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-storiesgit 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-stories)<a href="https://agentmods.dev/skills/phuryn/pm-skills/user-stories"><img src="https://agentmods.dev/badge/skills/phuryn/pm-skills/user-stories/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-stories"><img src="https://agentmods.dev/badge/skills/phuryn/pm-skills/user-stories.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector 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 | $0.00051 | $0.00655 |
| Opus 5 | $0.00026 | $0.00328 |
| Sonnet 5 | $0.00010 | $0.00131 |
| Haiku 4.5 | $0.00005 | $0.00065 |
Grade A, and why
user-stories 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 10d 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
3 near-identical copies found in the catalogue:
- user-stories — 100% identical, 0 lines differ
- user-stories — 100% identical, 0 lines differ
- user-stories — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Stories
Create user stories following the 3 C's (Card, Conversation, Confirmation) and INVEST criteria. Generates stories with descriptions, design links, and acceptance criteria.
Use when: Writing user stories, breaking down features into stories, creating backlog items, or defining acceptance criteria.
Arguments:
$PRODUCT: The product or system name$FEATURE: The new feature to break into stories$DESIGN: Link to design files (Figma, Miro, etc.)$ASSUMPTIONS: Key assumptions or context
Step-by-Step Process
- Analyze the feature based on provided design and context
- Identify user roles and distinct user journeys
- Apply 3 C's framework:
- Card: Simple title and one-liner
- Conversation: Detailed discussion of intent
- Confirmation: Clear acceptance criteria
- Respect INVEST criteria: Independent, Negotiable, Valuable, Estimable, Small, Testable
- Use plain language a primary school graduate can understand
- Link to design files for visual reference
- Output user stories in structured format
Story Template
Title: [Feature name]
Description: As a [user role], I want to [action], so that [benefit].
Design: [Link to design files]
Acceptance Criteria:
- [Clear, testable criterion]
- [Observable behavior]
- [System validates correctly]
- [Edge case handling]
- [Performance or accessibility consideration]
- [Integration point]
Example User Story
Title: Recently Viewed Section
Description: As an Online Shopper, I want to see a 'Recently viewed' section on the product page to easily revisit items I considered.
Design: [Figma link]
Acceptance Criteria:
- The 'Recently viewed' section is displayed at the bottom of the product page for every user who has previously viewed at least 1 product.
- It is not displayed for users visiting the first product page of their session.
- The current product itself is excluded from the displayed items.
- The section showcases product cards or thumbnails with images, titles, and prices.
- Each product card indicates when it was viewed (e.g., 'Viewed 5 minutes ago').
- Clicking on a product card leads the user to the corresponding product page.
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.
- 10d ago First seen · 75 lines · 51 tokens per session scan A 8ce89cd81de5
user-stories is a skill published in the GitHub repository phuryn/pm-skills (26,131 stars, last pushed 2mo ago), licensed MIT. It adds 51 tokens to every session and 655 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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prd
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pre-mortem
Identifies and pressure-tests failure modes for any strategic initiative (product launch, pricing change, GTM pivot, new market entry, feature rollout) by running a cross-functional risk exercise. Loads brain context (ICP, positioning, competitive landscape) and, when available, guardrails from prior pre-mortems…
prioritization-frameworks
Selects and applies the right prioritization framework (9 frameworks: Opportunity Score, ICE, RICE, Eisenhower, Impact vs Effort, Risk vs Reward, Kano, Weighted Decision Matrix, MoSCoW) with PMM interpretation layer and GTM launch tier output (T1–T4). Reads brain context (ICP, positioning, revenue levers) and, when…
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, positioning, competitive landscape, proof points) and, when available, guardrails from prior beachhead decisions…
go-to-market-strategy
Assigns launch tier (T1–T4) using a four-signal framework and generates a complete GTM brief with positioning angles, channel strategy, success metrics, and competitive context. Reads brain (ICP, positioning, competitive, proof points) and, when available, guardrails from prior launches the user has logged.