smithery.ai

pql-framework

Methodology for defining product-qualified lead (PQL) signals, scoring, and routing.

First seen Mar 22, 2026

Installation

$ npx skills add https://smithery.ai

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,253 B
  • docs SUMMARY.md 105 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

PQL Framework Skill

When to Use

  • Standing up or recalibrating PQL/PQA programs.
  • Aligning product, growth, and sales on what constitutes a high-intent product user.
  • Auditing the health of existing PQL scoring + routing logic.

Framework

  1. Signal Library – catalog feature usage, plan limits, collaboration signals, intent, firmographics.
  2. Scoring Model – weight signals, set decay rules, and define negative indicators.
  3. Tiering – map PQL tiers (A/B/C) to follow-up motions and SLAs.
  4. Routing Rules – specify owners, cues, channels (CRM tasks, Slack alerts, CS queue).
  5. Measurement Loop – track conversion, ARR impact, and feedback for model tuning.

Templates

  • Signal inventory worksheet with data source + freshness.
  • Scoring matrix with weights, thresholds, and decay logic.
  • Routing decision tree linking tiers to plays.

Tips

  • Start with simple tiering, iterate once telemetry + feedback improve.
  • Include “disqualifier” signals (expired trials, churn risk) to avoid noise.
  • Pair with operationalize-pql-routing to push models into automation.