This guide shows how to build a system that monitors high-intent posts and responds automatically using Clawdbot.
You'll build something that detects real buying signals, filters noise, generates contextual replies, and avoids platform bans.
This assumes Clawdbot is already running locally. Set it up first if you haven't.
Step 1: Define What Real Buying Intent Looks Like
Not every post that mentions your category is intent. Most aren't. The ones that matter look like this:
These indicate evaluation or switching behavior — someone is actively looking, not casually browsing.
X search results for "alternative to [competitor]"
Reddit thread asking for "best tool for [category]"
Inside Clawdbot, create a monitoring agent and define keyword patterns around:
Competitor names
Category terms
Switching language (moving from, replacing, alternative to)
Pain-based queries
Clawdbot dashboard → Create new monitoring agent
Keep the list tight. Precision beats volume.
Step 2: Filter Noise Before Replying
Most automation fails because it replies to everything. Platforms notice. Users notice.
Before generating a reply, the agent should:
| Check | Pass condition |
|---|---|
| Read the full post | Contains explicit tool-seeking language |
| Summarize context | Clear pain point or switch trigger present |
| Score intent | Above minimum threshold (e.g. 7/10) |
| Check product fit | Your product addresses the stated need |
If the product doesn't clearly fit, skip it. Add guardrails:
Minimum intent threshold
Skip general discussion threads
Ignore posts without explicit tool-seeking language
Detected post inside Clawdbot with context summary + intent score
Agent configuration panel showing filters / thresholds
The goal is qualified visibility, not volume.
Step 3: Generate Contextual Replies — Not Pitches
The reply structure matters more than the monitoring. Bad automation is obvious. It hard sells, drops links immediately, sounds generic.
Use this structure instead:
Acknowledge the situation
Mention 2–3 relevant tools (including competitors)
Position yours naturally
Offer clarification
Draft reply generated inside Clawdbot
If someone removes your brand name, the comment should still make sense. That's the standard.
Step 4: Control Automation to Avoid Bans
Platforms penalize unnatural behavior. The patterns are obvious once you know what they flag.
| Rule | Setting |
|---|---|
| Daily reply cap | 100–150 max |
| Delay between replies | Randomized 2–7 minutes |
| Phrasing | Varied — no repeated templates |
| Account warmup | Gradual — don't start at full volume |
| Links in first reply | Avoid — earn trust first |
Agent settings showing reply cap + random delay configuration
Toggle showing "Manual approval" enabled
Start with manual approval mode. Scale only after confirming quality.
Step 5: Measure the Right Metrics
Don't optimize for impressions. Impressions don't pay.
Profile visits
People investigating further
Website clicks
Traffic from replies
Demo bookings
Direct pipeline from replies
Revenue
Closed deals attributable to captures
X analytics showing reply impressions + profile visits
Website analytics showing traffic from social
Reddit and Quora replies often rank in Google. That creates long-term traffic from the same reply.
Google search results showing Reddit thread ranking
Step 6: Improve the System Weekly
Once running, it needs weekly attention. Not much — but consistent.
Remove low-converting keywords
Add competitor-specific phrases that are performing
Adjust intent threshold up or down based on reply quality
Improve reply templates based on what gets engagement
Keyword list inside agent configuration
Updated reply template inside workflow editor
This becomes a daily demand capture engine.
It surfaces active buyers. It feeds pipeline. It compounds quietly.
Next step
Haven't set up Clawdbot yet?
OpenClaw + Telegram + Claude. Takes ~20 minutes.
