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-digital-divide-diagnosticgit 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-digital-divide-diagnostic)<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-digital-divide-diagnostic"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-digital-divide-diagnostic/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-digital-divide-diagnostic"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-digital-divide-diagnostic.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 | $0.00075 | $0.01551 |
| Opus 5.5 | $0.00030 | $0.00620 |
| Sonnet 5.5 | $0.00015 | $0.00310 |
| Haiku 4.5 | $0.00007 | $0.00155 |
Grade A, and why
apply-digital-divide-diagnostic 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apply Digital Divide Diagnostic
Diagnose which of four independent access barriers — motivational, physical/material, skills, or usage — is actually blocking a target population's adoption of a digital product or program, and match the intervention to that specific barrier, rather than defaulting to a generic "more devices" or "more marketing" response.
Why This Is Best Practice
Why best: Low adoption in an underserved population is often treated as a single undifferentiated problem ("they don't have access"), but van Dijk's model separates it into four distinct, independently diagnosable barriers, each requiring a different fix. Treating all four as the same problem means an intervention aimed at the wrong barrier (e.g., subsidized devices for a population that already has devices but lacks the skills or motivation to use them) produces no adoption gain despite real investment.
Adopted by: The ITU's "Measuring Digital Development" reporting and Pew Research Center's ongoing digital-divide survey series are both structured around this same multi-stage distinction, and both have repeatedly found that the binding constraint varies by population and shifts over time — in many developed-market contexts, physical/material access (device and connectivity availability) is no longer the dominant barrier; skills and motivational access frequently are. This empirical variability is exactly what makes the diagnostic necessary rather than assuming one universal barrier.
Impact: Programs that diagnosed the actual binding barrier before designing an intervention — for example, discovering that a population already has device access but lacks confidence or relevant use cases (motivational/skills barriers), not connectivity — have redirected investment from further device subsidy toward digital-literacy training or locally relevant content, which more directly addresses the actual constraint. Programs that skip this diagnosis and default to device or connectivity investment in a population where that isn't the binding barrier see limited adoption gains despite the spending.
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 · 80 lines · 75 tokens per session scan A 16a9f037d4a4
apply-digital-divide-diagnostic is a skill published in the GitHub repository jeffreytse/grimoire-core (5 stars, last pushed 5d ago), licensed MIT. It adds 75 tokens to every session and 1,551 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.
Other skills, from other repositories
semantic-model-disambiguation
Analyze Power BI semantic models for column-level overlaps that confuse Copilot and Fabric data agents. Detect ambiguity, review with domain expert, apply fixes via MCP.
prompt-2-data
Generate comprehensive synthetic relational data for any specified subject with multiple normalized CSV files maintaining referential integrity.
academic-paper-drafting
End-to-end academic paper drafting for CHI, HBR, journals, and conferences with venue-specific templates, drafting workflows, and revision strategies.
research-first-development
Build knowledge bases that build software — research before code, teach before execute.
infrastructure-as-code
Domain: DevOps & Cloud Engineering.
agent-governance
Patterns for adding safety, trust, and policy enforcement to AI agent systems -- control which tools agents can call, what content they process, and maintain accountability through audit trails.