| name | prospect-research |
| description | Full account research sweep. Uses internal knowledge + live web data to build research file and account-brief.
Use when: researching a new account, refreshing intel before outreach, understanding tech stack,
finding the right contacts, identifying buying signals, or building an account-brief.
Triggers: research, scan, deep scan, intel, company overview, tech stack, who should I target,
find contacts, what do we know about, prospect, account brief, initial research, new account,
add account, what's the hook.
|
| version | 1.0.0 |
| user-invocable | true |
Prospect Research
⚠️ DATA RULE: Never trust pre-training knowledge for current company state. Always pull live data.
Pre-training is stale. Internal knowledge first → web research second → synthesize third.
⚠️ DATE RULE: Check currentDate from context before creating any file. All files named YYYY-MM-DD-{topic}.md.
STEP 0 — Confirm Account and Goal
Before any research:
- Confirm the account name (exact spelling — this becomes the folder path in kebab-case)
- Is this initial research or a refresh?
- Check if
accounts/{account-name}/ already exists
- Check if
account-brief.md already exists
If no folder exists → create the structure now:
accounts/{account-name}/
├── account-brief.md ← Create skeleton from _TEMPLATE
├── research/
├── discovery/
├── meetings/
└── emails/
STEP 1 — Internal Knowledge First
Check what already exists before going to the web:
CRM (if MCP configured):
- Search for existing relationship, open opportunities, past interactions
- Pull any contact records for the account
Local files:
accounts/{account-name}/ — prior research files, call notes, meeting briefsknowledge/competitive/ — relevant battlecardsknowledge/personas/ — ICP persona guides
What to look for:
STEP 2 — Web Research
Detect mode first: Check if mcp__brightdata__* tools are available.
FULL MODE (Bright Data available)
Run in parallel via mcp__brightdata__search_engine_batch:
Query 1: "{Company} tech stack {{SOLUTION_CATEGORY}} 2025 2026"
Query 2: "{Company} engineering hiring jobs 2026"
Query 3: "{Company} news funding announcement 2026"
Query 4: "{Company} CTO VP Engineering Chief Architect"
Query 5: "{Company} {{SOLUTION_CATEGORY}} challenge problem pain"
Query 6: "{Company} AI machine learning 2026"
Query 7: "{{COMPANY_NAME}} {Company industry} case study customer"
--- Deep Signal Layer (always run) ---
Query 8: "{Company} engineering blog AI machine learning 2026"
Query 9: "{Company} AI conference talk keynote 2025 2026"
Query 10: "{Company} hiring AI engineer vector LLM RAG embedding 2026"
Query 11: "{Company} {CTO/VP name} blog post LinkedIn article 2026"
Then scrape for depth:
- Company website → About, Engineering, Careers pages
- Recent blog posts (prioritize AI/engineering content)
- Press releases from last 90 days
- LinkedIn company page (if accessible)
LITE MODE (no Bright Data — WebSearch fallback)
1. "{Company} tech stack 2026"
2. "{Company} CTO VP Engineering LinkedIn"
3. "{Company} news funding 2026"
4. "{Company} {{SOLUTION_CATEGORY}} use case"
5. "{Company} engineering blog AI"
Note in output: "⚠️ Lite mode — search results only, no page scraping. Lower signal quality."
Key signals to find:
STEP 3 — FITS Scoring
Score the account before writing the research file:
| Dimension | Score (0–25) | What to assess |
|---|
| F — Firmographic Fit | /25 | Industry, company size, stage, headcount match {{ICP_DESCRIPTION}} |
| I — Intent Signals | /25 | Active hiring, product launch, funding, leadership change, competitor eval |
| T — Timing | /25 | Urgency window — is there a forcing function? Fiscal year end? Launch date? |
| S — Solution Match | /25 | Is their confirmed pain solvable by {{PRODUCT_NAME}}? |
Total FITS Score: /100
- 80+: Tier 1 — prioritize immediately
- 60–79: Tier 2 — strong, worth outreach
- 40–59: Tier 3 — possible, lower priority
- Below 40: Tier 4 — stretch, deprioritize
STEP 4 — Synthesize and Save
Output path: accounts/{account-name}/research/{YYYY-MM-DD}-initial-research.md
(Refresh: {YYYY-MM-DD}-research-update.md)
Required sections (chain interface — write-outreach reads these):
# {Company} — Research
**Date:** {YYYY-MM-DD} | **Mode:** Full / Lite
---
## 2026 Key Signals
[Fresh, time-sensitive findings. Lead with the most recent.]
## Company Overview
[2-3 sentences: what they do, size, stage, HQ]
## Tech Stack (Confirmed)
| System | Technology | Source |
|--------|-----------|--------|
## Leadership Contacts
| Name | Title | Relevance | Source |
|------|-------|-----------|--------|
## People Publishing About AI / {{SOLUTION_CATEGORY}}
| Name | Title | Topic | Link | Why It Matters |
|------|-------|-------|------|----------------|
## AI / {{SOLUTION_CATEGORY}} Job Postings
| Role | Tools Mentioned | Posted | Implication |
|------|----------------|--------|-------------|
## Pain Signals (Research-Confirmed)
1. [Pain] → Source: [URL]
## Sales Opportunity Analysis
**FITS Score:** /100 | **ICP Tier:** 1/2/3/4
**Sales Motion Route:** Classic / Sprint / Fast / Unknown
**Value Driver:** Make Money / Save Money / Go Fast / Be Safe
**Why Anything:** [What breaks if they do nothing?]
**Why {{PRODUCT_NAME}}:** [Which capability matches their pain?]
**Why Now:** [What external event creates urgency?]
## Top 3 Outreach Hooks
1. [Specific person + specific signal + specific angle. Ready to use.]
2.
3.
## Recommended First Contact
**Who:** [Name + title]
**Channel:** LI / Email / Cold Call
**Hook:** [One-liner]
## Sources
- [URL or document with description]
STEP 5 — Update account-brief.md
After saving the research file:
- Add/update FITS score and ICP tier
- Add any new confirmed contacts
- Update "Last Updated" date
- Update "Top Pain Signal" and "Top Hook" fields
Quality Gates
