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routinglisted

Cost-optimised task routing. Use at the start of any coding, search, extraction, review, or multi-step task to pick the cheapest execution strategy (deterministic tool, haiku worker, sonnet worker, main model, fable worker) and to handle escalation when a worker fails. Also triggers on mentions of cost, budget, routing, or delegation.
ThomasLangbroek/frugal · ★ 36 · AI & Automation · score 77
Install: claude install-skill ThomasLangbroek/frugal
# Frugal routing Route every sub-task to the cheapest strategy that can succeed. Priorities, in order: correctness, cost, latency, extensibility, simplicity. ## Step 0: overrides If `.claude/routing-overrides.md` exists in the project, read it first. Its rules win over everything below. ## Step 1: tool first Before any delegation: if a deterministic command solves the task (grep, rg, jq, yq, sed, awk, git, terraform, kubectl, helm, docker, a compiler, a test runner), run it. No LLM call. Reasoning models are for reasoning. Step 1 covers **one-shot** commands only: you know the exact command and its output answers the question directly. The moment discovery turns iterative — a second search informed by the first, listing directories to decide what to read next, reading files to summarise them — it is no longer a tool call, it is a locate/extract task. Bright line: the third search/list/read operation on the same question means you are exploring inline; stop and hand the whole question to `scout` or `extractor`, including what you already learned. Every raw tool result you ingest is paid at main-loop rates; a haiku worker reads the same bytes at a fraction of the cost and returns a summary. ## Step 2: decision table Decompose the request into sub-tasks. For each, match signals to the cheapest capable agent: | Task signals | Required capabilities | Route | |---|---|---| | "where is X", "what uses Y", map directory, grep logs | locate | `scout` (haiku) | | pull fields fr