Ask which of your customers cost more in LLM calls than they pay, per user and per feature.
AI cost and margin intelligence for SaaS founders. See exactly which users cost you more than they pay, per LLM call, zero added latency.
Drop the SDK into your app, get a dashboard that shows cost per user / feature / model and recommends cheaper swaps. Set per-plan spending caps the SDK enforces before the LLM call.
| Package | Install | Source | |
|---|---|---|---|
| TypeScript / Node | @weckr/sdk | npm install @weckr/sdk | typescript/ |
| Python | weckr-sdk | pip install weckr-sdk | python/ |
| Claude / Cursor (MCP) | @weckr/mcp | npx -y @weckr/mcp | mcp/ |
| Claude Code (Skills) | weckr plugin | /plugin install weckr@weckr | skills/ |
Each subfolder has full setup docs, examples, and supported-model lists.
This repo is also an installable Claude Code plugin that bundles four skills, so your AI assistant knows real model prices and the exact Weckr syntax instead of inventing code:
/plugin marketplace add Ghiles3232/weckr-sdks
/plugin install weckr@weckr
| Skill | What Claude learns |
|---|---|
weckr-integration | Wire the SDK in with the correct two line pattern, errors, and provider notes |
weckr-model-pricing | Current per token prices for OpenAI, Anthropic, Gemini, and Kimi |
weckr-cost-estimator | Project what an AI feature costs per call, per user, per month before shipping |
weckr-margin-audit | Flag which pricing plans go underwater once LLM cost is counted |
The pricing skills read the live feed at useweckr.com/pricing.json first (generated from the same table Weckr bills with), and a weekly pricing watcher opens a reviewed PR whenever a provider changes rates, so the numbers track reality instead of drifting. Full explainer at useweckr.com/skills.
Click around with seeded data for a fictional SaaS. No signup required.
Clone a full AI SaaS with Weckr already wired in and deploy in minutes. Both are MIT licensed.
Step by step integrations for the stacks people actually ship on, each with the exact code and where the wrap goes:
flush() for serverless, streaming notes.wk.chat(client, opts).(userId, feature, model, tokens, latency, plan).(cost, revenue, margin) per request.Only the call metadata above: model name, token counts, latency, plan, and your userId string. No prompt text or completion text is ever sent.
app.useweckr.com. Free tier, no credit card.
Have a question about integration or a feature request? Open a discussion: https://github.com/Ghiles3232/weckr-sdks/discussions
We respond to every question.
MIT. See LICENSE.
Source-derived launch command. Check the maintainer’s required arguments and credentials before running:
npx -y @weckr/mcpMerge this template into ~/Library/Application Support/Claude/claude_desktop_config.json. Keep existing servers. Add any arguments, credentials, and permissions required by the maintainer; this template has not been install-tested.
{
"mcpServers": {
"com-useweckr-weckr": {
"command": "npx",
"args": [
"-y",
"@weckr/mcp"
]
}
}
}Restart Claude Desktop completely for changes to take effect. Confirm the server appears connected in the client’s tool list, then try a read-only example from its documentation.
Claude Desktop setup reference@weckr/mcpnpmcom.useweckr/weckr works with any MCP-compatible client. Copy the config snippet from the Configuration section above and add it to the file shown for your client, then restart the application.
~/Library/Application Support/Claude/claude_desktop_config.jsonRestart Claude Desktop completely for changes to take effect.~/.cursor/mcp.jsonRestart Cursor for changes to take effect..vscode/mcp.jsonReload VS Code window for changes to take effect.~/.codeium/windsurf/mcp_config.jsonRestart Windsurf for changes to take effect..mcp.jsonSave at the project root, then start Claude Code in that project and review the MCP server approval prompt. Keep real credentials out of shared files.