Situation Mining
Gather public data -> infer situations -> generate diagnostic messaging.
IMPORTANT: Situations beat signals. Everyone emails the funded company. You email based on what their data reveals about their worldview.
Data Collection (run in parallel)
Each source maps to a validated process file in ./references/research-processes/. Use the PRIMARY query pattern from each file.
| Source |
Process File |
PRIMARY Query |
What to Gather |
| Company profile |
find-profiles.md |
{{company_name}} {{category}} company overview |
Industry, size, funding, category |
| Founder/CEO |
find-founders.md |
{{company_name}} CEO OR founder interview OR podcast |
Posting frequency/topics, background, worldview |
| Job postings |
find-hiring.md |
{{company_name}} careers |
Hiring SDRs? Engineers? Marketers? NO sales roles? |
| Growth signals |
find-growth-signals.md |
site:{{domain}} blog OR pricing OR newsletter OR demo OR "free trial" OR "book a call" |
Content investment, marketing maturity, lead capture |
| Competitors |
find-competitors.md |
{{company_name}} competitors |
Market position, alternatives, differentiation |
| Negativity |
find-negativity.md |
{{company_name}} {{category}} complaints OR "negative reviews" OR problems OR issues |
Customer pain points, public friction |
Rules:
- Follow stop conditions from each process file - don't waste searches when data is sufficient
- Respect kill lists - never run queries marked in "do not search" sections
- If
{{category}} is unknown, omit it from queries (most PRIMARY patterns work without it)
- For ambiguous company names (Clay, Stripe), category is critical for accuracy
Worldview Inference Patterns
| Data Point |
Inferred Worldview |
Messaging Implication |
| High revenue, low headcount |
Max leverage per person |
Talk efficiency, not hiring |
| Funded but not hiring |
Bootstrap mentality despite capital |
Respect scrappiness |
| CEO posting content |
Believes in founder brand |
"Amplify what you're doing" |
| No outbound team |
Anti-outbound or no fit |
Don't lead with "we do outbound" |
| Heavy blog investment |
Content drives growth |
Adjacent offering angle |
| Using [competitor] |
Category-familiar, has opinions |
Position against friction points |
| Serial founder |
Pattern recognition, hates BS |
Get to the point fast |
| Active hiring in sales |
Growth mode, needs pipeline |
Lead generation angle |
| Negative reviews about support |
Overwhelmed team, scaling pain |
"Handle the volume" angle |
| No competitors found |
Niche/new category |
Category creation messaging |
Adjacent Offering Logic
| Investing In |
Adjacent Offering |
| Content marketing |
Distribution, amplification |
| Paid ads |
Attribution, conversion |
| Sales team |
Enablement, leads |
| Product-led growth |
Expansion revenue, onboarding |
| SEO |
New channels (Reddit, LLM ranking) |
Output Structure
- Raw Data - website, LinkedIn, funding, tech stack, jobs, competitors, negativity findings
- Situations Identified - each with evidence, inferred worldview, messaging implication
- Diagnostic Messages (3-4 options) - each with why it works and what to avoid
- Recommendations - best angle, avoid list, test hypothesis, personalization hook, next actions
Execution Flow
- User provides company name/domain (and optionally category)
- GATHER (parallel): Run 6 validated searches using PRIMARY queries from table above
- INFER: Map data to worldview patterns, identify adjacent offerings, note contradictions
- GENERATE: 3-4 diagnostic messages, explain why each works, flag what to AVOID
- OUTPUT: Full report, offer to save to knowledge base
API Usage
- Serper (primary, ~$0.001/search): 6 validated searches from process files above
- Total cost per prospect: ~$0.006
- WebFetch (backup): when full page content needed beyond search snippets
- Kill-list awareness: never run queries from process file "do not search" sections (e.g.,
site:apollo.io, {{company_name}} annual report, site:youtube.com)
IMPORTANT: Situations are inferences, not facts. Always frame as "Based on public data..." / "This suggests..." / "Test this hypothesis..."
1---2name: situation-mine3description: Mine prospect situations for diagnostic messaging using Stealth Offer framework. USE WHEN user says "situation mine" OR "mine situations" OR "research [company] for outreach" OR "find angles for [company]" OR "diagnostic messaging for".4---56# Situation Mining78Gather public data -> infer situations -> generate diagnostic messaging.910IMPORTANT: Situations beat signals. Everyone emails the funded company. You email based on what their data reveals about their worldview.1112## Data Collection (run in parallel)1314Each source maps to a validated process file in `./references/research-processes/`. Use the PRIMARY query pattern from each file.1516|Source|Process File|PRIMARY Query|What to Gather|17|------|-----------|-------------|-------------|18|Company profile|find-profiles.md|`{{company_name}} {{category}} company overview`|Industry, size, funding, category|19|Founder/CEO|find-founders.md|`{{company_name}} CEO OR founder interview OR podcast`|Posting frequency/topics, background, worldview|20|Job postings|find-hiring.md|`{{company_name}} careers`|Hiring SDRs? Engineers? Marketers? NO sales roles?|21|Growth signals|find-growth-signals.md|`site:{{domain}} blog OR pricing OR newsletter OR demo OR "free trial" OR "book a call"`|Content investment, marketing maturity, lead capture|22|Competitors|find-competitors.md|`{{company_name}} competitors`|Market position, alternatives, differentiation|23|Negativity|find-negativity.md|`{{company_name}} {{category}} complaints OR "negative reviews" OR problems OR issues`|Customer pain points, public friction|2425**Rules:**26- Follow stop conditions from each process file - don't waste searches when data is sufficient27- Respect kill lists - never run queries marked in "do not search" sections28- If `{{category}}` is unknown, omit it from queries (most PRIMARY patterns work without it)29- For ambiguous company names (Clay, Stripe), category is critical for accuracy3031## Worldview Inference Patterns3233|Data Point|Inferred Worldview|Messaging Implication|34|----------|-----------------|---------------------|35|High revenue, low headcount|Max leverage per person|Talk efficiency, not hiring|36|Funded but not hiring|Bootstrap mentality despite capital|Respect scrappiness|37|CEO posting content|Believes in founder brand|"Amplify what you're doing"|38|No outbound team|Anti-outbound or no fit|Don't lead with "we do outbound"|39|Heavy blog investment|Content drives growth|Adjacent offering angle|40|Using [competitor]|Category-familiar, has opinions|Position against friction points|41|Serial founder|Pattern recognition, hates BS|Get to the point fast|42|Active hiring in sales|Growth mode, needs pipeline|Lead generation angle|43|Negative reviews about support|Overwhelmed team, scaling pain|"Handle the volume" angle|44|No competitors found|Niche/new category|Category creation messaging|4546## Adjacent Offering Logic4748|Investing In|Adjacent Offering|49|------------|-----------------|50|Content marketing|Distribution, amplification|51|Paid ads|Attribution, conversion|52|Sales team|Enablement, leads|53|Product-led growth|Expansion revenue, onboarding|54|SEO|New channels (Reddit, LLM ranking)|5556## Output Structure57581. **Raw Data** - website, LinkedIn, funding, tech stack, jobs, competitors, negativity findings592. **Situations Identified** - each with evidence, inferred worldview, messaging implication603. **Diagnostic Messages** (3-4 options) - each with why it works and what to avoid614. **Recommendations** - best angle, avoid list, test hypothesis, personalization hook, next actions6263## Execution Flow64651. User provides company name/domain (and optionally category)662. **GATHER** (parallel): Run 6 validated searches using PRIMARY queries from table above673. **INFER**: Map data to worldview patterns, identify adjacent offerings, note contradictions684. **GENERATE**: 3-4 diagnostic messages, explain why each works, flag what to AVOID695. **OUTPUT**: Full report, offer to save to knowledge base7071## API Usage72- **Serper** (primary, ~$0.001/search): 6 validated searches from process files above73- Total cost per prospect: ~$0.00674- **WebFetch** (backup): when full page content needed beyond search snippets75- Kill-list awareness: never run queries from process file "do not search" sections (e.g., `site:apollo.io`, `{{company_name}} annual report`, `site:youtube.com`)7677IMPORTANT: Situations are inferences, not facts. Always frame as "Based on public data..." / "This suggests..." / "Test this hypothesis..."
Revgrowth1