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 deciqAI/knowledge-skills --skill network-effectsgit clone --depth 1 https://github.com/deciqAI/knowledge-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/deciqai/knowledge-skills/network-effects)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/network-effects"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/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.
<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/network-effects"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/network-effects.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00138 | $0.02177 |
| Opus 5 | $0.00069 | $0.01089 |
| Sonnet 5 | $0.00028 | $0.00435 |
| Haiku 4.5 | $0.00014 | $0.00218 |
Grade A, and why
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.
How it starts
The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Network Effects
Overview
Some products get more valuable the more people use them — not because the company gets cheaper at scale, but because each user makes the product more useful to every other user. This is the network effect: value per user is an increasing function of total user count. Most products that claim this don't have it; they have scale economies, virality, or social proof — valuable, but structurally different. The skill diagnoses which is which, estimates the critical-mass threshold, and designs for amplification.
Composes with s-curve-technology-adoption, pmf-crossing-the-chasm, feedback-loops, and signaling-games.
When to Use
- A pitch or strategy document claims "network effects" as a moat — most don't survive scrutiny
- Building a marketplace, social product, communication tool, or platform; need to model when the dynamic activates
- Evaluating whether a competitor's network-effect claim is structural or rhetorical
- Suspecting you have scale effects but not network effects — the strategic difference is large
- Auditing an AI moat claim — CUDA/developer ecosystems, model or app marketplaces, "data flywheels," AI-capex or AI-bubble debates — where genuine network effects blur with chip-scale economies and export-control-fragmented markets
When NOT to use: standard B2B SaaS with no inter-customer interaction; the "effect" is lower cost at scale; single-player product with no user-generated value.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete product/case → run The Process directly.
- Coach mode: user is unfamiliar or has no concrete case → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- One-line what-it-is: some products get more valuable as more people use them because users create value for each other — but most claiming this have ordinary scale or viral growth, which look similar but behave differently.
- Check fit against When to Use / When NOT to use. If single-player, point elsewhere.
- Elicit their real case — a specific product or strategic claim, not a hypothetical.
[WAIT — do not advance until user responds]
- Walk the diagnostic one question at a time: who interacts with whom, what is the per-user value formula, what happens at 10x users, what is the critical-mass threshold.
[WAIT — do not advance until user responds]
- Close with the verdict: "true network effects + strategic implication" or "scale/viral effects but not network effects + strategic implication."
[WAIT — do not advance until user responds]
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 124 lines · 138 tokens per session scan A 985bba951a64
network-effects is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 138 tokens to every session and 2,177 once invoked, about $0.0007 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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