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accelerating-pipeline

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업데이트2026년 2월 22일 09:40

Use when pipeline velocity is flat or declining, win rates are below 20 percent, sales cycles are lengthening, deals are single-threaded, or lead routing is slow. Use when MQL volume is prioritized over pipeline quality, when marketing and sales define funnel stages differently, or when signal-based engagement has not replaced linear nurture drips.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

amogha-dalvi

amogha-dalvi/Marketing_GTM

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

Pipeline Accelerator

Overview

Pipeline velocity is four levers operating together: deal volume, win rate, deal size, and cycle time. A 15% improvement across all four produces a 75% velocity increase. This skill identifies the highest-leverage lever and applies AI-powered signal-based approaches to move all four.

When to Use

Pipeline velocity is flat or declining quarter over quarter
Win rate is below 20% overall
Sales cycle length is increasing or exceeds 60 days
Deals are single-threaded (one champion per opportunity)
Lead response time exceeds 5 minutes for high-intent signals
MQL volume is the primary marketing success metric
Marketing and sales define pipeline stages differently
No formal pipeline velocity measurement exists
Lead scoring is manual, static, or nonexistent

Don't use when: You have fewer than 30 closed deals to analyze, or when the constraint is product-market fit rather than GTM execution.

Quick Reference

Phase	Duration	Output
Pipeline velocity audit	Day 1-2	Velocity, lever analysis
Intent-based ABM design	Day 3-5	Account tiers, sequences
Signal-based engagement	Day 6-8	Signal taxonomy, routing
Buying group orchestration	Day 9-10	Coverage maps, playbook
Predictive lead scoring	Day 11-13	Scoring model
Win rate improvement	Day 14-16	Win/loss system
RevOps alignment	Day 17-19	Dashboard, SLAs
AI agent deployment	Day 20-22	Agent specs, metrics

Core Deliverables

Pipeline Velocity Audit -- Velocity calculation, lever analysis, conversion map
ABM Program Design -- Account tiers, buying group maps, engagement sequences
Signal Engagement System -- Signal taxonomy, scoring model, routing rules
Buying Group Orchestration -- Coverage maps, role-based content, gap-filling playbook
Lead Scoring Model -- Fit, intent, engagement layers with composite scoring
Win Rate Playbook -- Win/loss analysis, personalization, deal acceleration
RevOps Dashboard -- Pipeline, funnel, signal, attribution views

Common Mistakes

Optimizing only top-of-funnel volume while ignoring other velocity levers
Using MQL volume as success metric instead of pipeline created
Building linear nurture drips that ignore buyer context and signal strength
Routing all leads the same way regardless of intent level
Single-threading deals instead of orchestrating the buying group
Deploying AI agents without measuring baseline performance first

Integration

Feeds into: tracking-marketing-metrics, managing-marketing-ops

Refresh: Velocity weekly. Lever analysis monthly. ABM list quarterly. Lead scoring quarterly. Full system audit every 6 months.

See workflow.md for detailed phase-by-phase execution, velocity calculators, ABM templates, signal workflows, scoring architecture, and AI agent deployment plans.

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name	accelerating-pipeline
description	Use when pipeline velocity is flat or declining, win rates are below 20 percent, sales cycles are lengthening, deals are single-threaded, or lead routing is slow. Use when MQL volume is prioritized over pipeline quality, when marketing and sales define funnel stages differently, or when signal-based engagement has not replaced linear nurture drips.
user-invocable	true
metadata	{"version":"1.0.0"}

Pipeline Accelerator

Overview

When to Use

Pipeline velocity is flat or declining quarter over quarter
Win rate is below 20% overall
Sales cycle length is increasing or exceeds 60 days
Deals are single-threaded (one champion per opportunity)
Lead response time exceeds 5 minutes for high-intent signals
MQL volume is the primary marketing success metric
Marketing and sales define pipeline stages differently
No formal pipeline velocity measurement exists
Lead scoring is manual, static, or nonexistent

Don't use when: You have fewer than 30 closed deals to analyze, or when the constraint is product-market fit rather than GTM execution.

Quick Reference

Phase	Duration	Output
Pipeline velocity audit	Day 1-2	Velocity, lever analysis
Intent-based ABM design	Day 3-5	Account tiers, sequences
Signal-based engagement	Day 6-8	Signal taxonomy, routing
Buying group orchestration	Day 9-10	Coverage maps, playbook
Predictive lead scoring	Day 11-13	Scoring model
Win rate improvement	Day 14-16	Win/loss system
RevOps alignment	Day 17-19	Dashboard, SLAs
AI agent deployment	Day 20-22	Agent specs, metrics

Core Deliverables

Pipeline Velocity Audit -- Velocity calculation, lever analysis, conversion map
ABM Program Design -- Account tiers, buying group maps, engagement sequences
Signal Engagement System -- Signal taxonomy, scoring model, routing rules
Buying Group Orchestration -- Coverage maps, role-based content, gap-filling playbook
Lead Scoring Model -- Fit, intent, engagement layers with composite scoring
Win Rate Playbook -- Win/loss analysis, personalization, deal acceleration
RevOps Dashboard -- Pipeline, funnel, signal, attribution views

Common Mistakes

Optimizing only top-of-funnel volume while ignoring other velocity levers
Using MQL volume as success metric instead of pipeline created
Building linear nurture drips that ignore buyer context and signal strength
Routing all leads the same way regardless of intent level
Single-threading deals instead of orchestrating the buying group
Deploying AI agents without measuring baseline performance first

Integration

Feeds into: tracking-marketing-metrics, managing-marketing-ops

Refresh: Velocity weekly. Lever analysis monthly. ABM list quarterly. Lead scoring quarterly. Full system audit every 6 months.

See workflow.md for detailed phase-by-phase execution, velocity calculators, ABM templates, signal workflows, scoring architecture, and AI agent deployment plans.