AI Social Listening Agent
Get listening agent spec - just enter keywords, platforms, alerts.
How It Works
Tell it your keywords, platforms and alerts. This generates a listening agent spec the way an experienced automation strategist would build it - a real, usable deliverable, not a generic checklist. The output follows the standard: listening-agent spec: query sets, classification model, alert rules, routing matrix, reporting cadence. Replace every [[token]] with your specifics and it is ready to implement.
What to Provide
| Input | What to enter |
|---|---|
| Keywords | brand name, product name, top 3 competitors |
| Platforms | Instagram, X, Reddit |
| Alerts | Slack #brand-alerts, email digest at 9am |
AI Social Listening Agent
This is the finished deliverable.
1. Keyword/boolean and brand/competitor/topic queries per platform
Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for keyword/boolean and brand/competitor/topic queries per platform]]. Base it on your keywords and adjust as real cases come in.
Signal of expertise this section should show: Share-of-voice and net-sentiment metrics.
Mistake this guards against: Just counting mentions with no sentiment or routing.
2. Sentiment and emotion classification
Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for sentiment and emotion classification]]. Base it on your keywords and adjust as real cases come in.
Signal of expertise this section should show: crisis-spike velocity alerting.
Mistake this guards against: No crisis threshold.
3. Spike/anomaly and crisis-alert thresholds
Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for spike/anomaly and crisis-alert thresholds]]. Base it on your keywords and adjust as real cases come in.
Signal of expertise this section should show: intent classification for routing.
Mistake this guards against: Ignoring platform ToS/API limits.
4. Routing and SLA
Cover each of these explicitly rather than leaving them implied: [[sales lead]], [[support issue]], [[PR risk]]. Define [[the specific rule or default for sales lead]] so nothing is left to guesswork.
Signal of expertise this section should show: platform-API/ToS and PII constraints.
Mistake this guards against: Vanity volume over actionable signal.
5. Influencer/share-of-voice tracking
Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for influencer/share-of-voice tracking]]. Base it on your keywords and adjust as real cases come in.
Signal of expertise this section should show: Share-of-voice and net-sentiment metrics.
Mistake this guards against: Just counting mentions with no sentiment or routing.
6. Data sources and API limits
Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for data sources and api limits]]. Base it on your keywords and adjust as real cases come in.
Signal of expertise this section should show: crisis-spike velocity alerting.
Mistake this guards against: No crisis threshold.
7. Tagging taxonomy
Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for tagging taxonomy]]. Base it on your keywords and adjust as real cases come in.
Signal of expertise this section should show: intent classification for routing.
Mistake this guards against: Ignoring platform ToS/API limits.
8. Reporting digest format and cadence
Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for reporting digest format and cadence]]. Base it on your keywords and adjust as real cases come in.
Signal of expertise this section should show: platform-API/ToS and PII constraints.
Mistake this guards against: Vanity volume over actionable signal.
9. Privacy/ToS compliance for scraping
Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for privacy/tos compliance for scraping]]. Base it on your keywords and adjust as real cases come in.
Signal of expertise this section should show: Share-of-voice and net-sentiment metrics.
Mistake this guards against: Just counting mentions with no sentiment or routing.
Worked Examples
Example 1 - Consumer packaged goods brand
Inputs: Brand + 3 competitors, Instagram/X/Reddit, Slack alerting
Result: Crisis-spike alert caught a viral complaint thread 40 minutes after it started, before it trended.
Example 2 - SaaS company tracking competitor moves
Inputs: Product name + category keywords, weekly digest
Result: Share-of-voice tracking surfaced a competitor pricing change 2 days before it hit press.
Format Checklist
| Element | What good looks like |
|---|---|
| Keyword/boolean and brand/competitor/topic queries per platform | Specific and filled in, not left as a placeholder or generic label |
| Sentiment and emotion classification | Specific and filled in, not left as a placeholder or generic label |
| Spike/anomaly and crisis-alert thresholds | Specific and filled in, not left as a placeholder or generic label |
| Routing | Specific and filled in, not left as a placeholder or generic label |
| Influencer/share-of-voice tracking | Specific and filled in, not left as a placeholder or generic label |
| Data sources and API limits | Specific and filled in, not left as a placeholder or generic label |
| Tagging taxonomy | Specific and filled in, not left as a placeholder or generic label |
| Reporting digest format and cadence | Specific and filled in, not left as a placeholder or generic label |
| Privacy/ToS compliance for scraping | Specific and filled in, not left as a placeholder or generic label |
Common Mistakes to Avoid
- Just counting mentions with no sentiment or routing.
- No crisis threshold.
- Ignoring platform ToS/API limits.
- Vanity volume over actionable signal.
Next Steps After You Generate This
Week 1: pilot with a small internal group or a single channel/segment. Week 2: review real output against the format checklist below and fix the top 2-3 gaps. Weeks 3-4: expand scope and set a recurring review cadence so the workflow stays accurate as your data and process change.
This deliverable gives you a working starting point on day one - keep the [[tokens]] current as your process, tools, and volume change.
Illustrative preview - your actual result is built from your inputs.
How it works.
AI Social Listening Agent: provide keywords, platforms, alerts and get a complete listening agent spec in minutes - including capture, sentiment, alerting. Free AI workflow, no signup required to preview.
Get your listening agent spec
Listening-agent spec: query sets, classification model, alert rules, routing matrix, reporting cadence.
What good looks like.
What it must include
- 01Keyword/boolean and brand/competitor/topic queries per platform
- 02sentiment and emotion classification
- 03spike/anomaly and crisis-alert thresholds
- 04routing (sales lead, support issue, PR risk) and SLA
- 05influencer/share-of-voice tracking
- 06data sources and API limits
- 07tagging taxonomy
- 08reporting digest format and cadence
- 09privacy/ToS compliance for scraping
Signals of expertise
- ★Share-of-voice and net-sentiment metrics
- ★crisis-spike velocity alerting
- ★intent classification for routing
- ★platform-API/ToS and PII constraints
Common mistakes
- ×Just counting mentions with no sentiment or routing
- ×no crisis threshold
- ×ignoring platform ToS/API limits
- ×vanity volume over actionable signal
Frequently asked.
Is the AI Social Listening Agent free to use?
Yes. You can generate a full a listening agent spec for free with no signup and no credit card. An account is only needed if you want to save the result or download it later.
What do I need to provide to ai social listening agent?
3 fields: Keywords, Platforms, Alerts. Each field has an example placeholder shown in the form, so you always have a model answer to work from even if you're not sure what to type.
How long does it take?
Most people get a finished a listening agent spec in under five minutes: fill in the inputs, generate, then copy the result into ChatGPT, Claude, or Gemini. Most users reach an 80–90% ready result within 1–3 passes.
Which AI model does it work with?
The output is a portable prompt and template - it works with GPT, Claude, Gemini, or Perplexity. You paste it into whichever model you already use; nothing is locked to one vendor.
What makes a good a listening agent spec?
It should include: Keyword/boolean and brand/competitor/topic queries per platform; sentiment and emotion classification; spike/anomaly and crisis-alert thresholds; routing (sales lead, support issue, PR risk) and SLA; and more. The tool is pre-loaded with these criteria so the generated draft already covers them.
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