diff --git a/docs/integrations/jetski-cortex.md b/docs/integrations/jetski-cortex.md
new file mode 100644
index 00000000..270d542c
--- /dev/null
+++ b/docs/integrations/jetski-cortex.md
@@ -0,0 +1,238 @@
+---
+title: Jetski/Cortex + Gemini Integration Guide
+description: "Come usare antigravity-awesome-skills con Jetski/Cortex evitando l’overflow di contesto con 1.200+ skill."
+---
+
+# Jetski/Cortex + Gemini: integrazione sicura con 1.200+ skill
+
+Questa guida mostra come integrare il repository `antigravity-awesome-skills` con un agente basato su **Jetski/Cortex + Gemini** (o framework simili) **senza superare il context window** del modello.
+
+L’errore tipico visto in Jetski/Cortex è:
+
+> `TrajectoryChatConverter: could not convert a single message before hitting truncation`
+
+Il problema non è nelle skill, ma **nel modo in cui vengono caricate**.
+
+---
+
+## 1. Antipattern da evitare
+
+Non bisogna mai:
+
+- leggere **tutte** le directory `skills/*/SKILL.md` all’avvio;
+- concatenare il contenuto di tutte le `SKILL.md` in un singolo system prompt;
+- reiniettare l’intera libreria per **ogni** richiesta.
+
+Con oltre 1.200 skill, questo approccio riempie il context window prima ancora di aggiungere i messaggi dell’utente, causando l’errore di truncation.
+
+---
+
+## 2. Pattern raccomandato
+
+Principi chiave:
+
+- **Manifest leggero**: usare `data/skills_index.json` per sapere *quali* skill esistono, senza caricare i testi completi.
+- **Lazy loading**: leggere `SKILL.md` **solo** per le skill effettivamente invocate in una conversazione (es. quando compare `@skill-id`).
+- **Limiti espliciti**: imporre un massimo di skill/tokens caricati per turno, con fallback chiari.
+
+Il flusso consigliato è:
+
+1. **Bootstrap**: all’avvio dell’agente leggere `data/skills_index.json` e costruire una mappa `id -> meta`.
+2. **Parsing dei messaggi**: prima di chiamare il modello, estrarre tutti i riferimenti `@skill-id` dai messaggi utente/sistema.
+3. **Risoluzione**: mappare gli id trovati in oggetti `SkillMeta` usando la mappa di bootstrap.
+4. **Lazy load**: leggere i file `SKILL.md` solo per questi id (fino a un massimo configurabile).
+5. **Prompt building**: costruire i system messages del modello includendo solo le definizioni delle skill selezionate.
+
+---
+
+## 3. Struttura di `skills_index.json`
+
+Il file `data/skills_index.json` è un array di oggetti, ad esempio:
+
+```json
+{
+  "id": "brainstorming",
+  "path": "skills/brainstorming",
+  "category": "planning",
+  "name": "brainstorming",
+  "description": "Use before any creative or constructive work.",
+  "risk": "safe",
+  "source": "official",
+  "date_added": "2026-02-27"
+}
+```
+
+Campi chiave:
+
+- **`id`**: identificatore usato nelle menzioni `@id` (es. `@brainstorming`).
+- **`path`**: directory che contiene la `SKILL.md` (es. `skills/brainstorming/`).
+
+Per ottenere il percorso alla definizione della skill:
+
+- `fullPath = path.join(SKILLS_ROOT, meta.path, "SKILL.md")`.
+
+> Nota: `SKILLS_ROOT` è la directory radice dove avete installato il repository (es. `~/.agent/skills`).
+
+---
+
+## 4. Pseudo‑codice di integrazione (TypeScript)
+
+> Esempio completo in: [`examples/jetski-gemini-loader/`](../../examples/jetski-gemini-loader/).
+
+### 4.1. Tipi di base
+
+```ts
+type SkillMeta = {
+  id: string;
+  path: string;
+  name: string;
+  description?: string;
+  category?: string;
+  risk?: string;
+};
+```
+
+### 4.2. Bootstrap: caricare il manifest
+
+```ts
+function loadSkillIndex(indexPath: string): Map<string, SkillMeta> {
+  const raw = fs.readFileSync(indexPath, "utf8");
+  const arr = JSON.parse(raw) as SkillMeta[];
+  const map = new Map<string, SkillMeta>();
+  for (const meta of arr) {
+    map.set(meta.id, meta);
+  }
+  return map;
+}
+```
+
+### 4.3. Parsing dei messaggi per trovare `@skill-id`
+
+```ts
+const SKILL_ID_REGEX = /@([a-zA-Z0-9-_./]+)/g;
+
+function resolveSkillsFromMessages(
+  messages: { role: string; content: string }[],
+  index: Map<string, SkillMeta>,
+  maxSkills: number
+): SkillMeta[] {
+  const found = new Set<string>();
+
+  for (const msg of messages) {
+    let match: RegExpExecArray | null;
+    while ((match = SKILL_ID_REGEX.exec(msg.content)) !== null) {
+      const id = match[1];
+      if (index.has(id)) {
+        found.add(id);
+      }
+    }
+  }
+
+  const metas: SkillMeta[] = [];
+  for (const id of found) {
+    const meta = index.get(id);
+    if (meta) metas.push(meta);
+    if (metas.length >= maxSkills) break;
+  }
+
+  return metas;
+}
+```
+
+### 4.4. Lazy loading dei file `SKILL.md`
+
+```ts
+async function loadSkillBodies(
+  skillsRoot: string,
+  metas: SkillMeta[]
+): Promise<string[]> {
+  const bodies: string[] = [];
+
+  for (const meta of metas) {
+    const fullPath = path.join(skillsRoot, meta.path, "SKILL.md");
+    const text = await fs.promises.readFile(fullPath, "utf8");
+    bodies.push(text);
+  }
+
+  return bodies;
+}
+```
+
+### 4.5. Costruzione del prompt Jetski/Cortex
+
+Pseudocodice per la fase di pre‑processing, prima del `TrajectoryChatConverter`:
+
+```ts
+async function buildModelMessages(
+  baseSystemMessages: { role: "system"; content: string }[],
+  trajectory: { role: "user" | "assistant" | "system"; content: string }[],
+  skillIndex: Map<string, SkillMeta>,
+  skillsRoot: string,
+  maxSkillsPerTurn: number
+): Promise<{ role: string; content: string }[]> {
+  const selectedMetas = resolveSkillsFromMessages(
+    trajectory,
+    skillIndex,
+    maxSkillsPerTurn
+  );
+
+  const skillBodies = await loadSkillBodies(skillsRoot, selectedMetas);
+
+  const skillMessages = skillBodies.map((body) => ({
+    role: "system" as const,
+    content: body,
+  }));
+
+  return [...baseSystemMessages, ...skillMessages, ...trajectory];
+}
+```
+
+> Suggerimento: aggiungete una stima dei token per troncare o riassumere i `SKILL.md` se il context window si avvicina al limite.
+
+---
+
+## 5. Gestione degli overflow di contesto
+
+Per evitare errori difficili da capire per l’utente, impostate:
+
+- una **soglia di sicurezza** (es. 70–80% del context window);
+- un **limite massimo di skill per turno** (es. 5–10).
+
+Strategie quando si supera la soglia:
+
+- ridurre il numero di skill incluse (es. in base a recenza o priorità); oppure
+- restituire un errore chiaro all’utente, ad esempio:
+
+> "Sono state richieste troppe skill in un singolo turno. Riduci il numero di `@skill-id` nel messaggio o dividili in più passaggi."
+
+---
+
+## 6. Scenari di test raccomandati
+
+- **Scenario 1 – Messaggio semplice ("hi")**
+  - Nessun `@skill-id` → nessuna `SKILL.md` caricata → il prompt rimane piccolo → nessun errore.
+- **Scenario 2 – Poche skill**
+  - Messaggio con 1–2 `@skill-id` → solo le relative `SKILL.md` vengono caricate → nessun overflow.
+- **Scenario 3 – Molte skill**
+  - Messaggio con molte `@skill-id` → si attiva il limite `maxSkillsPerTurn` o il controllo di token → nessun overflow silenzioso.
+
+---
+
+## 7. Sottoinsiemi di skill e bundle
+
+Per ulteriore controllo:
+
+- spostate le skill non necessarie in `skills/.disabled/` per escluderle in certi ambienti;
+- usate i **bundle** descritti in [`docs/users/bundles.md`](../users/bundles.md) per caricare solo gruppi tematici.
+
+---
+
+## 8. Riepilogo
+
+- Non concatenate mai tutte le `SKILL.md` in un singolo prompt.
+- Usate `data/skills_index.json` come manifest leggero.
+- Caricate le skill **on‑demand** in base a `@skill-id`.
+- Impostate limiti chiari (max skill per turno, soglia di token).
+
+Seguendo questo pattern, Jetski/Cortex + Gemini può usare l’intera libreria di `antigravity-awesome-skills` in modo sicuro, scalabile e compatibile con il context window dei modelli moderni.
+
diff --git a/docs/users/faq.md b/docs/users/faq.md
index 7b8bc8f8..8ebeb4e9 100644
--- a/docs/users/faq.md
+++ b/docs/users/faq.md
@@ -47,6 +47,20 @@ Start from:
 - ✅ Free for commercial use
 - ✅ You can modify them
 
+### How do these skills avoid overflowing the model context?
+
+Some host tools (for example custom agents built on Jetski/Cortex + Gemini) might be tempted to **concatenate every `SKILL.md` file into a single system prompt**.  
+This is **not** how this repository is designed to be used, and it will almost certainly overflow the model’s context window with 1,200+ skills.
+
+Instead, hosts should:
+
+- use `data/skills_index.json` as a **lightweight manifest** for discovery; and
+- load individual `SKILL.md` files **only when a skill is invoked** (e.g. via `@skill-id` in the conversation).
+
+For a concrete example (including pseudo‑code) see:
+
+- [`docs/integrations/jetski-cortex.md`](../integrations/jetski-cortex.md)
+
 ### Do skills work offline?
 
 The skill files themselves are stored locally on your computer, but your AI assistant needs an internet connection to function.
diff --git a/docs/users/usage.md b/docs/users/usage.md
index 4089e72f..d5cb6a5b 100644
--- a/docs/users/usage.md
+++ b/docs/users/usage.md
@@ -314,6 +314,19 @@ Usually no, but if your AI doesn't recognize a skill:
 2. Check the installation path matches your tool
 3. Try the explicit path: `npx antigravity-awesome-skills --claude` (or `--cursor`, `--gemini`, etc.)
 
+### "Can I load all skills into the model at once?"
+
+No. Even though you have 1,200+ skills installed locally, you should **not** concatenate every `SKILL.md` into a single system prompt or context block.
+
+The intended pattern is:
+
+- use `data/skills_index.json` (the manifest) to discover which skills exist; and
+- only load the `SKILL.md` files for the specific `@skill-id` values you actually use in a conversation.
+
+If you are building your own host/agent (e.g. Jetski/Cortex + Gemini), see:
+
+- [`docs/integrations/jetski-cortex.md`](../integrations/jetski-cortex.md)
+
 ### "Can I create my own skills?"
 
 Yes! Use the `@skill-creator` skill:
diff --git a/examples/jetski-gemini-loader/README.md b/examples/jetski-gemini-loader/README.md
new file mode 100644
index 00000000..9ffdd137
--- /dev/null
+++ b/examples/jetski-gemini-loader/README.md
@@ -0,0 +1,91 @@
+# Jetski + Gemini Lazy Skill Loader (Example)
+
+This example shows one way to integrate **antigravity-awesome-skills** with a Jetski/Cortex‑style agent using **lazy loading** based on `@skill-id` mentions, instead of concatenating every `SKILL.md` into the prompt.
+
+> This is **not** a production‑ready library – it is a minimal reference you can adapt to your own host/agent implementation.
+
+---
+
+## What this example demonstrates
+
+- How to:
+  - load the global manifest `data/skills_index.json` once at startup;
+  - scan conversation messages for `@skill-id` patterns;
+  - resolve those ids to entries in the manifest;
+  - read only the corresponding `SKILL.md` files from disk (lazy loading);
+  - build a prompt array with:
+    - your base system messages;
+    - one system message per selected skill;
+    - the rest of the trajectory.
+- How to enforce a **maximum number of skills per turn** via `maxSkillsPerTurn`.
+
+This pattern avoids context overflow when you have 1,200+ skills installed.
+
+---
+
+## Files
+
+- `loader.ts`
+  - Implements:
+    - `loadSkillIndex(indexPath)`;
+    - `resolveSkillsFromMessages(messages, index, maxSkills)`;
+    - `loadSkillBodies(skillsRoot, metas)`;
+    - `buildModelMessages({...})`.
+- See also the integration guide:
+  - [`docs/integrations/jetski-cortex.md`](../../docs/integrations/jetski-cortex.md)
+
+---
+
+## Basic usage (pseudo‑code)
+
+```ts
+import path from "path";
+import {
+  loadSkillIndex,
+  buildModelMessages,
+  Message,
+} from "./loader";
+
+const REPO_ROOT = "/path/to/antigravity-awesome-skills";
+const SKILLS_ROOT = REPO_ROOT;
+const INDEX_PATH = path.join(REPO_ROOT, "data", "skills_index.json");
+
+// 1. Bootstrap once at agent startup
+const skillIndex = loadSkillIndex(INDEX_PATH);
+
+// 2. Before calling the model, build messages with lazy‑loaded skills
+async function runTurn(trajectory: Message[]) {
+  const baseSystemMessages: Message[] = [
+    {
+      role: "system",
+      content: "You are a helpful coding agent.",
+    },
+  ];
+
+  const modelMessages = await buildModelMessages({
+    baseSystemMessages,
+    trajectory,
+    skillIndex,
+    skillsRoot: SKILLS_ROOT,
+    maxSkillsPerTurn: 8,
+  });
+
+  // 3. Pass `modelMessages` to your Jetski/Cortex + Gemini client
+  // e.g. trajectoryChatConverter.convert(modelMessages)
+}
+```
+
+Adapt the paths and model call to your environment.
+
+---
+
+## Important notes
+
+- **Do not** iterate through `skills/*/SKILL.md` and load everything at once.
+- This example:
+  - assumes skills live under the same repo root as `data/skills_index.json`;
+  - uses Node.js `fs`/`path` APIs and TypeScript types for clarity.
+- In a real host:
+  - wire `buildModelMessages` into the point where you currently assemble the prompt before `TrajectoryChatConverter`;
+  - add token‑counting / truncation logic if you want a stricter safety budget.
+
diff --git a/examples/jetski-gemini-loader/loader.ts b/examples/jetski-gemini-loader/loader.ts
new file mode 100644
index 00000000..1ed8970f
--- /dev/null
+++ b/examples/jetski-gemini-loader/loader.ts
@@ -0,0 +1,112 @@
+import fs from "fs";
+import path from "path";
+
+export type SkillMeta = {
+  id: string;
+  path: string;
+  name: string;
+  description?: string;
+  category?: string;
+  risk?: string;
+};
+
+export type Message = {
+  role: "system" | "user" | "assistant";
+  content: string;
+};
+
+const SKILL_ID_REGEX = /@([a-zA-Z0-9-_./]+)/g;
+
+export function loadSkillIndex(indexPath: string): Map<string, SkillMeta> {
+  const raw = fs.readFileSync(indexPath, "utf8");
+  const arr = JSON.parse(raw) as SkillMeta[];
+  const map = new Map<string, SkillMeta>();
+
+  for (const meta of arr) {
+    map.set(meta.id, meta);
+  }
+
+  return map;
+}
+
+export function resolveSkillsFromMessages(
+  messages: Message[],
+  index: Map<string, SkillMeta>,
+  maxSkills: number
+): SkillMeta[] {
+  const found = new Set<string>();
+
+  for (const msg of messages) {
+    let match: RegExpExecArray | null;
+    while ((match = SKILL_ID_REGEX.exec(msg.content)) !== null) {
+      const id = match[1];
+      if (index.has(id)) {
+        found.add(id);
+      }
+    }
+  }
+
+  const metas: SkillMeta[] = [];
+  for (const id of found) {
+    const meta = index.get(id);
+    if (meta) {
+      metas.push(meta);
+    }
+    if (metas.length >= maxSkills) {
+      break;
+    }
+  }
+
+  return metas;
+}
+
+export async function loadSkillBodies(
+  skillsRoot: string,
+  metas: SkillMeta[]
+): Promise<string[]> {
+  const bodies: string[] = [];
+
+  for (const meta of metas) {
+    const fullPath = path.join(skillsRoot, meta.path, "SKILL.md");
+    const text = await fs.promises.readFile(fullPath, "utf8");
+    bodies.push(text);
+  }
+
+  return bodies;
+}
+
+export async function buildModelMessages(options: {
+  baseSystemMessages: Message[];
+  trajectory: Message[];
+  skillIndex: Map<string, SkillMeta>;
+  skillsRoot: string;
+  maxSkillsPerTurn?: number;
+}): Promise<Message[]> {
+  const {
+    baseSystemMessages,
+    trajectory,
+    skillIndex,
+    skillsRoot,
+    maxSkillsPerTurn = 8,
+  } = options;
+
+  const selectedMetas = resolveSkillsFromMessages(
+    trajectory,
+    skillIndex,
+    maxSkillsPerTurn
+  );
+
+  if (selectedMetas.length === 0) {
+    return [...baseSystemMessages, ...trajectory];
+  }
+
+  const skillBodies = await loadSkillBodies(skillsRoot, selectedMetas);
+
+  const skillMessages: Message[] = skillBodies.map((body) => ({
+    role: "system",
+    content: body,
+  }));
+
+  return [...baseSystemMessages, ...skillMessages, ...trajectory];
+}
+