fix: correct Gong API endpoints and RB2B integration model

Gong: fix endpoints to POST (not GET), remove non-existent /v2/deals,
use tenant-specific base URL, correct recording limitation, add source
links. RB2B: clarify API Partner Program vs native integrations, add
pricing volatility note and source links. Registry: fix '10k calls'
to '10k API calls'.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

skills/ai-seo/SKILL.md

@@ -2,7 +2,7 @@
 name: ai-seo
 description: "When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' or 'optimize for Claude/Gemini.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema-markup."
 metadata:
-  version: 1.1.0
+  version: 1.2.0
 ---
 
 # AI SEO
@@ -226,6 +226,50 @@ AI systems don't just cite your website — they cite where you appear.
 - Create YouTube content for key how-to queries
 - Answer relevant Quora questions with depth
 
+### Machine-Readable Files for AI Agents
+
+AI agents aren't just answering questions — they're becoming buyers. When an AI agent evaluates tools on behalf of a user, it needs structured, parseable information. If your pricing is locked in a JavaScript-rendered page or a "contact sales" wall, agents will skip you and recommend competitors whose information they can actually read.
+
+Add these machine-readable files to your site root:
+
+**`/pricing.md` or `/pricing.txt`** — Structured pricing data for AI agents
+
+```markdown
+# Pricing — [Your Product Name]
+
+## Free
+- Price: $0/month
+- Limits: 100 emails/month, 1 user
+- Features: Basic templates, API access
+
+## Pro
+- Price: $29/month (billed annually) | $35/month (billed monthly)
+- Limits: 10,000 emails/month, 5 users
+- Features: Custom domains, analytics, priority support
+
+## Enterprise
+- Price: Custom — contact sales@example.com
+- Limits: Unlimited emails, unlimited users
+- Features: SSO, SLA, dedicated account manager
+```
+
+**Why this matters now:**
+- AI agents increasingly compare products programmatically before a human ever visits your site
+- Opaque pricing gets filtered out of AI-mediated buying journeys
+- A simple markdown file is trivially parseable by any LLM — no rendering, no JavaScript, no login walls
+- Same principle as `robots.txt` (for crawlers), `llms.txt` (for AI context), and `AGENTS.md` (for agent capabilities)
+
+**Best practices:**
+- Use consistent units (monthly vs. annual, per-seat vs. flat)
+- Include specific limits and thresholds, not just feature names
+- List what's included at each tier, not just what's different
+- Keep it updated — stale pricing is worse than no file
+- Link to it from your sitemap and main pricing page
+
+**`/llms.txt`** — Context file for AI systems (see [llmstxt.org](https://llmstxt.org))
+
+If you don't have one yet, add an `llms.txt` that gives AI systems a quick overview of what your product does, who it's for, and links to key pages (including your pricing).
+
 ### Schema Markup for AI
 
 Structured data helps AI systems understand your content. Key schemas:
@@ -309,7 +353,7 @@ Monthly manual check:
 - Feature comparison tables (you vs. category, not just competitors)
 - Specific metrics ("processes 10,000 transactions/sec" not "blazing fast")
 - Customer count or social proof with numbers
-- Pricing transparency (AI cites pages with visible pricing)
+- Pricing transparency (AI cites pages with visible pricing) — add a `/pricing.md` file so AI agents can parse your plans without rendering your page (see "Machine-Readable Files" above)
 - FAQ section addressing common buyer questions
 
 ### Blog Content
@@ -358,6 +402,7 @@ Monthly manual check:
 - **Ignoring third-party presence** — You may get more AI citations from a Wikipedia mention than from your own blog
 - **No structured data** — Schema markup gives AI systems structured context about your content
 - **Keyword stuffing** — Unlike traditional SEO where it's just ineffective, keyword stuffing actively reduces AI visibility by 10% (Princeton GEO study)
+- **Hiding pricing behind "contact sales" or JS-rendered pages** — AI agents evaluating your product on behalf of buyers can't parse what they can't read. Add a `/pricing.md` file
 - **Blocking AI bots** — If GPTBot, PerplexityBot, or ClaudeBot are blocked in robots.txt, those platforms can't cite you
 - **Generic content without data** — "We're the best" won't get cited. "Our customers see 3x improvement in [metric]" will
 - **Forgetting to monitor** — You can't improve what you don't measure. Check AI visibility monthly at minimum

skills/customer-research/SKILL.md

@@ -121,17 +121,18 @@ Choose sources based on your ICP type — then read `references/source-guides.md
 
 | ICP Type | Primary Sources |
 |----------|----------------|
-| B2B SaaS / technical buyers | Reddit (role-specific subs), G2/Capterra, Hacker News, LinkedIn, Indie Hackers |
-| SMB / founders | Reddit (r/entrepreneur, r/smallbusiness), Indie Hackers, Product Hunt, Facebook Groups |
+| B2B SaaS / technical buyers | Reddit (role-specific subs), G2/Capterra, Hacker News, LinkedIn, Indie Hackers, SparkToro |
+| SMB / founders | Reddit (r/entrepreneur, r/smallbusiness), Indie Hackers, Product Hunt, Facebook Groups, SparkToro |
 | Developer / DevOps | r/devops, r/programming, Hacker News, Stack Overflow, Discord servers |
 | B2C / consumer | App store reviews (1-3 star), Reddit hobby/lifestyle subs, YouTube comments, TikTok/Instagram comments |
-| Enterprise | LinkedIn, industry analyst reports, G2 Enterprise filter, job postings |
+| Enterprise | LinkedIn, industry analyst reports, G2 Enterprise filter, job postings, SparkToro |
 
 **Quick decision guide:**
 - Have a product category? → Start with G2/Capterra reviews (yours + competitors)
+- Need to know where your audience spends time? → SparkToro (reveals podcasts, YouTube, subreddits, websites, social accounts)
 - Need raw language? → Reddit and YouTube comments
 - Need trigger events? → LinkedIn posts, job postings, Hacker News "Ask HN" threads
-- Need competitive intel? → Competitor 4-star reviews on G2; Product Hunt discussions
+- Need competitive intel? → Competitor 4-star reviews on G2; Product Hunt discussions; SparkToro competitor audience analysis
 
 ### What to Extract from Each Source
 

skills/customer-research/references/source-guides.md

@@ -282,6 +282,61 @@ Comments on review videos are especially valuable — these are people actively
 
 ---
 
+## SparkToro (Audience Intelligence)
+
+SparkToro is a behavioral audience research tool. Instead of mining individual posts and comments, it aggregates clickstream, search, and social data to show what your audience does at scale — what they read, watch, listen to, follow, and search for.
+
+### When to Use SparkToro vs. Manual Research
+
+- **SparkToro first** when you need to understand where your ICP spends time, what content they consume, and which influencers they follow — it answers these questions in seconds with aggregated data
+- **Manual research first** (Reddit, G2, communities) when you need raw language, exact quotes, emotional context, and the "why" behind behavior
+- **Best together**: Use SparkToro to identify which podcasts, subreddits, and websites matter, then go mine those sources manually for voice-of-customer language
+
+### Key Queries to Run
+
+**By competitor:**
+- "People who follow @competitor" — reveals shared audience affinities
+- "People who visit competitor.com" — shows what else they consume
+
+**By audience description:**
+- "People who frequently talk about [topic]" — finds audience behaviors
+- "People whose bio contains [job title]" — profiles a role-based segment
+
+**By your own audience:**
+- "People who visit yourdomain.com" — understand your actual audience
+- Compare against competitor audience profiles to find gaps
+
+### What to Extract
+
+| Data Type | What It Tells You | Use It For |
+|-----------|------------------|------------|
+| Top websites visited | Where your audience reads | Content partnerships, guest posting targets |
+| Top podcasts | What they listen to | Podcast guesting, sponsorship decisions |
+| Top YouTube channels | What they watch | Video content strategy, ad placements |
+| Top subreddits | Where they discuss | Community participation, Reddit ad targeting |
+| Search keywords | What they Google | SEO and content topic planning |
+| AI prompt topics | What they ask AI tools | Emerging content opportunities |
+| Social accounts followed | Who influences them | Influencer partnerships, co-marketing |
+| Demographics | Who they are | Persona building, ad targeting |
+
+### Source Weighting
+
+SparkToro data is aggregated and anonymized — it shows patterns, not individual opinions. Treat it as:
+- **High confidence** for behavioral data (what they visit, follow, search for)
+- **Medium confidence** for demographic data (self-reported, may be incomplete)
+- **Not a substitute** for qualitative research (doesn't capture language, emotions, or the "why")
+
+### Limitations
+
+- Free tier: 5 reports/month, shallow results (top 5–10)
+- No public API — all research done through web interface
+- Skews English-language, US-centric
+- Shows what audiences do, not why — pair with qualitative sources
+
+See [tools/integrations/sparktoro.md](../../../tools/integrations/sparktoro.md) for full tool details and pricing.
+
+---
+
 ## Organizing Your Research
 
 Use a simple tagging system across all sources:
@@ -319,6 +374,7 @@ Not all sources carry equal weight. Use this guide when assigning confidence lab
 | Survey (multiple choice) | Low-medium | Artifacts of the options you provided |
 | NPS verbatims | Medium | Correlates with score; prompted by the survey moment |
 | YouTube/TikTok comments | Medium | Skews toward engaged viewers; social performance |
+| SparkToro audience data | Medium-high | Aggregated behavioral data; strong for "what" but not "why" |
 | Job postings | Low-medium | Aspirational, not necessarily reflective of current pain |
 
 ### Confidence Labels in Practice

tools/REGISTRY.md

@@ -74,6 +74,9 @@ Quick reference for AI agents to discover tool capabilities and integration meth
 | pendo | Product Analytics | ✓ | - | [✓](clis/pendo.js) | - | [pendo.md](integrations/pendo.md) |
 | similarweb | Competitive Intelligence | ✓ | - | [✓](clis/similarweb.js) | - | [similarweb.md](integrations/similarweb.md) |
 | firehose | Competitive Intelligence | ✓ | - | - | - | [firehose.md](integrations/firehose.md) |
+| sparktoro | Audience Research | - | - | - | - | [sparktoro.md](integrations/sparktoro.md) |
+| rb2b | Visitor Identification | ✓ | - | - | - | [rb2b.md](integrations/rb2b.md) |
+| gong | Revenue Intelligence | ✓ | - | - | - | [gong.md](integrations/gong.md) |
 | airops | AI Content | ✓ | - | [✓](clis/airops.js) | - | [airops.md](integrations/airops.md) |
 | buffer | Social | ✓ | - | [✓](clis/buffer.js) | - | [buffer.md](integrations/buffer.md) |
 | wistia | Video | ✓ | - | [✓](clis/wistia.js) | - | [wistia.md](integrations/wistia.md) |
@@ -340,6 +343,36 @@ Traffic analytics, competitor benchmarking, and market research.
 
 **Agent recommendation**: Similarweb for competitor traffic analysis and market benchmarking.
 
+### Audience Research
+
+Audience intelligence and behavioral research tools.
+
+| Tool | Best For | Notes |
+|------|----------|-------|
+| **sparktoro** | Audience affinities, behavioral data | Clickstream + social data |
+
+**Agent recommendation**: SparkToro for discovering where your ICP spends time — what they read, watch, listen to, follow, and search for. Essential for customer research, content strategy, and media buying decisions.
+
+### Visitor Identification
+
+Website visitor de-anonymization for B2B sales and marketing.
+
+| Tool | Best For | Notes |
+|------|----------|-------|
+| **rb2b** | Person-level visitor ID, intent signals | LinkedIn profiles, emails, page-level data |
+
+**Agent recommendation**: RB2B for identifying anonymous B2B website visitors and routing high-intent visitors to outreach tools. Pairs well with Clay for enrichment and Instantly/Lemlist for cold email.
+
+### Revenue Intelligence
+
+Sales conversation analytics, call recording, and deal intelligence.
+
+| Tool | Best For | Notes |
+|------|----------|-------|
+| **gong** | Call recording, transcript analysis, deal insights | REST API, 10k API calls/day |
+
+**Agent recommendation**: Gong for mining sales call transcripts for customer research, competitive intelligence, and coaching insights. Essential for revenue attribution and win/loss analysis.
+
 ### AI Content
 
 AI-powered content generation and optimization platforms.

tools/integrations/gong.md

@@ -0,0 +1,144 @@
+# Gong
+
+Revenue intelligence platform that records, transcribes, and analyzes sales conversations (calls, video meetings, emails) to surface deal insights, coaching opportunities, and competitive intelligence.
+
+## Capabilities
+
+| Integration | Available | Notes |
+|-------------|-----------|-------|
+| API | ✓ | REST API, Basic Auth or OAuth2 |
+| MCP | - | Not available |
+| CLI | - | Not available |
+| SDK | - | REST API only; community Python client via dltHub |
+
+## Authentication
+
+- **Type**: Basic Auth (Access Key + Secret) or OAuth2 for published apps
+- **Get key**: Admin-only — Settings > API at https://app.gong.io
+- **Base URL**: Tenant-specific — retrieve from your Gong API settings (typically `https://{tenant}.api.gong.io/v2/`)
+- **Docs**: https://help.gong.io/docs/what-the-gong-api-provides
+
+## API Endpoints
+
+Most Gong API endpoints use **POST** with JSON request bodies for filtering. Check the [official API docs](https://gong.app.gong.io/settings/api/documentation) for current endpoint availability.
+
+### Calls
+
+```bash
+# List calls (with date/user filters)
+POST /v2/calls/extensive
+
+# Get call transcripts (batch, by call IDs)
+POST /v2/calls/transcript
+```
+
+### Users & Stats
+
+```bash
+# List users
+GET /v2/users
+
+# Get activity stats (talk ratio, questions asked, longest monologue)
+POST /v2/stats/activity/day-by-day
+```
+
+### Engagement Flows
+
+```bash
+# List flows
+GET /v2/flows
+
+# Get flow analytics
+GET /v2/flows/{id}/analytics
+```
+
+## Key Data Points
+
+### Per Call
+- Full transcript with speaker labels and timestamps
+- Talk-to-listen ratio per participant
+- Topics discussed (auto-detected)
+- Questions asked (count and content)
+- Longest monologue duration
+- Next steps mentioned
+- Competitor mentions
+- Pricing discussions flagged
+
+### Per Deal
+- All associated calls and emails
+- Deal stage progression
+- Risk signals (gone dark, competitor mentioned, champion left)
+- Engagement score
+
+### Per Rep
+- Talk ratio trends
+- Question frequency
+- Topic coverage vs. playbook
+- Win rate correlation with behaviors
+
+## Common Agent Operations
+
+### Extract Competitive Intelligence from Calls
+
+1. Query calls mentioning competitor names
+2. Extract: objections raised, features compared, pricing discussed
+3. Synthesize into competitive battlecard updates
+4. Track competitor mention frequency over time
+
+### Mine Calls for Customer Research
+
+1. Pull transcripts from recent won/lost deals
+2. Extract: pain points, trigger events, decision criteria, language used
+3. Feed into persona building and messaging work
+4. Identify recurring objections for sales enablement
+
+### Revenue Attribution
+
+1. Pull call data alongside CRM deal data
+2. Map which content/pages were discussed in winning deals
+3. Identify which talking points correlate with closed-won
+4. Build content-to-revenue attribution reports
+
+### Rep Coaching Insights
+
+1. Compare top performer call patterns vs. team average
+2. Identify: talk ratio, question frequency, topic coverage gaps
+3. Surface specific call moments for coaching review
+4. Track improvement over time
+
+## Rate Limits
+
+- 3 API calls per second
+- 10,000 API calls per day
+- Pagination required for large result sets
+
+## When to Use
+
+- Mining sales call transcripts for customer research and VOC data
+- Extracting competitive intelligence from prospect conversations
+- Building revenue attribution models (content → deal influence)
+- Analyzing win/loss patterns across deal transcripts
+- Coaching sales reps based on conversation analytics
+- Identifying common objections and buying signals
+
+## Limitations
+
+- API access requires admin credentials
+- Transcript quality depends on call audio quality
+- Rate limits (10k/day) may constrain large-scale analysis
+- Pricing is enterprise-level (not publicly listed, typically $100+/user/month)
+- Requires team adoption — records calls via integrations, but also supports uploading calls from non-integrated telephony systems
+
+## Relevant Skills
+
+- customer-research
+- sales-enablement
+- competitor-alternatives
+- revops
+- cold-email
+
+## Sources
+
+- [Gong API overview](https://help.gong.io/docs/what-the-gong-api-provides)
+- [Gong API documentation](https://gong.app.gong.io/settings/api/documentation)
+- [Call upload support](https://help.gong.io/docs/uploading-calls-from-a-non-integrated-telephony-system)

tools/integrations/rb2b.md

@@ -0,0 +1,110 @@
+# RB2B
+
+Website visitor identification platform that de-anonymizes B2B website traffic, revealing the individual people visiting your site with LinkedIn profiles, emails, and company data.
+
+## Capabilities
+
+| Integration | Available | Notes |
+|-------------|-----------|-------|
+| API | Limited | API Partner Program (separate from standard app) |
+| MCP | - | Not available |
+| CLI | - | Not available |
+| SDK | - | Not available |
+
+Most teams use RB2B via its native integrations (Slack, CRM push, Zapier, webhooks) rather than direct API access. A separate [API Partner Program](https://www.rb2b.com/apis) exists for programmatic access.
+
+## Authentication
+
+- **Type**: Native integrations (no API key needed for standard use)
+- **API Partner Program**: Separate credentials via https://www.rb2b.com/apis
+- **Free tier**: Limited credits/month with Slack alerts
+
+## Pricing Tiers
+
+Pricing changes frequently — verify at https://www.rb2b.com/pricing.
+
+| Plan | Approx. Price | Key Features |
+|------|--------------|-------------|
+| Free | $0 | Limited credits, Slack alerts, LinkedIn profiles |
+| Starter | ~$79/mo | Person-level ID, basic integrations |
+| Pro | ~$129-349/mo | CSV export, CRM push, validated emails |
+| Pro+ | ~$299+/mo | All integrations, higher credit volume |
+
+## Key Integrations
+
+RB2B pushes identified visitor data to 50+ tools:
+- **CRM**: Salesforce, HubSpot
+- **Outreach**: Instantly, HeyReach, Lemlist
+- **Enrichment**: Clay, Apollo, Clearbit
+- **Automation**: Zapier, Make
+- **Alerts**: Slack (real-time notifications)
+
+## What RB2B Reveals Per Visitor
+
+- Full name and LinkedIn profile URL
+- Job title and company
+- Validated business email (Pro+)
+- Pages visited and visit duration
+- Number of visits and return frequency
+- Company data (size, industry, location)
+
+## Common Agent Operations
+
+### Real-Time Visitor Alerts
+
+Configure Slack alerts for high-intent visitors:
+- Visitors who hit pricing page
+- Visitors who return 3+ times
+- Visitors from target account list
+- Visitors matching ICP job titles
+
+### Visitor-to-Outreach Pipeline
+
+1. RB2B identifies visitor with LinkedIn + email
+2. Filter by ICP criteria (title, company size, pages visited)
+3. Route to outreach tool (Instantly, Lemlist) or CRM (HubSpot, Salesforce)
+4. Trigger personalized cold email referencing pages they visited
+
+### Intent Scoring
+
+Score visitors by behavior signals:
+- **High intent**: Pricing page, demo page, comparison pages, 3+ visits
+- **Medium intent**: Feature pages, case studies, 2 visits
+- **Low intent**: Blog only, single visit, bounced quickly
+
+### Suppression Lists
+
+Prevent outreach to:
+- Existing customers (match against CRM)
+- Active deals in pipeline
+- Competitors and agencies
+- Recently contacted prospects
+
+## When to Use
+
+- Identifying anonymous website visitors for sales outreach
+- Building ABM (account-based marketing) target lists from site traffic
+- Understanding which companies are researching your product
+- Triggering personalized outreach based on page-level intent signals
+- Feeding enrichment tools (Clay, Apollo) with warm visitor data
+
+## Limitations
+
+- Person-level identification works best for US B2B traffic
+- Not all visitors can be identified (typical match rates: 15-30%)
+- Requires sufficient website traffic to be cost-effective
+- Privacy considerations — ensure compliance with applicable regulations
+- Free tier limited to Slack alerts (no CRM push or email export)
+
+## Relevant Skills
+
+- cold-email
+- revops
+- customer-research
+- paid-ads
+
+## Sources
+
+- [RB2B pricing](https://www.rb2b.com/pricing)
+- [RB2B plans comparison](https://support.rb2b.com/en/articles/9173659-rb2b-plans-side-by-side-comparisons)
+- [RB2B API Partner Program](https://support.rb2b.com/en/articles/12579420-rb2b-apis-rb2b-s-api-partner-program)

tools/integrations/sparktoro.md

@@ -0,0 +1,127 @@
+# SparkToro
+
+Audience research platform that reveals what your target audience reads, watches, listens to, follows, and searches for — using clickstream data, Google search data, and public social profiles.
+
+## Capabilities
+
+| Integration | Available | Notes |
+|-------------|-----------|-------|
+| API | - | Not yet public (coming soon) |
+| MCP | - | Not available |
+| CLI | - | Not available |
+| SDK | - | Not available |
+
+SparkToro is primarily a web-based research tool. No public API, CLI, or SDK is currently available. Use the web interface at https://sparktoro.com for all queries.
+
+## Authentication
+
+- **Type**: Account login at https://sparktoro.com
+- **Free tier**: 5 reports/month with limited results
+- **Paid plans**: $50–$300/month with expanded results and exports
+
+## Pricing Tiers
+
+| Plan | Price | Reports/Month | Users | Result Depth |
+|------|-------|---------------|-------|-------------|
+| Free | $0 | 5 | 1 | Top 5–10 results |
+| Personal | $50/mo | 50 | 1 | Top 50 results |
+| Business | $150/mo | 500 | 10 | Top 150 results, contact data, AI advice |
+| Agency | $300/mo | Unlimited | 100 | Top 300 results, full CSV export |
+
+## What SparkToro Reveals
+
+### Audience Behaviors
+- **Websites** they visit and engage with
+- **Podcasts** they listen to
+- **YouTube channels** they watch
+- **Subreddits** they participate in
+- **Social accounts** they follow
+- **Search keywords** they use on Google
+- **AI prompt topics** they ask ChatGPT, Claude, Gemini
+
+### Audience Demographics
+- Gender, age ranges
+- Job titles and roles
+- Industries and skills
+- Education levels
+- Geographic distribution
+- Interests and affinities
+
+### Audience Characteristics
+- Bio descriptions and self-identifiers
+- Language patterns in posts and comments
+- Preferred social networks and platforms
+- E-commerce platforms they use
+
+## Common Agent Operations
+
+Since SparkToro has no API, these are the research workflows agents should guide users through.
+
+### Audience Profile Research
+
+Query SparkToro with phrases like:
+- "People who follow @competitor" — reveals shared audience behaviors
+- "People who visit competitor.com" — shows what else they consume
+- "People who frequently talk about [topic]" — finds audience affinities
+- "People whose bio contains [job title]" — profiles a role-based segment
+
+### Finding Where Your ICP Spends Time
+
+1. Search for your ICP by description, competitor followers, or website visitors
+2. Extract: top websites visited, podcasts listened to, YouTube channels watched, subreddits
+3. Use this to prioritize: guest podcast appearances, content partnerships, ad placements, community participation
+
+### Discovering Content Topics
+
+1. Search your audience segment
+2. Review the "Search Keywords" tab — what they Google
+3. Review the "AI Prompt Topics" tab — what they ask AI tools
+4. Use these to inform content strategy and SEO keyword targeting
+
+### Building Data-Backed Personas
+
+1. Run 3–5 queries for different segments of your audience
+2. Compare demographic breakdowns across segments
+3. Note which behaviors and affinities are shared vs. unique per segment
+4. Export data and build personas grounded in observed behavior, not assumptions
+
+### Competitive Audience Analysis
+
+1. Search "People who follow @competitor" or "People who visit competitor.com"
+2. Compare against your own audience profile
+3. Identify: channels they use that you don't, content they consume that you don't produce, influencers they follow that you haven't engaged
+
+## Data Sources
+
+SparkToro aggregates from three sources:
+- **Clickstream data** — anonymized browsing behavior
+- **Google search results** — search keyword patterns
+- **Public social profiles** — bios, follows, engagement
+
+## When to Use
+
+- Identifying where your ICP spends time online (podcasts, YouTube, subreddits, websites)
+- Finding influencers and social accounts your audience follows
+- Discovering content topics and search keywords your audience cares about
+- Building data-backed personas instead of assumption-based ones
+- Planning podcast guest appearances, sponsorships, or content partnerships
+- Understanding what your competitors' audience looks like
+- Validating audience assumptions with behavioral data
+- Discovering AI prompt topics your audience uses
+
+## Limitations
+
+- No public API — all research is done through the web interface
+- Free tier limited to 5 reports/month with shallow results
+- Data skews toward English-language, US-centric audiences
+- Clickstream data may not capture all niche audiences
+- Cannot track individual users — all data is aggregated and anonymized
+
+## Relevant Skills
+
+- customer-research
+- content-strategy
+- competitor-alternatives
+- paid-ads
+- social-content
+- cold-email