← ClaudeAtlas

ecc-cost-aware-llm-pipelinelisted

Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching. Use when LLM spend needs to come down, or when routing tasks across model tiers and budgets.
KornLabs/truss · ★ 2 · AI & Automation · score 68
Install: claude install-skill KornLabs/truss
<!-- Source: affaan-m/ECC (MIT) - re-imported 2026-09-09 - moved and renamed for this baseline, frontmatter `name` set to the directory name; body unchanged. Licence text: .claude/THIRD-PARTY-LICENSES.md --> # Cost-Aware LLM Pipeline Patterns for controlling LLM API costs while maintaining quality. Combines model routing, budget tracking, retry logic, and prompt caching into a composable pipeline. ## When to Activate - Building applications that call LLM APIs (Claude, GPT, etc.) - Processing batches of items with varying complexity - Need to stay within a budget for API spend - Optimizing cost without sacrificing quality on complex tasks ## Core Concepts ### 1. Model Routing by Task Complexity Automatically select cheaper models for simple tasks, reserving expensive models for complex ones. ```python MODEL_SONNET = "claude-sonnet-5" 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. Immutable Cost Tracking Track cumulative spend with frozen dataclasses. Each API call returns a new tracker — n