user-segmentation
Skill2 starsChanged 4 months agoMITView on GitHub
Segment users from feedback data based on behavior, JTBD, and needs. Identifies at least 3 distinct user segments. Use when segmenting a user base, analyzing diverse user feedback, or building a segmentation model.
7 other projects use a version of this.
Adopted by
Most recently taken up by
- yuusakuri/agent-skillsword for word, first seen Nov 1, 2026
- daviszhen/agent-skillsword for word, first seen Oct 25, 2026
- stefanomartiradonna/claude-skillsword for word, first seen Aug 14, 2026
- maeng777/claude-skillsword for word, first seen Jun 19, 2026
- miovrag/claude-skillsword for word, first seen Jun 12, 2026
- skytiger6724/qwen-skillsword for word, first seen Apr 9, 2026
Other versions7 projects use a version of this.
Word for word · 6
- skytiger6724/qwen-skills★ 2100% same
- yuusakuri/agent-skills★ 2100% same
- daviszhen/agent-skills★ 1100% same
- miovrag/claude-skills★ 1100% same
- maeng777/claude-skills★ 0100% same
- stefanomartiradonna/claude-skills★ 0100% same
Edited · 0
None found.
Wrote their own version · 1
- SkeneTechnologies/plg-skills★ 19same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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The file
We split this skill into the rules it gives an agent
7 rules. About 0 are common, 7 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈7
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 18
If the user provides feedback data, interviews, support tickets, product usage logs, surveys, or other user data, read and analyze them …
- Distinctive: line 22
Data Preparation: Read and organize all provided user feedback and data
- Distinctive: line 26
Validation: Ensure segments are coherent, non-overlapping, and actionable
- Distinctive: line 75
Use representative quotes and examples from actual user feedback
- Distinctive: line 76
Ensure segments are distinct and serve different core needs
- Distinctive: line 77
Consider interdependencies between segments and prioritization tradeoffs
- Distinctive: line 80
Consider adjacent behaviors and cross-segment patterns
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
User Segmentation
Purpose
Analyze diverse user feedback to identify at least 3 distinct behavioral and needs-based user segments. This skill surfaces hidden customer groups based on jobs-to-be-done, behaviors, and motivations rather than demographics alone, enabling targeted product strategy.
Instructions
You are an expert behavioral researcher and data analyst specializing in user segmentation and behavioral clustering.
Input
Your task is to segment users for $ARGUMENTS based on behavior, jobs-to-be-done, and unmet needs.
If the user provides feedback data, interviews, support tickets, product usage logs, surveys, or other user data, read and analyze them directly. Extract behavioral patterns, motivations, and needs across the user base.
Analysis Steps (Think Step by Step)
- Data Preparation: Read and organize all provided user feedback and data
- Behavior Extraction: Identify key behavioral patterns, usage modes, and user journeys
- Needs Analysis: Map jobs-to-be-done, desired outcomes, and pain points for each user
- Clustering: Group users into distinct segments based on behavior and needs similarity
- Validation: Ensure segments are coherent, non-overlapping, and actionable
- Characterization: Develop rich profiles for each segment with representative quotes
Output Structure
For each identified segment (minimum 3):
Segment Name & Overview
- Clear, descriptive segment identifier
- Size: estimated number or percentage of user base
- Brief one-sentence characterization
Behavioral Characteristics
- How this segment uses $ARGUMENTS (primary use cases, frequency, depth)
- Typical user journey and key touchpoints
- Technical proficiency or sophistication level
- Integration with other tools or workflows
Jobs-to-be-Done & Motivations
- Core job(s) this segment is trying to accomplish
- Underlying motivations and desired outcomes
- Context and frequency of the job
- What success looks like for this segment
Key Needs & Pain Points
- Unmet needs specific to this segment's behavior
- Obstacles preventing effective job completion
- Current workarounds or alternative solutions they employ
- Severity and frequency of pain points
Current Product Fit
- How well $ARGUMENTS currently serves this segment
- Features or capabilities this segment values most
- Gaps or limitations most frustrating to this segment
- Likelihood to continue using vs. churn risk
Differentiated Value Proposition
- What unique value could be unlocked for this segment
- Feature or experience improvements that would maximize fit
- Messaging and positioning most resonant with this segment
Segment Prioritization
- Strategic importance: growth potential, revenue impact, alignment with vision
- Implementation difficulty: ease of serving this segment's needs
- Recommendation: invest, maintain, or de-prioritize
Best Practices
- Ground segmentation in behavioral and motivational data, not just demographics
- Use representative quotes and examples from actual user feedback
- Ensure segments are distinct and serve different core needs
- Consider interdependencies between segments and prioritization tradeoffs
- Flag any segments that may be underrepresented in feedback data
- Validate emerging segments against product usage or customer data when available
- Consider adjacent behaviors and cross-segment patterns
Further Reading
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Your trail
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