phuryn/pm-skills is a marketplace of reusable skills, commands, and plugins that guide AI assistants through product-management work such as discovery, strategy, planning, metrics, launches, and growth. It is for product managers and teams using Claude Code, Cowork, or compatible assistants. The catalogue entries are the project's own workflows and extensions.
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 phuryn/pm-skills --skill user-segmentationgit clone --depth 1 https://github.com/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/phuryn/pm-skills/user-segmentation)<a href="https://agentmods.dev/skills/phuryn/pm-skills/user-segmentation"><img src="https://agentmods.dev/badge/skills/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/phuryn/pm-skills/user-segmentation"><img src="https://agentmods.dev/badge/skills/phuryn/pm-skills/user-segmentation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
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 1mo 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- user-segmentation — 100% identical, 0 lines differ
- user-segmentation — 100% identical, 0 lines differ
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.
- 1mo ago First seen · 89 lines · 49 tokens per session scan A d91f745c9520
user-segmentation is a skill published in the GitHub repository phuryn/pm-skills (27,115 stars, last pushed today), 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-15.
Other skills, from other repositories
writing-assistant
Rewrites, sharpens and drafts B2B writing in the writer's real voice: Slack messages, emails, memos, PRDs, decks, plus marketing content (blog posts, landing pages, press releases, case studies, newsletters, social posts). Use whenever someone wants to rewrite, tighten, draft or audit copy, asks why copy isn't landing…
product-marketing-context
Build and maintain the shared GTM brain — product context, ICP, positioning, voice, market context, proof points, plus optional strategy and execution, and the deal-reality fields (objections, switching forces, customer language, anti-persona) — that every other skill in this stack reads before producing output. Works…
competitive-battlecard
Builds a one-page competitor battlecard a seller can skim in 60 seconds before a call and quote mid-call, tailored to role, GTM motion, buyer persona, deal stage and the competitor's status in the deal. Reads the brain and the alternatives map; proposes field notes back to Section 3. Trigger on: "battlecard for…
competitor-deep-dive
Builds a long-form, fully sourced deep-dive on one competitor for a named decision (strategy call, RFP, pricing change, roadmap bet): dated reviews, deal evidence, recent moves, head-to-head per offering, where they win, and a block the battlecard reads. Triggers: "deep-dive on [competitor]", "full competitor…
beachhead-segment
Identifies and scores your highest-priority beachhead segment using four-dimension scoring (Burning Pain, Willingness to Pay, Winnability, Referral Potential) with blocking gates. Reads brain context (ICP, alternatives & positioning, proof points) and, when available, guardrails from prior beachhead decisions the user…
workflow-orchestrator
Orchestrates multi-skill PMM programs end-to-end — chains positioning, competitive, GTM strategy, campaign briefs, stakeholder maps, and retros into one coherent program with a master document and brain updates. Trigger on: "run full GTM workflow", "run a full launch", "launch [product] end to end", "positioning…