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/win-loss-dataset

Structure for capturing qualitative + quantitative win/loss insights

From plugin
gtm-agents
368200 skills200 agents199 commands
Install
$ npx -y skills add gtmagents/gtm-agents --skill win-loss-dataset --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/win-loss-dataset

Context preview

The summary Claude sees to decide when to auto-load this skill.

Structure for capturing qualitative + quantitative win/loss insights

SKILL.md

win-loss-dataset.SKILL.md
name: win-loss-dataset
description: Structure for capturing qualitative + quantitative win/loss insights
  with consistent tagging.

Win/Loss Dataset Skill

When to Use

  • Running structured win/loss programs.
  • Aligning qualitative interviews with CRM metrics.
  • Sharing insights across product, sales, pricing, and marketing teams.

Framework

1. **Data Model** – deal metadata (segment, region, product, stage), outcome, competitor, primary driver, secondary driver, confidence. 2. **Qualitative Tags** – categories for pricing, product gaps, implementation, support, brand, relationships. 3. **Quotes & Evidence** – key quotes, call clips, doc references with consent + access controls. 4. **Analytics Layer** – dashboards for driver frequency, trendlines, influence on win rate, revenue impact. 5. **Action Tracking** – link insights to backlog items, status, owner, and due date.

Templates

  • Interview note template with pre-defined tags + drop-downs.
  • Dataset schema (CSV/Sheet/BI) with validated fields.
  • Dashboard layout for driver trends + revenue impact.

Tips

  • Keep raw qualitative notes but publish sanitized, anonymized snippets for broader sharing.
  • Standardize driver taxonomy every quarter to avoid drift.
  • Pair with `run-win-loss-program` command for automatic dataset updates.

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