cost-aware-llm-pipeline

What this skill does

When applied, it prepends a system prompt before your request is sent — no extra calls and no change to how you are billed beyond the added tokens.

---
name: cost-aware-llm-pipeline
description: LLM API 使用成本优化模式 —— 基于任务复杂度的模型路由、预算跟踪、重试逻辑和提示缓存。
origin: ECC
---

# 成本感知型 LLM 流水线

在保持质量的同时控制 LLM API 成本的模式。将模型路由、预算跟踪、重试逻辑和提示词缓存组合成一个可组合的流水线。

## 何时激活

* 构建调用 LLM API（Claude、GPT 等）的应用程序时
* 处理具有不同复杂度的批量项目时
* 需要将 API 支出控制在预算范围内时
* 需要在复杂任务上优化成本而不牺牲质量时

## 核心概念

### 1. 根据任务复杂度进行模型路由

自动为简单任务选择更便宜的模型，为复杂任务保留昂贵的模型。

```python
MODEL_SONNET = "claude-sonnet-4-6"
MODEL_HAIKU = "claude-haiku-4-5-20251001"

_SONNET_TEXT_THRESHOLD = 10_000  # chars
_SONNET_ITEM_THRESHOLD = 30     # items

def select_model(
    text_length: int,
    item_count: int,
    force_model: str | None = None,
) -> str:
    """Select model based on task complexity."""
    if force_model is not None:
        return force_model
    if text_length >= _SONNET_TEXT_THRESHOLD or item_count >= _SONNET_ITEM_THRESHOLD:
        return MODEL_SONNET  # Complex task
    return MODEL_HAIKU  # Simple task (3-4x cheaper)
```

### 2. 不可变的成本跟踪

使用冻结的数据类跟踪累计支出。每个 API 调用都会返回一个新的跟踪器 —— 永不改变状态。

```python
from dataclasses import dataclass

@dataclass(frozen=True, slots=True)
class CostRecord:
    model: str
    input_tokens: int
    output_tokens: int
    cost_usd: float

@dataclass(frozen=True, slots=True)
class CostTracker:
    budget_limit: float = 1.00
    records: tuple[CostRecord, ...] = ()

    def add(self, record: CostRecord) -> "CostTracker":
        """Return new tracker with added record (never mutates self)."""
        return CostTracker(
            budget_limit=self.budget_limit,
            records=(*self.records, record),
        )

    @property
    def total_cost(self) -> float:
        return sum(r.cost_usd for r in self.records)

    @property
    def over_budget(self) -> bool:
        return self.total_cost > self.budget_limit
```

### 3. 窄范围重试逻辑

仅在暂时性错误时重试。对于认证或错误请求错误，快速失败。

```python
from anthropic import (
    APIConnectionError,
    InternalServerError,
    RateLimitError,
)

_RETRYABLE_ERRORS = (APIConnectionError, RateLimitError, InternalServerError)

Use this skill

{
  "model": "gpt-4o-mini",
  "skill": "imp-ac78e803-a358-47c4-9a15-4a77db6d5e3c",
  "messages": [{ "role": "user", "content": "…" }]
}

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