Imported from jeremylongshore/tons-of-skills-marketplace (plugins/saas-packs/openrouter-pack/skills/openrouter-context-optimization/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill openrouter-context-optimization. Copyright stays with the author (MIT).

OpenRouter Context Optimization

Overview

OpenRouter models have varying context windows (4K to 1M+ tokens). Since pricing is per-token, stuffing unnecessary context wastes money and can degrade output quality. This skill covers context window lookup, token estimation, conversation trimming, chunking strategies, and Anthropic prompt caching for large contexts.

Prerequisites

• An OpenRouter API key (sk-or-v1-...) exported as OPENROUTER_API_KEY — see the openrouter-install-auth skill for setup
• Python 3.8+ with the OpenAI SDK and requests for model-metadata lookup; tiktoken for exact token counting per the references
• curl and jq to query context windows and pricing from /api/v1/models
• Node.js 18+ if you use the TypeScript context-budget calculator in the references

Instructions

1. Run the Query Context Limits one-liner — it returns context_length and prompt price per 1M tokens for each candidate model, so you know the real budget before writing code.
2. Estimate input size (~4 characters per token, or exactly with tiktoken per the references) and pick a model with select_model_for_context() from Context-Aware Model Selection — it applies an 80% safety margin and falls back through gpt-4o-mini (128K) → Claude 3.5 Sonnet (200K) → Gemini 2.0 Flash (1M).
3. Keep long conversations inside budget with trim_conversation() per Conversation Trimming: system prompt plus the last N messages, with a trim-marker note injected where history was dropped.
4. For documents that exceed any window, use chunk_and_process() per Chunking for Large Documents — 8,000-char chunks with 500-char overlap, analyzed independently at temperature=0 and then synthesized.
5. Mark large static blocks with cache_control: {"type": "ephemeral"} per Prompt Caching for Repeated Context to cut repeated input cost by 90% on Anthropic models.
6. Monitor prompt_tokens on every response (Enterprise Considerations) to catch context bloat before it becomes a 400 context_length_exceeded.

Query Context Limits

# Check context window for specific models
curl -s https://openrouter.ai/api/v1/models | jq '[.data[] | select(
  .id == "anthropic/claude-3.5-sonnet" or
  .id == "openai/gpt-4o" or
  .id == "google/gemini-2.0-flash-001" or
  .id == "meta-llama/llama-3.1-70b-instruct"
) | {id, context_length, prompt_per_M: ((.pricing.prompt|tonumber)*1000000)}]'

Context-Aware Model Selection

import os, requests
from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key=os.environ["OPENROUTER_API_KEY"],
    default_headers={"HTTP-Referer": "https://my-app.com", "X-Title": "my-app"},
)

# Cache model metadata at startup
MODELS = {m["id"]: m for m in requests.get("https://openrouter.ai/api/v1/models").json()["data"]}

def estimate_tokens(text: str) -> int:
    """Rough estimate: 1 token ~ 4 characters for English text."""
    return len(text) // 4

def select_model_for_context(messages: list, preferred: str = "anthropic/claude-3.5-sonnet") -> str:
    """Pick a model that fits the context, falling back to larger windows."""
    estimated_tokens = sum(len(m.get("content", "")) for m in messages) // 4

    FALLBACK_CHAIN = [
        ("openai/gpt-4o-mini", 128_000),
        ("anthropic/claude-3.5-sonnet", 200_000),
        ("google/gemini-2.0-flash-001", 1_000_000),
    ]

    # Try preferred model first
    preferred_ctx = MODELS.get(preferred, {}).get("context_length", 0)
    if estimated_tokens < preferred_ctx * 0.8:  # 80% safety margin
        return preferred

    for model_id, ctx in FALLBACK_CHAIN:
        if estimated_tokens < ctx * 0.8:
            return model_id

    raise ValueError(f"Content too large ({estimated_tokens} est. tokens)")

Conversation Trimming

def trim_conversation(
    messages: list[dict],
    max_tokens: int = 100_000,
    keep_system: bool = True,
    keep_last_n: int = 4,
) -> list[dict]:
    """Trim conversation history to fit context window.

    Strategy: Keep system prompt + last N messages.
    If still too large, reduce to last 2 messages.
    """
    system = [m for m in messages if m["role"] == "system"] if keep_system else []
    non_system = [m for m in messages if m["role"] != "system"]

    kept = non_system[-keep_last_n:]
    trimmed = non_system[:-keep_last_n] if len(non_system) > keep_last_n else []

    total_est = sum(estimate_tokens(m.get("content", "")) for m in system + kept)
    if total_est > max_tokens and keep_last_n > 2:
        kept = non_system[-2:]

    result = system + kept
    if trimmed:
        summary_note = {
            "role": "system",
            "content": f"[Previous {len(trimmed)} messages trimmed for context limits]",
        }
        result = system + [summary_note] + kept

    return result

Chunking for Large Documents

def chunk_and_process(document: str, question: str, model: str = "openai/gpt-4o-mini",
                      chunk_size: int = 8000, overlap: int = 500) -> str:
    """Process a large document in overlapping chunks, then synthesize."""
    chunks = []
    start = 0
    while start < len(document):
        chunks.append(document[start:start + chunk_size])
        start += chunk_size - overlap

    results = []
    for i, chunk in enumerate(chunks):
        response = client.chat.completions.create(
            model=model,
            messages=[
                {"role": "system", "content": f"Analyzing chunk {i+1}/{len(chunks)}."},
                {"role": "user", "content": f"Document:\n{chunk}\n\nQuestion: {question}"},
            ],
            max_tokens=1024, temperature=0,
        )
        results.append(response.choices[0].message.content)

    # Synthesize
    synthesis = client.chat.completions.create(
        model=model,
        messages=[
            {"role": "system", "content": "Synthesize these partial analyses."},
            {"role": "user", "content": f"Question: {question}\n\nResults:\n" + "\n---\n".join(results)},
        ],
        max_tokens=2048, temperature=0,
    )
    return synthesis.choices[0].message.content

Prompt Caching for Repeated Context

# Anthropic models support prompt caching -- mark large static blocks
# Subsequent requests with same cached block cost 90% less for input tokens
response = client.chat.completions.create(
    model="anthropic/claude-3.5-sonnet",
    messages=[
        {
            "role": "system",
            "content": [
                {
                    "type": "text",
                    "text": large_reference_document,  # 50K+ tokens
                    "cache_control": {"type": "ephemeral"},
                }
            ],
        },
        {"role": "user", "content": "Summarize section 3."},
    ],
    max_tokens=1024,
)
# First request: cache_creation_input_tokens at 1.25x rate
# Subsequent: cache_read_input_tokens at 0.1x rate (90% savings)

Output

• A jq-formatted listing of model IDs with context_length and per-1M prompt pricing from /api/v1/models
• A model ID selected to fit the estimated token count within an 80% safety margin, or a ValueError when nothing fits
• A trimmed message list containing the system prompt, a [Previous N messages trimmed for context limits] note, and the most recent turns
• A single synthesized answer assembled from per-chunk analyses of an oversized document

Examples

Multi-turn chat with the references' prune_conversation() holding a 2,000-token budget — oldest messages drop as the conversation grows:

[Pruned] 9 -> 7 messages (1876 tokens)
Q: What about class-based decorators?...
Tokens: 412

The pruner always keeps the system message and removes the oldest non-system turns first. More worked examples: references/examples.md.

Error Handling

Enterprise Considerations

• Query /api/v1/models at startup to cache context limits -- don't hardcode (they change)
• Use max_tokens on every request to prevent runaway completion costs on large contexts
• Implement conversation trimming as middleware so all calls respect limits
• Use Anthropic prompt caching for RAG contexts that repeat across requests (90% input savings)
• Route large-context tasks to cost-effective models (Gemini Flash for 1M context at low cost)
• Monitor prompt_tokens in responses to detect context bloat before it hits limits

References

• Examples | Errors
• Prompt Caching | Models API