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 jeffreytse/grimoire-core --skill apply-diffusion-of-innovationsgit clone --depth 1 https://github.com/jeffreytse/grimoire-coreWrote 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/jeffreytse/grimoire-core/apply-diffusion-of-innovations)<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-diffusion-of-innovations"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-diffusion-of-innovations/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/jeffreytse/grimoire-core/apply-diffusion-of-innovations"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-diffusion-of-innovations.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 | $0.00074 | $0.02101 |
| Opus 5.5 | $0.00030 | $0.00840 |
| Sonnet 5.5 | $0.00015 | $0.00420 |
| Haiku 4.5 | $0.00007 | $0.00210 |
Grade A, and why
apply-diffusion-of-innovations 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 1mo 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apply Diffusion of Innovations
Classify the target population into adopter categories and diagnose an innovation's relative advantage, compatibility, complexity, trialability, and observability to identify what's actually slowing adoption, rather than assuming price or awareness alone explain a slow adoption curve.
Why This Is Best Practice
Why best: A slow adoption rate is often misdiagnosed as a pricing or awareness problem when the actual barrier is one of five specific, independently diagnosable attributes of the innovation itself. Rogers' framework separates the population (who adopts, and in what order) from the innovation's own characteristics (what makes it adopt faster or slower), giving two independent diagnostic axes instead of one vague "the market isn't ready" explanation.
Adopted by: The adopter-category percentile distribution (roughly 2.5% innovators, 13.5% early adopters, 34% early majority, 34% late majority, 16% laggards) has been empirically validated across decades of adoption studies spanning agricultural technology, public health interventions (the framework's original application, developed to explain the spread of new farming and health practices), and consumer technology adoption. Geoffrey Moore's "Crossing the Chasm" (1991) — the foundational go-to-market text this repo's own design-go-to-market-strategy and design-go-to-market skills already build on — is itself a direct extension of Rogers' adopter categories, applying them specifically to the gap between early adopters and the early majority in technology markets.
Impact: Public-health diffusion studies using Rogers' framework have identified specific attribute failures (low trialability, poor compatibility with existing practice) as the actual cause of slow adoption in cases where awareness campaigns alone failed to move adoption rates — redirecting intervention design toward the actual barrier (e.g., enabling low-commitment trial) rather than more messaging. The same diagnostic separates, in a technology context, "nobody knows about this" from "people know about it but it doesn't fit how they already work" — two problems with entirely different fixes.
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.
- 1mo ago First seen · 93 lines · 74 tokens per session scan A b23241142fb7
apply-diffusion-of-innovations is a skill published in the GitHub repository jeffreytse/grimoire-core (5 stars, last pushed 5d ago), licensed MIT. It adds 74 tokens to every session and 2,101 once invoked, about $0.0003 per session on Opus 5.5. 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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