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 ideal-customer-profilegit 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/ideal-customer-profile)<a href="https://agentmods.dev/skills/phuryn/pm-skills/ideal-customer-profile"><img src="https://agentmods.dev/badge/skills/phuryn/pm-skills/ideal-customer-profile/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/ideal-customer-profile"><img src="https://agentmods.dev/badge/skills/phuryn/pm-skills/ideal-customer-profile.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 | $0.00047 | $0.01231 |
| Opus 5 | $0.00023 | $0.00616 |
| Sonnet 5 | $0.00009 | $0.00246 |
| Haiku 4.5 | $0.00005 | $0.00123 |
Grade A, and why
ideal-customer-profile 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 12d 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:
- ideal-customer-profile — 100% identical, 0 lines differ
- ideal-customer-profile — 100% identical, 0 lines differ
- ideal-customer-profile — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ideal Customer Profile
Overview
Identify your Ideal Customer Profile (ICP) from research and survey data. This skill synthesizes customer research to define the customer most likely to find value, retain, and expand with your product.
When to Use
- Defining ICP from product-market fit survey data
- Targeting high-value customer segments
- Analyzing customer success and expansion patterns
- Prioritizing sales and marketing efforts
- Evaluating new customer opportunities for fit
- Refining target market definition
ICP Framework Components
Demographics
Who are they from a firmographic and personal perspective?
- Company size (employees, revenue)
- Industry or vertical
- Geographic location
- Job title and department
- Years of experience in role
- Education and background
- Organizational structure and reporting
Behaviors
How do they work and make decisions?
- How they discover and evaluate solutions
- Buying process and decision-making timeline
- Technical literacy and product adoption speed
- Collaboration style (solo decision vs committee)
- Change management and adoption style
- Tool switching frequency
- Community involvement and peer influence
Jobs to Be Done (JTBD)
What are they trying to accomplish?
- Primary job/goal they're trying to achieve
- Secondary jobs that support the primary job
- Emotional jobs (how they want to feel)
- Social jobs (status and perception)
- Jobs they avoid or want to eliminate
- Frequency and importance of each job
- Success metrics for completing job
Needs and Pain Points
What problems does your product solve?
- Specific pain points they experience
- Current workarounds and limitations
- Impact on productivity or outcomes
- Cost or time burden of the problem
- Emotional frustration levels
- Barriers to solving the problem
- Available budget to solve
- Competing priorities
How It Works
Step 1: Gather Customer Data
Collect research about actual and potential customers:
- Product-market fit survey responses
- Customer interview transcripts
- Trial or freemium user behavior data
- Customer feedback and support tickets
- Churn analysis and customer lifecycle data
- Win/loss analysis from sales
- Competitor customer analysis
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.
- 12d ago First seen · 165 lines · 47 tokens per session scan A 8d434df786db
ideal-customer-profile is a skill published in the GitHub repository phuryn/pm-skills (26,161 stars, last pushed 2mo ago), licensed MIT. It adds 47 tokens to every session and 1,231 once invoked, about $0.0002 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.
Other skills, from other repositories
pmm-resume
Resume reviewer and tailoring engine for Product Marketing Managers (IC to VP, including AI PMM roles). Takes baseline resume + job description → dissects JD → ranks bullets by impact fit → rebuilds complete resume in one pass. Trigger on: resume + JD paste, "tailor this", "which bullets for this role", "rebuild for…
prd
Guides Product Managers and Product Marketing Managers to co-create complete Product Requirements Documents with embedded Solution Stories. Reads brain context (positioning, ICP, Revenue Levers) to anchor PRDs in strategy. Outputs: structured Solution Story for GTM communications + full PRD for execution alignment.
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…
stakeholder-maps
Builds political maps (not org charts) showing who can kill your launch, who champions it, and what to say to each stakeholder. Reads brain context (ICP, positioning, GTM motion) and guardrails from prior stakeholder mapping sessions; produces Power × Interest grid with political role assignment, conflict map, and…
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…