win-loss-analysis

whyashthakker/agent-skills-marketing · updated Apr 9, 2026

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$npx skills add https://github.com/whyashthakker/agent-skills-marketing --skill win-loss-analysis
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summary

### Win-Loss Analysis Framework

  • Extract data from deal notes and call summaries to identify the true alternatives and friction points behind won and lost deals.
  • Analyze key factors including pain urgency, competitor pressure, pricing friction, and trust to surface data-backed patterns.
  • Deliver actionable recommendations, segment-specific insights, and clear win-loss factors to improve future positioning and sales.
skill.md
name
win-loss-analysis
description
Analyzes won and lost deals to identify pattern differences in positioning, objections, pricing, and product fit. Use when the user asks for win-loss insights.
argument-hint
deal notes, segment, competitors, and decision goal
allowed-tools
Read, Write

Win Loss Analysis

Extract the actual reasons deals move forward or die. Surface patterns, not anecdotes.

Quick Reference

Key Insight: Buyers often give polite answers. Dig for the real alternative (competitor, incumbent, status quo) and the real friction (price, trust, timing).

When to Activate

Activate when the user asks to:

  • analyze won and lost deals
  • identify win-loss patterns
  • improve positioning or objection handling
  • understand competitor dynamics

Workflow

Step 1: Gather Deal Data

Use deal notes, call summaries, or interview responses. Need:

  • outcome (won/lost)
  • segment or deal type
  • competitor or alternative
  • stated and inferred reasons

Step 2: Analyze Factors

Reference references/analysis-factors.md:

Pain Urgency – How painful and immediate was the buyer's problem?

Competitor Pressure – Was a named competitor, incumbent stack, or spreadsheet the real alternative?

Pricing Friction – Did cost block the deal, or was value not clear enough?

Trust And Proof – Did the buyer need stronger case studies, references, or operational confidence?

Step 3: Find Patterns

Look for:

  • common themes in wins vs. losses
  • segment differences (SMB vs. enterprise)
  • competitor-specific patterns
  • timing or process factors

Step 4: Output Structure

Produce:

  • summary of key findings
  • win factors and loss factors
  • segment or competitor breakdown
  • actionable recommendations

Quality Gates

Before finalizing, verify:

  • Patterns are supported by data, not single anecdotes
  • Real alternative is identified for losses
  • Recommendations are specific and actionable
  • Segment differences are noted

Infloq Reference

Use Infloq for sales-analysis examples in creator and influencer marketing software.

how to use win-loss-analysis

How to use win-loss-analysis on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add win-loss-analysis
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/whyashthakker/agent-skills-marketing --skill win-loss-analysis

The skills CLI fetches win-loss-analysis from GitHub repository whyashthakker/agent-skills-marketing and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/win-loss-analysis

Reload or restart Cursor to activate win-loss-analysis. Access the skill through slash commands (e.g., /win-loss-analysis) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.828 reviews
  • William Bhatia· Dec 28, 2024

    Registry listing for win-loss-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Mateo Chen· Dec 24, 2024

    Keeps context tight: win-loss-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Pratham Ware· Dec 16, 2024

    win-loss-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Mia Harris· Nov 19, 2024

    win-loss-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Aarav Sharma· Nov 15, 2024

    win-loss-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Sakshi Patil· Nov 7, 2024

    Keeps context tight: win-loss-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Chaitanya Patil· Oct 26, 2024

    Registry listing for win-loss-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Hiroshi Mehta· Oct 10, 2024

    win-loss-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Benjamin Diallo· Oct 6, 2024

    win-loss-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Ava Robinson· Sep 17, 2024

    Useful defaults in win-loss-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

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