s4h-network-effects

s4h-network-effects is a skill for Claude Code from human-avatar/skills-for-humanity. It costs 95 tokens per session (2,018 once invoked), scanned A, original, MIT.

A network-analysis skill for studying network effects, where a product or service becomes more valuable as more people participate.

In plain words
What is it for?
Use it to assess participation-driven growth, critical mass, competitive defensibility, or winner-take-all market dynamics.
Why use it?
It helps explain tipping points, lock-in, and why some competitors or platforms become difficult to replace.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: names the AskUserQuestion tool.

Part of the skills-for-humanity plugin — 197 skills, 1 hook shipped together

Good fit Use it to assess participation-driven growth, critical mass, competitive defensibility, or winner-take-all market dynamics.

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Install with agentmods
npx agentmods add skills/human-avatar/skills-for-humanity/s4h-network-effects
Install

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.

Any agent
npx skills add human-avatar/skills-for-humanity --skill s4h-network-effects
Clone the repo
git clone --depth 1 https://github.com/human-avatar/skills-for-humanity

Made for: Claude Code.

Or install skills-for-humanity, the plugin that ships this one along with the rest of its 197 skills, 1 hook.

Wrote 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.

agentmods badge for s4h-network-effects

README.md
[![agentmods](https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-network-effects/github.svg)](https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-network-effects)
Your own site
<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-network-effects"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-network-effects/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.

agentmods 80×15 button for s4h-network-effects

Your own site · 80×15
<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-network-effects"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-network-effects.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 95 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,018 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00095 $0.02018
Opus 5 $0.00048 $0.01009
Sonnet 5 $0.00019 $0.00404
Haiku 4.5 $0.00010 $0.00202

Measured 9d ago against content hash 4b9e16bd2843, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

s4h-network-effects 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 9d 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.

skills/s4h-network-effects/SKILL.md · 134 lines

How it starts

The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Network: Network Effects

Robert Metcalfe observed in 1980 that the value of a telecommunications network scales with the square of the number of connected users. One fax machine is worthless; two fax machines form one connection; a thousand fax machines form almost half a million connections. This relationship — value growing superlinearly with participation — is now called Metcalfe's Law, and it describes a mechanism that creates some of the most durable competitive positions in the economy.

Network effects are not a single phenomenon. At least four distinct types operate through different mechanisms, have different tipping points, and create different levels of defensibility. Direct network effects: each additional user directly increases value for all other users (messaging apps, social networks, communication protocols). Indirect network effects: growth on one side of a market attracts valuable participants on the complementary side (more buyers attract more sellers; more developers attract more users). Data network effects: more usage generates data that improves the product, which attracts more usage. Local network effects: value depends on connections within a subgraph, not the whole network — so the product tips locally before it tips globally.

The strategic implications are profound and often misread. Tipping points exist below which adoption dies and above which it accelerates toward dominance. Winner-take-all dynamics emerge when the network effect is global, switching costs are high, and there are no structural holes that a challenger could occupy. But many "network effect" businesses are actually winner-take-most — local or niche sub-networks can sustain competitors. The difference between these two structures determines the viable competitive strategy.


Your Process

Step 1: Identify the Network Effect Type For this product or business, ask: why does each additional user create value? For whom? Through what mechanism? Map against the four types:

  • Direct (same-side): Users connect to other users; value is in the connections themselves
  • Indirect (cross-side): Users on one side attract or benefit users on the other side
  • Data: More usage improves the algorithm, recommendations, or intelligence that serves all users
  • Local: Value depends on connections within a subgraph; the network tips locally before globally

Read the full file on GitHub · 134 lines

Changes

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.

  1. 9d ago First seen · 134 lines · 95 tokens per session scan A 4b9e16bd2843

Subscribe to this mod's changes

s4h-network-effects is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 95 tokens to every session and 2,018 once invoked, about $0.0005 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 2026-09-03.

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