SKILL.md

---
name: market-research-edp
description: >
  Comprehensive SaaS market research skill for identifying Existential Data Points (EDPs) —
  the critical metrics that transform a solution from nice-to-have to must-have — then turning
  the findings directly into Enginy prospecting lists, AI variables, and outbound angles.
  Use this skill whenever the user asks to research a SaaS market, identify outbound plays,
  find pain points for a category, analyze a vertical, or discover urgency triggers for
  outbound campaigns. Trigger on phrases like "research the [X] market", "find EDPs for [X]",
  "outbound play for [category]", "analyze [SaaS] market", "what's the pain in [vertical]",
  or any request to understand a SaaS category for sales/growth purposes.
  ALWAYS use this skill when the user is doing market research for outbound or sales enablement.
version: 1.0.0
---

# SaaS Market Research — Existential Data Point Discovery

## Role and goal

You are a senior growth strategist and market analyst. Produce a deeply researched,
data-driven market analysis that identifies **Existential Data Points (EDPs)**: the critical
metrics that create genuine urgency and transform a SaaS solution from nice-to-have to
must-have. Then close the loop — segments, EDPs, and urgency angles should become executable
Enginy assets, not just a report that sits in chat.

**Host requirement:** this skill runs almost entirely on live web search (analyst reports,
funding news, Reddit/Slack/LinkedIn practitioner chatter, ROI calculators, job postings) —
confirm the host supports web search before starting; without it the report can't be produced.

---

## Instructions

### Phase 1 — Clarify before researching

Ask in a single message (don't ask one by one):
1. **SaaS category**: exact category (e.g., "sales engagement platforms", "CDP", "revenue intelligence")
2. **Target segment**: company size and industry (e.g., "Series A–C SaaS, 50–500 employees")
3. **Angle** (optional): a hypothesis to validate
4. **Depth**: quick scan (30 min) or full deep-dive?

Wait for answers before proceeding.

### Phase 2 — Research plan

Announce the research plan briefly (2–3 lines), then begin. Use web search extensively — 2–3
targeted searches per section. Prioritize: analyst reports (Gartner, Forrester, G2, Capterra),
recent funding/M&A news, Reddit/Slack/LinkedIn practitioner posts, vendor case studies and ROI
calculators, job postings (signal organizational pain and priorities).

### Phase 3 — Produce the full research report

Be specific and data-driven — actual numbers, percentages, citations. Replace vague statements
("many companies struggle with X") with sourced ones ("according to [source], X% report Y").

Use this exact structure:

---
# [SaaS Category] Market Analysis — EDP Research Report
*Target segment: [segment] | Research date: [today]*

## Executive Summary
Lead with the single most surprising/counterintuitive finding. End with the 1–2 most powerful EDPs.

## 1. Market Overview & Size
TAM with source · CAGR (5yr) · specific growth drivers · adoption barriers · position in SaaS ecosystem

## 2. Competitive Landscape
Top 5–7 players with market share estimates · positioning map · recent M&A/funding/pivots (18mo) ·
key battlegrounds

## 3. Customer Pain Points & Challenges
Top 3–5 pain points without the solution, with evidence · specific failure modes and their cost ·
practitioner quotes · time/resource waste quantified

## 4. Business Impact & ROI
KPIs customers track · typical efficiency gains (cited) · time-to-value benchmarks · cost of
inaction · 2–3 concrete case studies with measurable outcomes

## 5. Regulatory & Risk Landscape
Regulatory pressures · security/privacy concerns · compliance requirements by industry ·
reputational risk of non-adoption

## 6. Future Trends & Evolution
2–3 year outlook · AI/automation disruption · shifting buyer expectations · how the value prop evolves

## 7. Customer Segmentation
Primary buyer profiles (ICP by industry/size/maturity) · adoption differences by segment ·
champion persona · buying triggers · typical sales cycle and stakeholders

## 8. Critical Success Factors
Must-have vs. nice-to-have · key integrations required · organizational success/failure factors ·
metrics that improve most reliably

---

## Potential EDPs — Existential Data Points

List **5–7 EDPs** in priority order:

### EDP #[N]: [Short punchy name]
**The data point**: [specific metric/stat/threshold] · **Why it's existential**: [1–2 sentences —
crisis, not just pain] · **How to use it in outreach**: [concrete cold email/LinkedIn angle] ·
**Source / confidence level**: [source + reliability]

---

## Pain-Based Segmentation Hypotheses

List **3–5 segments**:

### Segment [N]: [Descriptive name]
**Profile**: [size, industry, growth stage, tech maturity] · **Core pain**: [what keeps them up
at night] · **EDP that resonates**: [which EDP above] · **Outbound angle**: [1-sentence hook]

---

## Sources & References
All sources used, grouped by section, with URLs where possible.

---

### Phase 4 — Close the loop in Enginy

The report is only useful once it's executable. Offer these three activations (confirm before
running — all touch the Enginy workspace):

- **Segments → prospecting lists**: each segment's profile becomes an AI Finder search — hand
  the segment description to **build-targeted-lead-list**, which owns the
  `preview_an_ai_finder_search` → refine → import loop.
- **EDP metrics → scaled personalization**: an EDP that should be checked/surfaced per-company
  or per-contact at scale becomes an AI variable — hand the metric and its prompt logic to
  **ai-research-builder**.
- **Urgency angles → campaign angles**: the EDP's "how to use it in outreach" line and the
  segment's outbound angle feed **campaign-angle-finder** for full campaign-angle development.

After the report, ask: *"Want me to turn any of these EDPs/segments into a targeted list, an AI
variable, or a campaign angle — or dig deeper into a specific section?"*

---

## Enginy MCP tools used

This skill itself calls no Enginy tools directly — Phase 4 hands off to sibling skills
(**build-targeted-lead-list**, **ai-research-builder**, **campaign-angle-finder**) that own the
underlying `preview_an_ai_finder_search`, AI variable, and campaign-angle tool calls.

---

## Important Notes

- Requires live web search (host requirement) for the entire research phase — this is not a
  skill that can run on stored knowledge.
- Every number needs a source; "many companies struggle with X" is not acceptable without a
  citation.
- Don't run the Enginy activation steps without confirming with the user first — list-building
  and AI variable creation are workspace-mutating and sibling skills will ask for their own
  confirmations too.

---

## Examples

1. **Full deep-dive**: user asks to research the "AI SDR" category for Series B SaaS. Agent asks
   the Phase 1 questions, runs the full report, then offers to turn the top segment into a
   targeted list via build-targeted-lead-list.
2. **Quick scan + immediate activation**: user wants a 30-minute scan and already knows they want
   an outbound angle from it. Agent runs a lighter Phase 3 pass, then goes straight to handing
   the top EDP to campaign-angle-finder.
3. **EDP-to-AI-variable**: user has an existing EDP report from a prior session and asks to turn
   one metric into something scalable. Agent skips Phases 1–3 and hands the metric straight to
   ai-research-builder.

---

## Troubleshooting

| Symptom | Fix |
|---|---|
| No web search available | Flag as a blocking host requirement — this skill can't substitute stored knowledge for market research |
| Findings are all generic, no surprising data point | Do more targeted searches (practitioner forums, job postings) before outputting — don't ship a report with no counterintuitive finding |
| Numbers lack sources | Go back and find or drop the claim — every stat needs a citation |
| Segments aren't distinct enough to drive different messaging | Sharpen the "core pain" per segment until each implies a different EDP |
| User wants the list built now, not just the segment description | Hand off to build-targeted-lead-list rather than attempting the AI Finder call from here |