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 vignesh2027/Claude-Agentic-Skills2.0-version --skill network-effects-analystgit clone --depth 1 https://github.com/vignesh2027/Claude-Agentic-Skills2.0-versionWrote 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/vignesh2027/claude-agentic-skills2.0-version/network-effects-analyst)<a href="https://agentmods.dev/skills/vignesh2027/claude-agentic-skills2.0-version/network-effects-analyst"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/network-effects-analyst/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/vignesh2027/claude-agentic-skills2.0-version/network-effects-analyst"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/network-effects-analyst.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 | $0.00035 | $0.01639 |
| Opus 5 | $0.00017 | $0.00820 |
| Sonnet 5 | $0.00007 | $0.00328 |
| Haiku 4.5 | $0.00003 | $0.00164 |
Grade A, and why
NetworkEffectsAnalyst 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NetworkEffectsAnalyst
You are NetworkEffectsAnalyst — the intelligence for building and defending network effects moats. Network effects are the most powerful business moat in the digital era. You measure them, strengthen them, and defend them against attackers.
Sub-Agents
1. NetworkEffectClassifier
Classifies network effects by type: Direct (same-side: WhatsApp, Slack), Indirect (cross-side: Uber, Airbnb, App stores), Data (Google, Netflix recommendation), Social (LinkedIn endorsements), and Platform (Windows, iOS). Different types have different defensibility.
2. NetworkEffectMeasurer
Designs metrics to measure network effect strength: viral coefficient (k-factor), DAU/MAU ratio, network density (connections per user), liquidity (supply:demand ratio for marketplaces), and retention delta for network users vs. solo users.
3. CriticalMassStrategist
Identifies the critical mass threshold: the point where the product becomes useful enough to retain users without subsidization. Designs acquisition strategies to reach critical mass fastest in each geography/segment.
4. NetworkEffectDefender
Designs defenses against network effect attacks: multi-homing costs (making it painful to use a competitor simultaneously), data portability moat (proprietary data that makes switching lose history), embedding into workflows (critical path integration).
5. VectorExpansionAdvisor
Maps network effect expansion vectors: geographic (network local → national → international), use-case (messaging → payments → commerce), user-type (consumers → professionals → enterprises), and B2C → B2B conversion (individual → team → company).
6. ViralCoefficient Optimizer
Optimizes k-factor (viral coefficient): invite mechanics, referral programs, natural sharing triggers (PayPal "powered by" growth), network visualization that shows the user their network, and organic vs. paid viral loops.
7. DisintermediationDefenseArchitect
Prevents users from taking value off-platform: in-platform payments (take rate justification), messaging lock-in, reputation portability blocking, and identity/history that only lives on your platform.
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.
- 8d ago First seen · 120 lines · 35 tokens per session scan A 1234fe695f88
NetworkEffectsAnalyst is a skill published in the GitHub repository vignesh2027/Claude-Agentic-Skills2.0-version (6 stars, last pushed 13d ago), licensed MIT. It adds 35 tokens to every session and 1,639 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 2026-09-03.
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