic-research/1.0 (research tool)" \
  "https://old.reddit.com/r/{subreddit}/search.json?q={QUERY}&sort=top&t=year&limit=20" | \
  jq -r '.data.children[]? | "\(.data.title)|\(.data.ups)|\(.data.num_comments)|https://old.reddit.com\(.data.permalink)"'

RSS fallback for subreddit search:

python3 - <<'PY'
import urllib.request, urllib.parse, xml.etree.ElementTree as ET, re, html, time

subreddit = '{subreddit}'
query = '{QUERY}'
url = f'https://www.reddit.com/r/{subreddit}/search.rss?' + urllib.parse.urlencode({
    'q': query,
    'restrict_sr': '1',
    'sort': 'relevance',
    't': 'all',
})
req = urllib.request.Request(url, headers={'User-Agent': 'topic-research/1.0 (RSS research)'})
with urllib.request.urlopen(req, timeout=25) as r:
    root = ET.fromstring(r.read())
ns = {'a': 'http://www.w3.org/2005/Atom'}
for entry in root.findall('a:entry', ns)[:10]:
    title = entry.findtext('a:title', default='', namespaces=ns).strip()
    link_el = entry.find('a:link', ns)
    link = link_el.attrib.get('href', '') if link_el is not None else ''
    content = entry.findtext('a:content', default='', namespaces=ns) or ''
    text = html.unescape(re.sub('<[^<]+?>', ' ', content))
    text = re.sub(r'\s+', ' ', text).strip()
    print(f'- {title}\n  {link}\n  {text[:350]}')
PY

Step 2: Get subreddit metadata

For each relevant subreddit, fetch its description and subscriber count:

curl -s -H "User-Agent: topic-research/1.0 (research tool)" \
  "https://old.reddit.com/r/{subreddit}/about.json" | \
  jq '{subscribers: .data.subscribers, created: .data.created_utc, description: (.data.public_description | .[0:500]), display_name: .data.display_name}'

This tells you community size and whether it's worth deep-diving.

Step 3: Fetch hot and new posts

Get both hot (popular) and new (recent) posts for breadth:

Hot posts (what the community values):

curl -s -H "User-Agent: topic-research/1.0 (research tool)" \
  "https://old.reddit.com/r/{subreddit}/hot.json?limit=25" | \
  jq -r '.data.children[]? | "\(.data.title)|\(.data.ups)|\(.data.num_comments)|\(.data.author)|https://old.reddit.com\(.data.permalink)"'

New posts (what's happening right now):

curl -s -H "User-Agent: topic-research/1.0 (research tool)" \
  "https://old.reddit.com/r/{subreddit}/new.json?limit=25" | \
  jq -r '.data.children[]? | "\(.data.title)|\(.data.ups)|\(.data.num_comments)|\(.data.author)|https://old.reddit.com\(.data.permalink)"'

Step 4: Read comment threads

For posts with significant comments (or that seem relevant), fetch the full thread:

curl -s -H "User-Agent: topic-research/1.0 (research tool)" \
  "https://old.reddit.com/r/{subreddit}/comments/{post_id}/{slug}.json?limit=20" | \
  jq -r '.[1].data.children[]? | "\(.data.author)|\(.data.body|gsub("\n";" ")|.[0:200])|\(.data.ups)"'

The URL format uses the post's permalink — extract post_id and slug from Step 3 results.

Note: .[0] contains the original post, .[1] contains the comments. Always use index [1] for comments.

RSS fallback for a known thread:

If JSON comments are blocked, try the thread RSS. It often returns the original post and recent/top-level feed content, but may not contain the same depth as JSON.

curl -L -s -A "topic-research/1.0 (RSS research)" \
  "https://www.reddit.com/r/{subreddit}/comments/{post_id}/{slug}/.rss"

If thread RSS returns 429, wait 20-60 seconds and continue with search RSS excerpts instead of retrying repeatedly.

Step 5: Categorize and analyze

Based on the data gathered, categorize posts into themes:

Common categories	What they reveal
Bug reports	Quality issues, ALPHA instability
Build/strategy questions	Depth of player engagement
Gacha/loot reactions	Monetization sentiment
Community/drama	Social dynamics, moderation issues
Meta complaints	Balance concerns, game direction
New player questions	Onboarding friction points

Step 6: Analyze user demographics

Extract demographic signals from the data:

From usernames: Number suffixes (name12345), gaming keywords (gamer, pro, rbx), anime references — these indicate age groups.

From comment language: Scan for non-English text to estimate geographic distribution. Common patterns:

• Spanish: el, los, por, que, juego
• Portuguese: eu, não, muito, jogo, brasil
• French: je, pas, pour, jeu, suis
• Filipino: ng, ang, sa, ako, naman
• Arabic characters, Thai characters, etc.

From slang: Map to generational cohorts:

• Gen Alpha (8-14): skibidi, sigma, rizz, aura
• Gen Z (10-25): bro, fr, ngl, fire, lit, cap, slay
• Both: cooked, goat, W

From post content: School references, parent mentions, spending language — these triangulate age and purchasing power.

Step 7: Synthesize findings

Structure the output as:

1. Community overview — subreddit size, activity level, creation date
2. Post categorization — breakdown by theme with representative examples
3. User persona insights — age range, geography, engagement depth
4. Sentiment analysis — what users love, hate, and want
5. Key quotes — representative comments that illustrate each finding
6. Cross-reference signals — how Reddit data confirms or contradicts other sources

Important notes

• Always set a descriptive User-Agent header — Reddit blocks generic/default agents
• Keep JSON requests to ~1 per second to avoid rate limiting
• Keep RSS requests slower, around 1 request per 10-20 seconds; Reddit returns 429 quickly
• old.reddit.com/.json is the JSON route; www.reddit.com/.../.rss and www.reddit.com/.../search.rss are the fallback route
• The .json suffix on any old.reddit.com URL returns the JSON version
• The .rss suffix or search.rss endpoint returns Atom/RSS XML that can be parsed with Python xml.etree.ElementTree
• For search, sort can be relevance, top, new, or hot
• t (time range) can be hour, day, week, month, year, or all
• Some subreddits are private or quarantined — these will return errors, just skip them
• Comment depth is limited to top-level by default; use depth=N parameter for nesting control
• Be transparent in the report when findings come from RSS excerpts rather than full comment threads