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Agent skill

Lindy Cost Tuning

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Optimize Lindy AI costs through credit management, model selection, and agent consolidation.

MITAuto-check passed

Install Lindy Cost Tuning

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill lindy-cost-tuning -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace lindy-cost-tuning --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/lindy-cost-tuning .claude/skills/lindy-cost-tuning && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
lindy-cost-tuning
GitHub stars
2.8k
Token cost
~1.7k tokens
SKILL.md length
708 words
Files
3 (incl. references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Optimize Lindy AI costs through credit management, model selection, and agent consolidation.

  • Works in 8 steps: Audit Agent Credit Consumption → Right-Size Models → Consolidate Redundant Agents → …
  • Analyzing credit usage patterns
  • SKILL.md covers Overview, Prerequisites, Credit Cost Reference and Plan Costs, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Lindy Cost Tuning is an agent skill from jeremylongshore/tons-of-skills-marketplace. Optimize Lindy AI costs through credit management, model selection, and agent consolidation. Use when reducing spend, analyzing credit usage patterns, or optimizing budget allocation across agents. Trigger with phrases like "lindy cost", "lindy billing", "reduce lindy spend", "lindy budget", "lindy credits".

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/implementation-guide.md` and `references/implementation.md`). Compatibility notes: Designed for Claude Code

It works with OpenAI. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Analyzing credit usage patterns
  • Optimizing budget allocation across agents
  • With phrases like lindy cost
  • Reduce lindy spend

Example prompts

  • “lindy cost”
  • “lindy billing”
  • “reduce lindy spend”
  • “/lindy-cost-tuning”

Requirements

  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Audit Agent Credit Consumption
  2. Right-Size Models
  3. Consolidate Redundant Agents
  4. Optimize Trigger Frequency
  5. Reduce Steps Per Task
  6. Optimize Knowledge Base Usage
  7. Budget Monitoring Setup
  8. Deactivate Idle Agents

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • lindy.ai
    • docs.lindy.ai

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Lindy Cost Tuning loads about 1.7k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 82 tokens; SKILL.md has 708 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~82
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 708 words, ~1,721 tokens.

Download SKILL.mdSave it as .claude/skills/lindy-cost-tuning/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
lindy-cost-tuning
description
Optimize Lindy AI costs through credit management, model selection, and agent consolidation. Use when reducing spend, analyzing credit usage patterns, or optimizing budget allocation across agents. Trigger with phrases like "lindy cost", "lindy billing", "reduce lindy spend", "lindy budget", "lindy credits".
allowed-tools
Read, Write, Edit
compatibility
Designed for Claude Code
version
1.20.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, lindy, cost-optimization

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

FactorCredits
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 task1 credit

Plan Costs

PlanMonthlyCreditsPer Extra Seat
Free$0400N/A
Pro$49.995,000$19.99
Business$299.9930,000Included
EnterpriseCustomCustomCustom

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:

AgentTasks/MonthCredits/TaskModelMonthly Credits% of Total
Support Bot5005Claude Sonnet2,50050%
Lead Router2002GPT-4o-mini4008%
Report Gen3010GPT-43006%
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 SetupOptimizedSavings
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. Most classification, routing, and extraction tasks work identically on smaller models.

Step 3: Consolidate Redundant Agents

Multiple single-purpose agents cost more than one multi-purpose agent:

Before (5 agents, 5 minimum credits per run):

Agent 1: Classify billing emails
Agent 2: Classify technical emails
Agent 3: Classify general emails
Agent 4: Draft billing responses
Agent 5: Draft technical responses

After (1 agent, 1 minimum credit per run):

Support Agent: Classify email → Condition (billing/technical/general)
  → Draft appropriate response → Send

Cost impact: Reducing from 5 agents to 1 saves minimum-credit overhead and simplifies management.

Step 4: Optimize Trigger Frequency

Credits are consumed every time a trigger fires. Reduce unnecessary triggers:

Email Received:

Before: Trigger on ALL emails (300/day) = 300 tasks
After:  Filter: label "support" AND NOT from "noreply@" (40/day) = 40 tasks
Savings: 87% fewer tasks

Schedule trigger:

Before: Every 15 minutes (96/day)
After:  Every 2 hours (12/day)
Question: Does this agent really need to run every 15 minutes?

Slack trigger:

Before: Any message in #general (200/day)
After:  Messages containing "@support-bot" (10/day)
Savings: 95% fewer tasks
Step 5: Reduce Steps Per Task

Each action in a workflow costs credits. Eliminate unnecessary steps:

  • Combine multiple LLM calls into one (see lindy-performance-tuning)
  • Use Set Manually instead of AI Prompt for known values
  • Remove debug/logging steps in production
  • Simplify condition branches
Step 6: Optimize Knowledge Base Usage

KB search costs credits per query. Optimize:

  • Reduce Max Results from 10 to 4 (sufficient for most queries)
  • Use specific query instructions to get relevant results in one search
  • For small datasets (<100 entries), consider putting data directly in the prompt
Show full SKILL.md (264 more words)Show less
Step 7: Budget Monitoring Setup
  1. Check credit usage weekly in Settings > Billing
  2. Set internal alerts for high-consumption agents:
    • 50% of budget: Warning — review usage
    • 80% of budget: Alert — optimize or upgrade
    • 95% of budget: Critical — pause non-essential agents
Step 8: Deactivate Idle Agents

Review agents monthly:

  • No tasks in 30 days → Pause the agent
  • No tasks in 90 days → Delete or archive
  • Lindy only charges for active agent execution, not idle agents

Monthly Cost Optimization Checklist

  • Review per-agent credit consumption
  • Identify agents using large models for simple tasks
  • Check for redundant agents that could be consolidated
  • Review trigger filter effectiveness
  • Remove unused integrations from agents
  • Verify no loops or runaway agent steps
  • Compare actual spend to budget

Error Handling

IssueCauseSolution
Unexpected credit spikeTrigger filter removed or loosenedReview and restore trigger filters
Agent consuming 10x normalLooping agent stepAdd exit conditions, check task history
Credits exhausted mid-monthUnder-budgeted or spikeUpgrade plan or pause non-critical agents
Model downgrade hurts qualityTask needs larger modelSelectively upgrade only that step

Output

Produce a cost-control report showing the workload volume, credit-consuming steps, monthly budget, proposed optimization, and the expected service-quality effect. Measure before and after the change rather than inferring savings from agent configuration alone.

Examples

For a high-volume enrichment agent, move deterministic validation ahead of the credit-consuming enrichment step and test it against a representative week of events. Compare completed useful actions, skipped invalid inputs, and credits per successful outcome before deploying the new ordering.

Resources

Next Steps

Proceed to lindy-reference-architecture for production architecture patterns.

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/.curated/lindy-cost-tuning of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/implementation-guide.md
  • references/implementation.md

Open the folder on GitHubat commit cfae287

Compare with similar skills

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AI SDKvercel-labs/ai-facts16820 repos~1.2kAutomated safety check: PassNone
AI Image Generation and Editingzhayujie/CowAgent47k—~1.3kAutomated safety check: PassMIT
PR Design DocOpenHands/OpenHands91k—~2.4kAutomated safety check: PassMIT
SEO GeoReScienceLab/opc-skills1.8k4 repos~2.1kAutomated safety check: PassApache-2.0

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Works with

Questions about Lindy Cost Tuning

What does Lindy Cost Tuning do?

Optimize Lindy AI costs through credit management, model selection, and agent consolidation. Lindy Cost Tuning is an agent skill from jeremylongshore/tons-of-skills-marketplace. Optimize Lindy AI costs through credit management, model selection, and agent consolidation.

When should I use Lindy Cost Tuning?

Lindy Cost Tuning fits situations like: analyzing credit usage patterns; optimizing budget allocation across agents; with phrases like lindy cost; reduce lindy spend.

How do I install Lindy Cost Tuning in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill lindy-cost-tuning -a claude-code`. Or copy the skill folder (skills/.curated/lindy-cost-tuning in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/lindy-cost-tuning in your project. Claude Code loads it when a task matches its description.

How do I install Lindy Cost Tuning in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill lindy-cost-tuning -a codex`. Or copy the skill folder (skills/.curated/lindy-cost-tuning in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/lindy-cost-tuning in your project. Codex loads it when a task matches its description.

Can I use Lindy Cost Tuning in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill lindy-cost-tuning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/lindy-cost-tuning, .gemini/skills/lindy-cost-tuning, .github/skills/lindy-cost-tuning and .opencode/skills/lindy-cost-tuning in your project.

What does Lindy Cost Tuning need to run?

SKILL.md names no scripts, command-line tools or credentials: Lindy Cost Tuning is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Edit. Compatibility (from SKILL.md): Designed for Claude Code.

Does Lindy Cost Tuning access the network?

SKILL.md names 2 domains. As links in the text: lindy.ai and docs.lindy.ai. This is read from the text; nothing was executed.

Is Lindy Cost Tuning safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Lindy Cost Tuning use?

Lindy Cost Tuning is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Lindy Cost Tuning use?

About 1.7k tokens (SKILL.md is roughly 6.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.8k tokens, read only when the agent opens those files.

What are the alternatives to Lindy Cost Tuning?

Skills that share tags, products or a category with Lindy Cost Tuning: Geo Fundamentals (wasp-lang/wasp, 19k stars), AI SDK (vercel-labs/ai-facts, 168 stars), AI Image Generation and Editing (zhayujie/CowAgent, 47k stars) and PR Design Doc (OpenHands/OpenHands, 91k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Lindy Cost Tuning?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.