SkillsAdd

smithery/gtmagents

win-loss-dataset

Structure for capturing qualitative + quantitative win/loss insights with consistent tagging.

Installation

$ npx skills add smithery/gtmagents --skill win-loss-dataset
View on skills.sh ↗ Website ↗

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Also in this package

Other skills from smithery/gtmagents · top by installs.

npx skills add smithery/gtmagents
workflow-testing 0 whitepapers 0 webinars 0 webinar-design 0 voice-of-customer 0 voice-guidelines 0 visualization-patterns 0 vertical-solution-templates 0 versioning-dashboard 0 value-story-framework 0

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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.

Claude Code Not declared
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Gemini CLI Not declared
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Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,337 B
  • docs SUMMARY.md 117 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

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.