Wasp is a full-stack web framework for building React and Node.js applications with declarative code, while Prisma handles database access. It is for developers who want built-in features such as authentication, background jobs, email, RPC, type safety, and deployment in one application framework.
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 wasp-lang/wasp --skill geo-fundamentalsgit clone --depth 1 https://github.com/wasp-lang/waspWrote 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/wasp-lang/wasp/geo-fundamentals)<a href="https://agentmods.dev/skills/wasp-lang/wasp/geo-fundamentals"><img src="https://agentmods.dev/badge/skills/wasp-lang/wasp/geo-fundamentals/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/wasp-lang/wasp/geo-fundamentals"><img src="https://agentmods.dev/badge/skills/wasp-lang/wasp/geo-fundamentals.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 | $0.00024 | $0.00857 |
| Opus 5 | $0.00012 | $0.00428 |
| Sonnet 5 | $0.00005 | $0.00171 |
| Haiku 4.5 | $0.00002 | $0.00086 |
Grade A, and why
geo-fundamentals 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 12d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- geo-fundamentals — 100% identical, 0 lines differ
- geo-fundamentals — 100% identical, 0 lines differ
- geo-fundamentals — 100% identical, 0 lines differ
- geo-fundamentals — 100% identical, 0 lines differ
- geo-fundamentals — 100% identical, 0 lines differ
- geo-fundamentals — 97% identical, 1 lines differ
- geo-fundamentals — 94% identical, 2 lines differ
- geo-fundamentals — 91% identical, 13 lines differ
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GEO Fundamentals
Optimization for AI-powered search engines.
1. What is GEO?
GEO = Generative Engine Optimization
| Goal | Platform |
|---|---|
| Be cited in AI responses | ChatGPT, Claude, Perplexity, Gemini |
SEO vs GEO
| Aspect | SEO | GEO |
|---|---|---|
| Goal | #1 ranking | AI citations |
| Platform | AI engines | |
| Metrics | Rankings, CTR | Citation rate |
| Focus | Keywords | Entities, data |
2. AI Engine Landscape
| Engine | Citation Style | Opportunity |
|---|---|---|
| Perplexity | Numbered [1][2] | Highest citation rate |
| ChatGPT | Inline/footnotes | Custom GPTs |
| Claude | Contextual | Long-form content |
| Gemini | Sources section | SEO crossover |
3. RAG Retrieval Factors
How AI engines select content to cite:
| Factor | Weight |
|---|---|
| Semantic relevance | ~40% |
| Keyword match | ~20% |
| Authority signals | ~15% |
| Freshness | ~10% |
| Source diversity | ~15% |
4. Content That Gets Cited
| Element | Why It Works |
|---|---|
| Original statistics | Unique, citable data |
| Expert quotes | Authority transfer |
| Clear definitions | Easy to extract |
| Step-by-step guides | Actionable value |
| Comparison tables | Structured info |
| FAQ sections | Direct answers |
5. GEO Content Checklist
Content Elements
- Question-based titles
- Summary/TL;DR at top
- Original data with sources
- Expert quotes (name, title)
- FAQ section (3-5 Q&A)
- Clear definitions
- "Last updated" timestamp
- Author with credentials
Technical Elements
- Article schema with dates
- Person schema for author
- FAQPage schema
- Fast loading (< 2.5s)
- Clean HTML structure
6. Entity Building
| Action | Purpose |
|---|---|
| Google Knowledge Panel | Entity recognition |
| Wikipedia (if notable) | Authority source |
| Consistent info across web | Entity consolidation |
| Industry mentions | Authority signals |
What ships with it
1 file 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.
- 12d ago First seen · 157 lines · 24 tokens per session scan A 2ee830fe6808
geo-fundamentals is a skill published in the GitHub repository wasp-lang/wasp (18,727 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 857 once invoked, about $0.0001 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 2026-08-30.
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