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 beachhead-segmentgit 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/beachhead-segment)<a href="https://agentmods.dev/skills/unixcrh/phuryn-pm-skills/beachhead-segment"><img src="https://agentmods.dev/badge/skills/unixcrh/phuryn-pm-skills/beachhead-segment/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/beachhead-segment"><img src="https://agentmods.dev/badge/skills/unixcrh/phuryn-pm-skills/beachhead-segment.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 | $0.00058 | $0.01210 |
| Opus 5 | $0.00029 | $0.00605 |
| Sonnet 5 | $0.00012 | $0.00242 |
| Haiku 4.5 | $0.00006 | $0.00121 |
Grade A, and why
beachhead-segment 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 11d 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 beachhead-segment — 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Beachhead Segment
Overview
Identify the first beachhead market segment for product launch. This skill evaluates potential market segments against key criteria to find your initial winning segment that enables fast PMF validation and adjacent expansion.
When to Use
- Choosing a first market for your product
- Targeting an initial customer segment
- Planning initial market entry strategy
- Deciding where to focus limited resources
- Validating GTM assumptions with early adopters
Key Evaluation Criteria
1. Burning Pain Point
Does this segment experience an acute, unmet problem?
- Daily frustration with the status quo
- Significant productivity loss or cost impact
- Emotional urgency to find a solution
- Current workarounds are expensive or fragile
- Problem is getting worse over time
2. Willingness to Pay
Does this segment have budget and motivation to pay for a solution?
- Documented budget allocation for this problem area
- ROI is clear and compelling (value > cost)
- Economic impact of problem justifies solution cost
- Decision-maker has autonomy or influence over budget
- No free or DIY alternatives that fully satisfy need
3. Winnable Market Share
Can you realistically capture 60-70% of this segment in 3-18 months?
- Segment is large enough but not oversaturated
- Limited competition or easy differentiation
- Market players are fragmented or complacent
- Your product has clear competitive advantage
- You have unique access or distribution advantage
4. Referral Potential
Will customers naturally refer or recommend to others?
- Segment contains professional communities
- Customers interact with adjacent segments (expansion opportunity)
- High word-of-mouth culture in this industry
- Network effects within the segment
- Solving problem for one creates demand in adjacent segments
How It Works
Step 1: List Potential Segments
Brainstorm all possible target segments:
- Industry verticals (SaaS, healthcare, manufacturing, etc.)
- Company size (SMB, mid-market, enterprise)
- Job titles or roles
- Geographic regions
- Use cases or use-case variations
- Customer maturity level
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.
- 11d ago First seen · 146 lines · 58 tokens per session scan A a3f94e98c053
beachhead-segment is a skill published in the GitHub repository unixcrh/phuryn-pm-skills (2 stars, last pushed 6mo ago), licensed MIT. It adds 58 tokens to every session and 1,210 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to beachhead-segment, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…