Agent skill · software engineering · jeremylongshore
runway-cost-tuning
Reduce Runway credit waste through current pricing, duplicate prevention, model policy, and measured usage without publishing stale price tables. Use when reviewing spend. Trigger with: "optimize Runway cost", "Runway credit usage", "reduce Runway spend".
Why this skill is useful
Provides specific implementation patterns and error handling for the Runway API that the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. Requires runwayml account access. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Evidence-Based Runway Credit Optimization Overview Runway pricing changes independently of models and code. Cost tuning begins from the current pricing page and authorized usage data, then removes accidental duplicates, rejected work, oversized outputs, unnecessary variants, and poor routing without lowering an approved quality or safety bar. Prerequisites Current first-party pricing and organization usage evidence Operation, task, model, duration, output-count, state, and credit records A product-owned quality policy and finance-approved budget Instructions Step 1: Freeze the authority Record retrieval time and fingerprint for the current pricing page, billing model, organization tier, and contract exceptions. Do not copy a historical credit table into a long-lived calculator. Step 2: Reconcile demand to tasks Join internal operation IDs to provider task IDs and usage. Separate successful, failed, moderated, cancelled, duplicate, abandoned, and stored outputs; account for safety failures that still consume credits. Step 3: Find duplicate generation Identify timeouts, client retries, queue resubmission, race conditions, and lost task IDs. Fix persistence and recovery before trading away output quality. Step 4: Compare eligible policies For each workload, compare approved direct models or Model Router configurations on current estimated credits, latency, and quality. Use router dryRun where supported because it returns selection and estimated cost without generating an asset. Step 5: Control request shape Review duration, resolution, output count, reference count, recipe choice, and retries against the product requirement. Reduce only fields that do not violate the approved result. Step 6: Roll out with guardrails Set per-operation and rolling budgets, alerts, concurrency locks, and automatic stop conditions. Compare credits per accepted asset and quality escape rate before expanding. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/runway-cost-tuning