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 s-curve-technology-adoptiongit 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/s-curve-technology-adoption)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/s-curve-technology-adoption"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/s-curve-technology-adoption/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/s-curve-technology-adoption"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/s-curve-technology-adoption.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 | $0.00151 | $0.02695 |
| Opus 5 | $0.00076 | $0.01347 |
| Sonnet 5 | $0.00030 | $0.00539 |
| Haiku 4.5 | $0.00015 | $0.00269 |
Grade A, and why
s-curve-technology-adoption 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
S-Curve Technology Adoption
Overview
Innovations spread on a sigmoid (S-shaped) curve: slow → accelerating → leveling off at saturation. The shape is universal: a reinforcing word-of-mouth loop drives growth; a balancing saturation loop caps it. Ryan & Gross (1943, Iowa hybrid corn) produced the first quantitative S-curve. Rogers (1962) codified five adopter categories: innovators (2.5%), early adopters (13.5%), early majority (34%), late majority (34%), laggards (16%) — each behaviorally distinct. Strategic core: what works to recruit one category fails for the next.
Composes with: feedback-loops · pmf-crossing-the-chasm · pricing-strategy · aarrr-pirate-metrics
When to Use
- Growth stalling after early success; need to diagnose why
- Planning a launch requiring sequenced strategy across adopter categories
- Marketing-fit breaking — channels, messaging, or pricing that worked are no longer working
- Forecasting market size and saturation timing; "when will this market peak?"
- Debating whether a technology is at inflection or saturation — e.g. "is genAI an AI bubble or just getting started?", separating the adoption S-curve from the capability/scaling curve, sizing AI capex bets against adoption phase
When NOT to use: mature saturated market (diffusion already played out); adoption driven by regulatory mandate; exogenous constraint caps the market; too little data to distinguish real diagnosis from curve-fitting.
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: new technology doesn't spread at a steady pace — it goes slow, then fast, then slow again in an S-shape, and the people who adopt early are completely different from those who adopt late, which means your sales and marketing strategy must change as you move along the curve.
- Check fit: does the user have an innovation spreading through a population (not a mandated rollout, not a saturated market)?
- Elicit their real case — what product, what adoption data do they have, where do they think they are?
[WAIT — do not advance until user responds]
- Run The Process one step at a time: locate on curve, confirm with customer signals, estimate ceiling, project trajectory, identify next category, audit strategy.
[WAIT — do not advance until user responds]
- Close by naming the one strategy shift required for the next adopter category in their specific situation.
[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 · 126 lines · 151 tokens per session scan A 5439fd6e8649
s-curve-technology-adoption is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 11d ago), licensed MIT. It adds 151 tokens to every session and 2,695 once invoked, about $0.0008 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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