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+---
+name: tokencut
+description: Compress text using AgentReady's TokenCut API to reduce token usage by 40-60% with minimal accuracy loss. Use when you need to reduce prompt length or compress text before sending to LLMs.
+category: Data & Integrations
+metadata:
+  author: curtastrophe.zo.computer
+  emojis: ["✂️", "📉", "⚡"]
+tags:
+  - compression
+  - token-optimization
+  - ai-costs
+  - agentready
+---
+
+# TokenCut - Text Compression Skill
+
+This skill compresses text using AgentReady's TokenCut API to reduce token usage by 40-60% with minimal accuracy loss (~0.4% on standard benchmarks).
+
+> **Data Disclosure:** All input text is transmitted to AgentReady's external servers (`agentready.cloud`) for compression processing. Your text leaves your environment. Do not compress text containing passwords, API keys, PII, or confidential information without first reviewing [AgentReady's data retention policy](https://agentready.cloud). Your LLM API keys are never sent to AgentReady, only the text content itself.
+
+---
+
+## ⚠️ CRITICAL: When TokenCut Works vs. Doesn't Work
+
+### ✅ TokenCut WORKS (Saves Tokens)
+
+| Scenario | Why It Works |
+|----------|--------------|
+| **Script calling external LLM** | Script fetches data → compresses → sends to OpenAI/Claude API. The LLM only sees compressed text. |
+| **zo.space API routes** | Route receives request → compresses → calls LLM. The LLM only sees compressed text. |
+| **Multi-agent pipeline with file handoff** | Agent A compresses → writes to file → Agent B reads compressed file. Agent B sees less content. |
+| **Pre-processing data for later** | Compress now, store. Future LLM calls use compressed data. |
+
+**The key:** The LLM that processes the data must see the COMPRESSED version, not the original.
+
+### ❌ TokenCut DOESN'T WORK (No Savings)
+
+| Scenario | Why It Fails |
+|----------|--------------|
+| **Single agent fetching and processing** | Agent reads data into its context (tokens consumed), then compresses. Too late. |
+| **Agent calling TokenCut on its own fetched data** | The agent already "saw" the uncompressed data. |
+| **Compression after LLM processing** | Tokens already consumed. |
+
+**The problem:** If an LLM agent fetches data via API, it immediately reads that data into its context. Compressing afterward doesn't undo the token usage.
+
+---
+
+## Architecture Comparison
+
+### ❌ Wrong Way (Single Agent)
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│                   SCHEDULED AGENT (GLM 5)                       │
+│                                                                 │
+│  Step 1: "Fetch emails from Gmail"                              │
+│          └── API returns 500 emails                             │
+│          └── Agent READS them ← 🔴 TOKENS CONSUMED HERE         │
+│                                                                 │
+│  Step 2: "Compress with TokenCut"                               │
+│          └── Returns compressed text                            │
+│          └── Agent READS result ← 🔴 MORE TOKENS                │
+│                                                                 │
+│  Step 3: "Summarize and send digest"                            │
+│          └── No savings possible - already consumed tokens      │
+│                                                                 │
+└─────────────────────────────────────────────────────────────────┘
+```
+
+### ✅ Right Way (Script + External LLM)
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│                    SCRIPT (No LLM context)                      │
+│                                                                 │
+│  Step 1: Fetch emails from Gmail API                            │
+│          └── Returns raw data (no token cost - just API call)   │
+│                                                                 │
+│  Step 2: Compress with TokenCut                                 │
+│          └── Returns compressed text                            │
+│                                                                 │
+│  Step 3: Send to EXTERNAL LLM (OpenAI, Claude, etc.)            │
+│          └── 🟢 LLM only sees COMPRESSED text                   │
+│          └── 🟢 Token savings: 40-60%                           │
+│                                                                 │
+│  Step 4: Save result to file                                    │
+│          └── Agent reads tiny summary (minimal tokens)          │
+│                                                                 │
+└─────────────────────────────────────────────────────────────────┘
+```
+
+---
+
+## Prerequisites
+
+- AgentReady API key saved in Zo Secrets as `AGENTREADY_API_KEY`
+
+---
+
+## Usage
+
+### Option 1: In Scripts (Recommended for Token Savings)
+
+Use TokenCut in scripts that call external LLMs:
+
+```typescript
+import { compressText } from "/home/workspace/Skills/tokencut/scripts/compress.ts";
+
+// Fetch data (no LLM yet)
+const rawData = await fetchDataFromAPI();
+
+// Compress BEFORE sending to LLM
+const compressed = await compressText(rawData, "standard");
+
+// Send to external LLM (only sees compressed version)
+const response = await fetch("https://api.openai.com/v1/chat/completions", {
+  body: JSON.stringify({
+    model: "gpt-4o",
+    messages: [{ role: "user", content: compressed }],
+  }),
+});
+```
+
+### Option 2: Direct Script Usage
+
+Run the compression script directly:
+
+```bash
+bun /home/workspace/Skills/tokencut/scripts/compress.ts --text "your long text here" --level standard
+```
+
+**Arguments:**
+- `--text` or `-t`: The text to compress (required)
+- `--level`: Compression level - `light`, `standard` (default), or `aggressive`
+- `--output` or `-o`: Optional output file path
+
+**Examples:**
+```bash
+# Standard compression (recommended)
+bun /home/workspace/Skills/tokencut/scripts/compress.ts --text "your prompt here" --level standard
+
+# Light compression (preserves more, saves 20-30%)
+bun /home/workspace/Skills/tokencut/scripts/compress.ts -t "your text" -l light
+
+# Aggressive compression (maximum savings, ~60%)
+bun /home/workspace/Skills/tokencut/scripts/compress.ts -t "your text" -l aggressive -o result.txt
+```
+
+### Option 3: REST API Direct
+
+Call the AgentReady API directly:
+
+```bash
+curl -X POST https://agentready.cloud/v1/compress \
+  -H "Authorization: Bearer $AGENTREADY_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{"text": "your text to compress", "level": "standard"}'
+```
+
+---
+
+## Compression Levels
+
+| Level | Token Savings | Best For |
+|-------|---------------|----------|
+| Light | 20-30% | Code, math, technical content |
+| Standard | 40-50% | General prompts, web content (recommended) |
+| Aggressive | 55-60% | Long documents, articles |
+
+---
+
+## Integration Patterns
+
+### Pattern 1: Script-Based Workflow (Best for Scheduled Tasks)
+
+Instead of having an agent do everything, use a script:
+
+```yaml
+# Agent instruction
+Run the script `bun /home/workspace/Scripts/your-batch-script.ts --output /tmp/result.md`
+Then read the output file and send a summary via Telegram.
+```
+
+**Why this works:** The script handles the heavy processing (fetch + compress + LLM call), the agent only sees a tiny summary.
+
+### Pattern 2: zo.space API Route
+
+Create an API route that compresses before calling an LLM:
+
+```typescript
+// /api/compress-and-process route
+import { compressText } from "/home/workspace/Skills/tokencut/scripts/compress.ts";
+
+export default async (c) => {
+  const { text } = await c.req.json();
+  
+  // Compress first
+  const compressed = await compressText(text, "standard");
+  
+  // Then process with LLM (only sees compressed)
+  const result = await callLLM(compressed);
+  
+  return c.json({ result });
+};
+```
+
+### Pattern 3: Multi-Agent Pipeline with File Handoff
+
+```
+Agent A (Fetcher):
+1. Fetch data from API
+2. Compress with TokenCut
+3. Write to file: /tmp/compressed_data.txt
+
+Agent B (Processor):
+1. Read file: /tmp/compressed_data.txt
+2. Process the compressed data
+3. Output final result
+```
+
+**Note:** Agent B saves tokens because it reads the compressed file. Agent A still consumes tokens reading the original data, but its job is minimal (just fetch and compress).
+
+---
+
+## What's New (Feb 2026)
+
+- **`/v1/compress` endpoint** - Now live and stable
+- **Simplified auth** - No `X-Upstream-API-Key` needed, only your AgentReady API key
+- **Optimized compression** - More efficient token reduction
+- **Token usage tracking** - Better visibility into savings
+- **Self-hosted version** - Coming soon for entirely local compression
+
+---
+
+## Tips
+
+- **Best for**: Web content, articles, documentation, long prompts
+- **Preserves**: Code blocks, URLs, numbers, proper nouns
+- **Avoid for**: Code (use light), math equations, very short prompts (<100 tokens)
+
+---
+
+## When to Trigger This Skill
+
+### Suggested Use Cases
+
+Consider offering TokenCut compression when the user is building:
+
+- Scripts that process multiple items through an external LLM in a batch/loop
+- Multi-agent pipelines with file handoffs between stages
+- zo.space API routes that process text before calling an LLM
+- Pre-processing pipelines that prepare data for later LLM consumption
+
+**Important:** Because this skill sends text to an external API, always confirm with the user before adding compression to their workflow. Never inject TokenCut silently into scripts or pipelines.
+
+**Opt-in tip:** On Zo, users can create a conditional rule to be prompted about TokenCut when writing batch LLM scripts.
+
+### When NOT to Trigger
+
+This skill should NOT be triggered when:
+- A single agent is fetching and processing data in one go (tokens already consumed in context)
+- The LLM has already consumed the tokens
+- You're just trying to reduce an agent's output size
+- The input is very short (<100 tokens / ~400 chars)
+- The input is structured data (JSON schemas, configs) where exact format matters
+- The input is executable code (compress docs ABOUT code, not the code itself)
diff --git a/Community/tokencut/scripts/compress.ts b/Community/tokencut/scripts/compress.ts
new file mode 100644
index 0000000..f521042
--- /dev/null
+++ b/Community/tokencut/scripts/compress.ts
@@ -0,0 +1,123 @@
+#!/usr/bin/env bun
+
+interface CompressResponse {
+  success: boolean;
+  data?: {
+    original_length: number;
+    compressed_length: number;
+    savings_percent: number;
+    compressed_text: string;
+  };
+  error?: string;
+}
+
+const API_URL = "https://agentready.cloud/v1/compress";
+
+async function getApiKey(): Promise<string> {
+  const apiKey = process.env.AGENTREADY_API_KEY;
+  if (!apiKey) {
+    throw new Error(
+      "AGENTREADY_API_KEY not found in environment. Please add it to Zo Secrets."
+    );
+  }
+  return apiKey;
+}
+
+export async function compressText(
+  text: string,
+  level: "light" | "standard" | "aggressive" = "standard"
+): Promise<string> {
+  const apiKey = await getApiKey();
+
+  const response = await fetch(API_URL, {
+    method: "POST",
+    headers: {
+      "Authorization": `Bearer ${apiKey}`,
+      "Content-Type": "application/json",
+    },
+    body: JSON.stringify({ text, level }),
+  });
+
+  if (!response.ok) {
+    const error = await response.text();
+    throw new Error(`Compression failed: ${response.status} ${error}`);
+  }
+
+  const result: CompressResponse = await response.json();
+
+  if (!result.success || !result.data) {
+    throw new Error(result.error || "Compression failed");
+  }
+
+  return result.data.compressed_text;
+}
+
+async function main() {
+  const args = process.argv.slice(2);
+  let text = "";
+  let level: "light" | "standard" | "aggressive" = "standard";
+  let outputFile = "";
+
+  // Parse arguments
+  for (let i = 0; i < args.length; i++) {
+    const arg = args[i];
+    if (arg === "--text" || arg === "-t") {
+      text = args[++i];
+    } else if (arg === "--level" || arg === "-l") {
+      const lvl = args[++i].toLowerCase();
+      if (["light", "standard", "aggressive"].includes(lvl)) {
+        level = lvl as "light" | "standard" | "aggressive";
+      }
+    } else if (arg === "--output" || arg === "-o") {
+      outputFile = args[++i];
+    } else if (arg === "--help" || arg === "-h") {
+      console.log(`
+TokenCut - Text Compression CLI
+
+Usage: bun compress.ts [options]
+
+Options:
+  -t, --text <text>     Text to compress (required)
+  -l, --level <level>  Compression level: light, standard, aggressive (default: standard)
+  -o, --output <file>  Output file path (optional)
+  -h, --help           Show this help message
+
+Examples:
+  bun compress.ts -t "your text here"
+  bun compress.ts --text "your text" --level aggressive
+  bun compress.ts -t "your text" -o compressed.txt
+`);
+      process.exit(0);
+    }
+  }
+
+  if (!text) {
+    console.error("Error: --text or -t is required");
+    console.log("Run with --help for usage information");
+    process.exit(1);
+  }
+
+  console.log(`Compressing text (${text.length} chars) with ${level} level...`);
+
+  try {
+    const compressed = await compressText(text, level);
+
+    if (outputFile) {
+      await Bun.write(outputFile, compressed);
+      console.log(`Compressed text saved to: ${outputFile}`);
+    } else {
+      console.log("\n--- Compressed Text ---\n");
+      console.log(compressed);
+    }
+
+    const savings = ((1 - compressed.length / text.length) * 100).toFixed(1);
+    console.log(`\nOriginal: ${text.length} chars | Compressed: ${compressed.length} chars | Savings: ${savings}%`);
+  } catch (error) {
+    console.error("Error:", error instanceof Error ? error.message : error);
+    process.exit(1);
+  }
+}
+
+if (import.meta.main) {
+  main();
+}