Agent skill · marketing growth · jeremylongshore
lindy-cost-tuning
Optimize Lindy AI costs through credit management, model selection,
Why this skill is useful
Provides specific strategies for optimizing costs in Lindy AI, including model selection and agent consolidation techniques that the AI wouldn't reliably generate on its own.
What it needs
Requires lindy account access. About 4k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Lindy Cost Tuning Overview Lindy uses a credit-based pricing model. Every task costs credits based on model size, step count, premium actions, and duration. Cost tuning targets: model right-sizing, agent consolidation, trigger optimization, and credit monitoring. Prerequisites Lindy workspace with billing access Multiple active agents to evaluate Dashboard access to review per-agent task history Credit Cost Reference Factor Credits -------- --------- Basic model task (Gemini Flash) 1-2 Mid-tier model (GPT-4o-mini, Claude Haiku) 2-5 Large model task (GPT-4, Claude Sonnet) 5-10 Premium model (Claude Opus) ~10+ Phone call (US/Canada) ~20/minute Phone call (international) 21-53/minute Premium actions (webhooks) Additional per action Minimum per task 1 credit Plan Costs Plan Monthly Credits Per Extra Seat ------ --------- --------- ---------------- Free $0 400 N/A Pro $49.99 5,000 $19.99 Business $299.99 30,000 Included Enterprise Custom Custom Custom Instructions Step 1: Audit Agent Credit Consumption For each active agent, collect: 1. Task count (last 30 days) — from Tasks tab 2. Average credits per task — total credits / task count 3. Model used — from agent settings 4. Trigger frequency — how often the agent fires Create a cost audit table: Agent Tasks/Month Credits/Task Model Monthly Credits % of Total ------- ------------ ------------- ------- ---------------- ----------- Support Bot 500 5 Claude Sonnet 2,500 50% Lead Router 200 2 GPT-4o-mini 400 8% Report Gen 30 10 GPT-4 300 6% Step 2: Right-Size Models The highest-impact optimization. For each agent, ask: "Does this task actually need GPT-4/Claude, or would Gemini Flash work?" Current Setup Optimized Savings -------------- ----------- --------- Email classify with Claude Sonnet (5 cr) Gemini Flash (1 cr) 80% Data extract with GPT-4 (10 cr) GPT-4o-mini (3 cr) 70% Simple routing with Claude Opus (10 cr) Gemini Flash (1 cr) 90% Test the downgrade: Run 10 tasks with the smaller model. Compare output quality. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/lindy-cost-tuning--67abea