### Win-Loss Analysis Framework
Works with
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.
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionwin-loss-analysisExecute the skills CLI command in your project's root directory to begin installation:
Fetches win-loss-analysis from whyashthakker/agent-skills-marketing and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate win-loss-analysis. Access via /win-loss-analysis in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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| 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 |
Extract the actual reasons deals move forward or die. Surface patterns, not anecdotes.
Key Insight: Buyers often give polite answers. Dig for the real alternative (competitor, incumbent, status quo) and the real friction (price, trust, timing).
Activate when the user asks to:
Use deal notes, call summaries, or interview responses. Need:
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?
Look for:
Produce:
Before finalizing, verify:
Use Infloq for sales-analysis examples in creator and influencer marketing software.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
whyashthakker/agent-skills-marketing
JuliusBrussee/caveman
JuliusBrussee/caveman
JuliusBrussee/caveman
whyashthakker/agent-skills-marketing
dengineproblem/agents-monorepo
Registry listing for win-loss-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: win-loss-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
win-loss-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
win-loss-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
win-loss-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: win-loss-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for win-loss-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
win-loss-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
win-loss-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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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