CtrlK
BlogDocsLog inGet started
Tessl Logo

iasv/deal-preliminary-screening

Rapid batch triage of 10-50+ startups into investment tiers using IASV criteria — screens for thesis fit, red flags, and prioritizes deep-dive candidates

100

Quality

100%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

Overview
Quality
Evals
Security
Files

SCREENING-CRITERIA.mdreferences/

Screening Criteria — Weighted Scoring

Criteria Weights & Signals

screening_criteria:
  traction:
    weight: 0.30
    signals:
      strong:
        - Revenue > $500K ARR
        - 100+ paying customers
        - Clear growth trajectory (>20% MoM)
      moderate:
        - Revenue < $500K but growing
        - 10-100 customers
        - Pilots with named enterprises
      weak:
        - No revenue
        - Only beta users
        - No named customers

  team:
    weight: 0.25
    signals:
      strong:
        - Prior exits
        - Domain expertise (5+ years)
        - Technical co-founder
        - Full-time commitment
      moderate:
        - Relevant industry experience
        - Strong advisory board
      weak:
        - First-time founders
        - No technical co-founder
        - Part-time

  market:
    weight: 0.20
    signals:
      strong:
        - TAM > $1B
        - Clear macro tailwind
        - Regulatory advantage
      moderate:
        - TAM $100M-$1B
        - Growing market
      weak:
        - Niche market
        - Declining sector

  differentiation:
    weight: 0.15
    signals:
      strong:
        - Proprietary tech/IP
        - Network effects
        - Data moat
      moderate:
        - Execution advantage
        - Unique positioning
      weak:
        - Commodity space
        - Many competitors

  stage_fit:
    weight: 0.10
    signals:
      strong:
        - Stage matches fund thesis
        - Check size appropriate
        - Geography aligned
      weak:
        - Too early or too late
        - Check size mismatch

Quick Filter Logic

def quick_filter(company):
    """Fast pass/fail based on hard criteria"""

    # Hard passes
    if company.raised > fund_max_stage:
        return "PASS", "Too late stage"
    if company.sector in excluded_sectors:
        return "PASS", "Sector exclusion"
    if company.geography not in target_geos:
        return "PASS", "Geography mismatch"

    # Quick tier assignment
    score = 0
    if company.revenue > 0:
        score += 2
    if company.customers > 10:
        score += 2
    if company.has_enterprise_pilot:
        score += 1
    if company.founder_has_exit:
        score += 2
    if company.noted_as_interesting:
        score += 1

    if score >= 5:
        return "TIER_1", "Strong initial signals"
    elif score >= 3:
        return "TIER_2", "Needs validation"
    else:
        return "TIER_3_OR_4", "Requires deeper review"

references

SCREENING-CRITERIA.md

SKILL.md

tile.json