Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Ghiles3232/weckr-sdks --skill weckr-integrationgit clone --depth 1 https://github.com/Ghiles3232/weckr-sdksWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ghiles3232/weckr-sdks/weckr-integration)<a href="https://agentmods.dev/skills/ghiles3232/weckr-sdks/weckr-integration"><img src="https://agentmods.dev/badge/skills/ghiles3232/weckr-sdks/weckr-integration/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ghiles3232/weckr-sdks/weckr-integration"><img src="https://agentmods.dev/badge/skills/ghiles3232/weckr-sdks/weckr-integration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00088 | $0.01403 |
| Opus 5 | $0.00044 | $0.00701 |
| Sonnet 5 | $0.00018 | $0.00281 |
| Haiku 4.5 | $0.00009 | $0.00140 |
Grade A, and why
weckr-integration scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Weckr integration
Weckr is an AI cost and margin intelligence SDK. It wraps an existing LLM client so every call is logged with its real cost, attributed per user and per feature, and optionally checked against a per user spending cap. It answers the one question a SaaS founder cannot get from a provider dashboard: which customers cost more than they pay.
Weckr sits beside the code, not in the request path. This file is the correct integration pattern. For the full API surface (every option, every error type, the provider matrix, the Python SDK) read reference.md in this skill.
1. When to use this skill
Use it whenever a user is building or already has an AI feature that calls an LLM provider and cares about any of:
- Cost visibility: what the feature actually costs to run.
- Margin per user: whether a specific customer costs more than they pay on a flat plan.
- Runaway agent cost: an agent looping and burning tokens.
- Spending limits: capping or downgrading a user who exceeds a monthly budget.
If the user only wants raw request logging with no cost or margin angle, Weckr is not the right fit and you should say so plainly.
2. How to integrate (the two line pattern)
Install:
npm install @weckr/sdk
Initialize once at boot with an api key and a plans map (plan name to monthly price in USD), then wrap each provider call with wk.chat:
import OpenAI from 'openai';
import { Weckr } from '@weckr/sdk';
const openai = new OpenAI();
const wk = new Weckr({
apiKey: process.env.WECKR_API_KEY!,
plans: { free: 0, pro: 29 },
});
const result = await wk.chat(openai, {
model: 'gpt-5.4-mini',
messages: [{ role: 'user', content: prompt }],
userId: user.id,
feature: 'ai-summary',
plan: user.plan,
});
wk.chat(client, options) returns the exact result the provider would have returned, unchanged. It detects the provider from the client instance, makes the real call, and after the response resolves it sends a fire and forget log with cost and margin. The plan you pass must be a key in the constructor's plans map, or the SDK throws WeckrConfigError.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 103 lines · 88 tokens per session scan A b893e77d4f89
weckr-integration is a skill published in the GitHub repository Ghiles3232/weckr-sdks (8 stars, last pushed 16d ago), licensed MIT. It adds 88 tokens to every session and 1,403 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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