SkillsAdd

smithery.ai

win-loss-dataset

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

First seen Apr 20, 2026

Installation

$ npx skills add https://smithery.ai
View on skills.sh ↗ Website ↗

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Also in this package

Other skills from smithery.ai · top by installs.

npx skills add https://smithery.ai
smithery-ai-cli 7.8K frontend-design 6.2K skill-creator 2.5K ck:ui-ux-pro-max 1.3K pdf 1.2K xlsx 966 pptx 894 docx 884 webapp-testing 1 web-design-reviewer 657

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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
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

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