win-loss-analysis▌
whyashthakker/agent-skills-marketing · updated Apr 9, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
### 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.
| 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 on Cursor
AI-first code editor with Composer
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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches win-loss-analysis from GitHub repository whyashthakker/agent-skills-marketing and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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
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.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 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▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★28 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.
showing 1-10 of 28