Reddit ICP Monitor

If REDDIT_CLIENT_ID is set: Use OAuth for 60 RPM. Fetch a token first:

TOKEN=$(curl -s -X POST \
  -d "grant_type=password&username=$REDDIT_USERNAME&password=$REDDIT_PASSWORD" \
  --user "$REDDIT_CLIENT_ID:$REDDIT_CLIENT_SECRET" \
  -H "User-Agent: varnan-skills/1.0" \
  https://www.reddit.com/api/v1/access_token \
  | python3 -c "import sys,json; print(json.load(sys.stdin)['access_token'])")

Use Authorization: Bearer $TOKEN and https://oauth.reddit.com base URL for all subsequent requests.

Step 2: Load ICP

Check for an existing ICP file:

ls docs/icp.md 2>/dev/null && echo "icp found" || echo "icp missing"

If docs/icp.md exists: Read it. Extract:

• product: one-sentence product description
• pain_points: list of pain point phrases (exact buyer language)
• anti_keywords: phrases that disqualify a post
• subreddits: list of subreddits to search

If docs/icp.md does not exist: Ask these 3 questions. Do not proceed until all 3 are answered:

1. What does your product do? (one sentence)
2. What subreddits does your ICP post in? (comma-separated, e.g. devops, startups, SaaS)
3. What pain point phrases should trigger a match? (3-5 phrases in your buyer's own words, e.g. "onboarding takes forever", "can't see why users churn")

After answers are collected, save to docs/icp.md in the canonical format from references/icp-format.md. Confirm: "ICP saved to docs/icp.md. It will be used automatically in future runs."

Read references/icp-format.md for the canonical format and examples of good vs bad pain point phrases.

Step 3: Search Reddit

For each combination of subreddit and pain point phrase, run one search. Use public endpoints unless OAuth credentials are set.

Without OAuth (public endpoint):

curl -s \
  -H "User-Agent: varnan-skills/1.0" \
  "https://www.reddit.com/r/{SUBREDDIT}/search.json?q={PHRASE}&sort=new&t=week&limit=25&restrict_sr=true" \
  | python3 -c "
import sys, json
d = json.load(sys.stdin)
posts = d.get('data', {}).get('children', [])
for p in posts:
    data = p['data']
    print(json.dumps({
        'id': data['id'],
        'title': data['title'],
        'body': data.get('selftext', '')[:600],
        'url': 'https://reddit.com' + data['permalink'],
        'score': data['score'],
        'comments': data['num_comments'],
        'subreddit': data['subreddit'],
        'created_utc': data['created_utc']
    }))
"

With OAuth: Replace https://www.reddit.com with https://oauth.reddit.com and add -H "Authorization: Bearer $TOKEN".

URL-encode the phrase before inserting into the query string:

ENCODED=$(python3 -c "import urllib.parse, sys; print(urllib.parse.quote(sys.argv[1]))" "{PHRASE}")

Time window defaults:

• Default: t=week (last 7 days)
• User says "today" or "last 24 hours": t=day
• User says "this month": t=month

After all searches:

• Collect all posts into one list
• Deduplicate by post ID. If the same post matches multiple phrases, keep it once and note all matching phrases.
• If a search returns 0 posts: note it and continue. Do not stop.

State the total candidate count before scoring: "Found X candidate posts across Y subreddits."

Step 4: Score Relevance with Gemini

Write all candidate posts and the ICP context to a temp file, then score:

cat > /tmp/reddit-score-request.json << 'ENDJSON'
{
  "system_instruction": {
    "parts": [{
      "text": "You are a GTM analyst identifying Reddit posts where someone is experiencing the exact pain point that a specific product solves. For each post provided, output a JSON object with these fields: id (the post id), score (integer 1-5), reason (one sentence explaining the score). Scoring rubric: 5 = person clearly has the problem AND is actively seeking help or venting frustration about it; 4 = strong signal, problem is present but less explicit; 3 = possible match, uncertain; 2 = tangentially related but not the core pain; 1 = unrelated. Output only a JSON array of score objects. No commentary before or after the array."
    }]
  },
  "contents": [{
    "parts": [{
      "text": "POSTS_AND_ICP_CONTEXT_HERE"
    }]
  }],
  "generationConfig": {
    "temperature": 0.2,
    "maxOutputTokens": 2048
  }
}
ENDJSON
curl -s -X POST \
  "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -d @/tmp/reddit-score-request.json \
  | python3 -c "import sys,json; d=json.load(sys.stdin); print(d['candidates'][0]['content']['parts'][0]['text'])"

Replace POSTS_AND_ICP_CONTEXT_HERE with:

• The product description and pain point phrases from Step 2
• Each candidate post: id, title, body snippet
• Anti-keywords from the ICP (Gemini should score these lower)

Keep only posts with score >= 4 for response drafting.

State: "X posts scored 4 or 5. Drafting replies."

If 0 posts score >= 4: output the zero-results message from Step 7 and stop. Do not draft empty replies.

Step 5: Draft Replies with Gemini

Read references/reply-rules.md before drafting. Determine the post type for each high-scoring post (Mode 1, 2, or 3) and pass the mode to Gemini.

For each post scoring >= 4, draft a reply (maximum 5 replies per session):

cat > /tmp/reddit-reply-request.json << 'ENDJSON'
{
  "system_instruction": {
    "parts": [{
      "text": "You are a practitioner who has personally solved the exact problem being described in this Reddit post. Write a helpful reply that: (1) directly addresses what the person asked or is frustrated about, (2) shares a specific, useful insight from real experience, (3) does NOT mention any product, tool, or company by name unless the post explicitly asks for tool recommendations, (4) is 2-5 sentences, written in plain conversational prose with no headers or bullet lists, (5) optionally ends with a low-pressure invitation to continue the conversation. Do not use marketing language. Do not use these words: game-changer, best-in-class, streamline, transform, revolutionize, leverage, seamless, robust, comprehensive, innovative. Sound like a real person."
    }]
  },
  "contents": [{
    "parts": [{
      "text": "POST_CONTENT_AND_MODE_HERE"
    }]
  }],
  "generationConfig": {
    "temperature": 0.7,
    "maxOutputTokens": 512
  }
}
ENDJSON
curl -s -X POST \
  "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -d @/tmp/reddit-reply-request.json \
  | python3 -c "import sys,json; d=json.load(sys.stdin); print(d['candidates'][0]['content']['parts'][0]['text'])"

Replace POST_CONTENT_AND_MODE_HERE with:

• The full post title and body
• The reply mode (Mode 1: venting/frustration, Mode 2: how-do-you-handle-X, Mode 3: tool recommendation)
• The product description (so Gemini knows what to avoid naming in Mode 1/2)
• If Mode 3: Gemini may name the product AND must name one alternative

Step 6: Self-QA

Run every check and fix violations before presenting:

• No product name in any Mode 1 or Mode 2 reply
• No banned words in any reply: "game-changer", "best-in-class", "streamline", "transform", "revolutionize", "leverage", "seamless", "robust", "innovative"
• Every reply is 2-5 sentences (count and state)
• Each reply directly addresses the specific question or frustration in the post
• All URLs in the output are real Reddit URLs from the search results (not constructed or guessed)
• No more than 5 replies drafted in total
• Mode 3 replies mention at least one alternative (not just the product)
• If any subreddit returned 0 results across all keyword searches, flag it in the output

Fix any violation before presenting.

Step 7: Output and Save

Present the full report:

## Reddit ICP Monitor — [YYYY-MM-DD]

**Subreddits monitored:** r/[subreddit1], r/[subreddit2]
**Time window:** Last 7 days
**Keywords searched:** [phrase1], [phrase2], [phrase3]
**Candidates found:** X posts
**High-signal matches (score 4-5):** Y posts

---

### Match 1 — Score [N]/5
**Post:** [title](url)
**Subreddit:** r/[subreddit] | [N] upvotes | [N] comments
**Signal quote:** "[relevant excerpt from post body]"
**Matched keyword:** [phrase]
**Post type:** Mode [1/2/3]

**Drafted reply:**
[reply text]

**Reply word count:** N words

---

[repeat for each match]

---

**Subreddits with 0 results:** [list any that returned nothing]

If 0 posts scored >= 4: "No high-signal matches found in the last 7 days across [subreddits]. Keywords searched: [list]. Try widening the time window (run again and say 'last month') or broadening your pain point phrases in docs/icp.md."

Save to file:

mkdir -p docs/reddit-intel
OUTFILE="docs/reddit-intel/$(date +%Y-%m-%d).md"
cat > "$OUTFILE" << 'EOF'
REPORT_CONTENT_HERE
EOF
echo "Saved to $OUTFILE"