---
name: llm-optimization
description: Enforces token optimization best practices across all languages and frameworks. Applies prompt compression, structured outputs, context pruning, caching, and model routing to save tokens and costs.
---
# LLM Token Optimization Skill

This skill strictly governs how you (the AI agent) MUST write code and prompt architectures that involve LLM interactions. If a user asks you to implement an AI feature, you are REQUIRED to follow these directives.

## MANDATORY DIRECTIVES

### 1. PROMPT COMPRESSION (MANDATORY)
You MUST NEVER use conversational language or pleasantries in prompts ("Please", "I want you to", "Can you").
You MUST structure all instructions using YAML, Markdown lists, and declarative commands.
Positive constraints ("Output ONLY JSON") are strictly preferred over negative constraints ("Do not output markdown").

**❌ Bad Prompt Example (Do not use):**
"You are a helpful assistant. Please read the following text and I would like you to extract the names of the people. Also, summarize the text in one sentence. Make sure you return it in JSON format and don't write anything before or after the JSON."

**✅ Optimized Prompt Example (Use this):**
```text
Task: Extract entities and summarize text.
Rules:
- Output valid JSON only.
- Schema: {"people": [str], "summary": str}
Input: <text>
```

### 2. STRICT OUTPUT FORMATTING (MANDATORY)
You MUST enforce the exact structure via API native parameters. Never rely solely on plaintext prompt instructions to get JSON.

#### OpenAI Implementation Snippet:
```python
response = client.chat.completions.create(
    model="gpt-4o-mini",
    response_format={ "type": "json_schema", "json_schema": {...} }, # Mandatory for JSON
    messages=[{"role": "system", "content": "..."}]
)
```

#### Anthropic Implementation Snippet:
```python
# Anthropic uses Tool Calling to force strict extraction schemas
response = client.messages.create(
    model="claude-3-5-haiku-20241022",
    tools=[{"name": "extract_data", "input_schema": {...}}],
    tool_choice={"type": "tool", "name": "extract_data"},
    messages=[...]
)
```

#### Gemini Implementation Snippet:
```python
from google.genai import types
response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents=...,
    config=types.GenerateContentConfig(
        response_mime_type="application/json",
        response_schema=MyPydanticModel
    )
)
```

### 3. CONTEXT PRUNING & SANITIZATION (CRITICAL RULE)
*   **Never** pass raw HTML/XML, unprocessed logs, or massive payloads to the model uncleaned. You MUST write pre-processing code to strip tags, metadata, and whitespace.
*   **Stop-Word Removal:** For datasets where grammar is irrelevant (e.g., massive log extraction), consider stripping stop-words ("the", "is", "a") prior to inference.
*   **RAG / Chunking:** Instead of sending an entire document, you MUST use semantic search or targeted chunking to retrieve only the relevant paragraphs.

### 4. TOKEN BUDGETING & MODEL DOWNGRADE
Before proposing an LLM architecture to a user, you MUST estimate the token cost per call.
*   **Never** default to flagship models (e.g., GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro) for simple extraction, classification, or formatting tasks.
*   You MUST route highly structured tasks to **GPT-4o-mini**, **Claude 3.5 Haiku**, or **Gemini 1.5 Flash**.

### 5. PROMPT CACHING REQUIREMENTS
When building repeatable LLM pipelines:
*   Place all static structural instructions, system prompts, schemas, and few-shot examples at the **VERY BEGINNING** of the prompt string array.
*   Place dynamic user data (the variable context) at the **VERY END**.
*   This specific ordering is required to maximize cache hit rates on Anthropic and Google systems, reducing costs by up to 90%.

### 6. TESTING / VALIDATION
Whenever you write an optimized prompt, you MUST accompany the code with a unit test or validation check. This proves that despite using compressed context and a downgraded/cheaper model, the result remains perfectly accurate.