prospect-research

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Full account research sweep using live data. Trigger with "research [company]", "deep scan [company]", "what's the hook for [company]", "who should I target at [company]", "add account [company]", or "tell me about [company]".

romiluz13 By romiluz13 schedule Updated 3/3/2026

name: prospect-research description: Full account research sweep using live data. Trigger with "research [company]", "deep scan [company]", "what's the hook for [company]", "who should I target at [company]", "add account [company]", or "tell me about [company]". version: 1.0.0

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.


STEP 0 — Confirm Account and Goal

Before any research:

  1. Confirm the account name (exact spelling — becomes the folder path in kebab-case)
  2. Initial research or a refresh?
  3. Check if accounts/{account-name}/ already exists

If no folder exists → create the structure now:

accounts/{account-name}/
├── account-brief.md
├── research/
├── discovery/
├── meetings/
└── emails/

STEP 1 — Internal Knowledge First

Check what already exists before going to the web:

~~CRM (if connected):

  • Search for existing relationship, open opportunities, past interactions
  • Pull any contact records

Local files:

  • accounts/{account}/ — prior research files, call notes, meeting briefs
  • knowledge/competitive/ — relevant battlecards
  • knowledge/personas/ — ICP persona guides

STEP 2 — Web Research

Detect mode: Is ~~web research (Bright Data) connected?

FULL MODE (~~web research connected)

Run these searches in parallel:

1. "{Company} tech stack {seller's solution category} 2025 2026"
2. "{Company} engineering hiring jobs 2026"
3. "{Company} news funding announcement 2026"
4. "{Company} CTO VP Engineering Chief Architect"
5. "{Company} {solution category} challenge problem pain"
6. "{Company} AI machine learning 2026"
7. "{seller's company} {company industry} case study customer"
8. "{Company} engineering blog AI machine learning 2026"
9. "{Company} hiring AI engineer vector LLM RAG embedding 2026"
10. "{Company} {CTO or 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 ~~web research — built-in web search)

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:

  • Confirmed tech stack (not assumed)
  • Engineering headcount and growth trajectory
  • AI/ML initiatives
  • Recent funding, product launches, leadership changes
  • Engineering leadership names + any recent changes
  • People publishing about relevant topics
  • Job postings mentioning specific tools → reveals architecture decisions

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 the ICP from seller profile
I — Intent Signals /25 Active hiring, product launch, funding, leadership change, competitor eval
T — Timing /25 Urgency window — is there a forcing function?
S — Solution Match /25 Is their confirmed pain solvable by the seller's product?

Tiers: 80+ = Tier 1 (prioritize) | 60–79 = Tier 2 | 40–59 = Tier 3 | <40 = Tier 4


STEP 4 — Synthesize and Save

Output path: accounts/{account-name}/research/{YYYY-MM-DD}-initial-research.md

Required sections (write-outreach reads these):

# {Company} — Research
**Date:** {YYYY-MM-DD} | **Mode:** Full / Lite

---

## Key Signals
[Fresh, time-sensitive findings. Lead with 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 Relevant Topics
| Name | Title | Topic | Link | Why It Matters |
|------|-------|-------|------|----------------|

## Job Postings (Architecture Intelligence)
| 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:** [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

  • Internal knowledge checked before web research
  • At least 5 queries run (full mode: 10+)
  • Tech stack confirmed from source — not assumed
  • At least 1 pain signal cited with source URL
  • FITS score calculated with reasoning per dimension
  • Sales motion route stated with reasoning
  • All required chain interface sections present
  • account-brief.md updated
  • No invented metrics
Install via CLI
npx skills add https://github.com/romiluz13/agentic-seller --skill prospect-research
Repository Details
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article Path SKILL.md
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