Helps the user with Buzzabout platform questions — from research setup and credit optimization through AI analysis, narrative tracking, audience segmentation, and competitive intelligence.
Step 1 — Gather context
If references/learnings.md exists, read it first for accumulated platform knowledge.
Ask the user:
-
What area of Buzzabout do you need help with?
- A) Research setup — topics, keywords, language filters
- B) Credit management — optimizing RH usage, understanding overage costs
- C) AI analysis — interpreting sentiment, trends, pain points
- D) Narrative tracking — setting up Slack alerts for topic shifts
- E) Audience segmentation — creating and refining synthetic audiences
- F) Competitive research — reverse-engineering competitor strategies
- G) Integrations — Zapier webhook, Slack alerts, API (Enterprise)
- H) Billing — Pro vs Business vs Enterprise plan differences
- I) Something else — describe it
-
What's your goal? (describe your specific question or problem)
If the user's request already provides most of this context, skip directly to Step 2. Lead with your best-effort answer using reasonable assumptions (stated explicitly), then ask only the most critical 1-2 clarifying questions at the end.
Step 2 — Route or answer directly
If the request maps to a specialized skill, route:
- Social listening strategy or tool comparison →
/sales-social-listening [question] - Audience intelligence (where audiences spend attention) →
/sales-sparktoro [question] - Content intelligence and trending content →
/sales-buzzsumo [question] - Reddit-specific monitoring or lead gen →
/sales-social-listening [question] - Buyer intent signals and prioritization →
/sales-intent [question]
Otherwise, answer directly from the platform reference below.
Step 3 — Buzzabout platform reference
Read references/platform-guide.md for the full platform reference — capabilities, pricing, credit system, integration recipes, and comparison with alternatives.
Answer the user's question using only the relevant section. Don't dump the full reference.
Step 4 — Actionable guidance
You no longer need the platform guide — focus on the user's specific situation.
- Research setup — start with narrow topics, expand after reviewing initial results; use language filters to reduce noise
- Credit optimization — batch related queries; use custom date ranges to avoid scanning irrelevant timeframes; credit rollovers help if usage is uneven month-to-month
- Narrative tracking — set up Slack alerts for emerging narratives so you catch trend shifts early
- Audience segmentation — refine synthetic audiences by cross-referencing with actual customer profiles; segments are starting points, not final targeting
- Competitive research — analyze competitor brand mentions alongside your own; focus on sentiment differences and unmet needs
If you discover a gotcha, workaround, or tip not covered in references/learnings.md, append it there.
Gotchas
Best-effort from research — review these, especially items about plan-gated features and integration gotchas that may be outdated.
- Credit-based pricing is unpredictable. Research Hours (RH) consumption varies by query complexity and data volume. A broad topic scan burns more credits than a narrow one. Monitor usage in the dashboard and set overage alerts ($0.25/RH overage adds up fast).
- API is Enterprise-only. Pro and Business plans have no programmatic access except via the Zapier webhook digest workaround. If you need API access, you must contact sales for Enterprise pricing.
- Zapier webhook is the only automation path on Pro/Business. Insert Zapier's webhook URL as a digest destination to route topic alerts to Slack, email, or other tools. There is no native REST API endpoint.
- Synthetic audiences are AI-generated, not tracked cohorts. They're derived from conversation patterns, not actual user profiles. Useful for messaging direction, not for targeting.
- Data sources are broad but depth varies. Reddit and TikTok analysis is strongest. Instagram, LinkedIn, and X coverage depends on public data access, which platforms restrict periodically.
- Free trial is limited. Evaluate whether the credit allocation covers your research scope before committing to a paid plan.
Related skills
/sales-social-listening— Social listening strategy — monitoring setup, tool comparison, sentiment analysis, competitive intelligence, crisis detection/sales-sparktoro— SparkToro — audience intelligence revealing where audiences spend attention (websites, podcasts, YouTube, subreddits)/sales-buzzsumo— BuzzSumo — content intelligence, trending topics, journalist database, backlinks API/sales-reddinbox— Reddinbox — AI audience intelligence across Reddit, X, HN with intent scoring and market briefs/sales-do— Not sure which skill to use? The router matches any sales objective to the right skill. Install:npx skills add sales-skills/sales --skill sales-do
Examples
Example 1: Optimize credit usage for market research
User says: "I'm burning through my 200 RH in the first week doing competitor research — how do I make credits last?" Skill does:
- Reviews how RH consumption works — broader queries and longer date ranges consume more
- Recommends narrowing date ranges to last 30 days instead of all-time
- Suggests batching related competitor queries into single research sessions
- Notes credit rollovers mean unused credits carry over — consider spreading research across weeks Result: Practical credit optimization strategy to stretch 200 RH across a full month
Example 2: Set up automated content ideas via Zapier
User says: "How do I get Buzzabout to send me trending topic ideas in Slack every morning?" Skill does:
- Explains the Zapier webhook integration — insert webhook URL as digest destination
- Walks through setting up a Zapier workflow: Buzzabout digest → Zapier webhook trigger → OpenAI action (generate content ideas) → Slack message
- Notes this is the only automation path on Pro/Business — no native API
- Suggests daily digest frequency to avoid credit overconsumption Result: Working Zapier automation pipeline from Buzzabout to Slack with AI content generation
Example 3: Compare Buzzabout vs SparkToro for audience research
User says: "Should I use Buzzabout or SparkToro to understand my target audience?" Skill does:
- Explains the fundamental difference: Buzzabout analyzes what audiences say (conversations, sentiment, pain points); SparkToro reveals where audiences spend attention (websites, podcasts, channels)
- Notes Buzzabout is better for understanding motivations and trending narratives; SparkToro is better for channel discovery and ad placement
- Suggests using both if budget allows — SparkToro for finding channels, Buzzabout for understanding what resonates
- Compares pricing: Buzzabout Pro $49/mo vs SparkToro Personal $50/mo Result: Clear comparison based on user's specific research goals
Troubleshooting
AI analysis results feel generic or shallow
Symptom: Buzzabout returns insights that seem obvious or don't match what you see in actual conversations Cause: Topic query is too broad, covering millions of conversations without specificity Solution: Narrow the research topic with specific keywords, competitor names, or product categories. Use language filters if your market is non-English. Try the AI Chat interface to ask follow-up questions that drill into specific themes. Reduce the date range to focus on recent conversations where trends are clearer.
Research credits depleting too fast
Symptom: Running out of 200 RH (Pro) or 600 RH (Business) well before month end Cause: Running broad queries, long date ranges, or too many parallel research sessions Solution: Check credit usage in the dashboard. Batch related queries into fewer sessions. Use custom date ranges (last 30 days) instead of all-time scans. Consider the one-time top-up option for spike months rather than upgrading plans. Credits roll over, so light months offset heavy ones.
Slack alerts aren't arriving for narrative tracking
Symptom: You set up narrative tracking but Slack notifications aren't coming through Cause: Slack integration may not be properly connected, or alert thresholds are set too high Solution: Verify the Slack connection in Buzzabout settings. Check that narrative tracking topics have alert thresholds that match realistic conversation volumes. Test with a known trending topic first to confirm the pipeline works. If using Zapier webhook instead of native Slack, verify the webhook URL is correctly pasted as the digest destination.