Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add unixcrh/phuryn-pm-skills --skill user-segmentationgit clone --depth 1 https://github.com/unixcrh/phuryn-pm-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/unixcrh/phuryn-pm-skills/user-segmentation)<a href="https://agentmods.dev/skills/unixcrh/phuryn-pm-skills/user-segmentation"><img src="https://agentmods.dev/badge/skills/unixcrh/phuryn-pm-skills/user-segmentation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/unixcrh/phuryn-pm-skills/user-segmentation"><img src="https://agentmods.dev/badge/skills/unixcrh/phuryn-pm-skills/user-segmentation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.00827 |
| Opus 5.5 | $0.00020 | $0.00331 |
| Sonnet 5.5 | $0.00010 | $0.00165 |
| Haiku 4.5 | $0.00005 | $0.00083 |
Grade A, and why
user-segmentation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to user-segmentation — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 89 lines · 49 tokens per session scan A d91f745c9520
user-segmentation is a skill published in the GitHub repository unixcrh/phuryn-pm-skills (2 stars, last pushed 8mo ago), licensed MIT. It adds 49 tokens to every session and 827 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to user-segmentation, differing in 0 lines, and is treated as a copy.
user-segmentation compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| user-segmentationunixcrh/phuryn-pm-skills | Skill | 2 | 8mo ago | MIT | A | 49 | Claude Code |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | today | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 99k | today | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 4d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |