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 Omar-Obando/qwen-orchestrator --skill llm-integrationsgit clone --depth 1 https://github.com/Omar-Obando/qwen-orchestratorWrote 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/omar-obando/qwen-orchestrator/llm-integrations)<a href="https://agentmods.dev/skills/omar-obando/qwen-orchestrator/llm-integrations"><img src="https://agentmods.dev/badge/skills/omar-obando/qwen-orchestrator/llm-integrations/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/omar-obando/qwen-orchestrator/llm-integrations"><img src="https://agentmods.dev/badge/skills/omar-obando/qwen-orchestrator/llm-integrations.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 | $0.00073 | $0.03658 |
| Opus 5 | $0.00036 | $0.01829 |
| Sonnet 5 | $0.00015 | $0.00732 |
| Haiku 4.5 | $0.00007 | $0.00366 |
Grade A, and why
llm-integrations 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 6d 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 — 531 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Integrations Skill — Provider Configuration & Cost Optimization
Overview
This skill provides comprehensive guidance for integrating LLM providers (OpenAI, DeepSeek, OpenRouter, Anthropic, Google), configuring API keys, optimizing costs, implementing rate limiting, and managing LLM usage across projects. It includes best practices for cost optimization and API management. Based on OpenAI, Anthropic, Google, and other LLM provider official documentation.
When to Use
Use this skill when:
- Integrating LLM providers (OpenAI, DeepSeek, OpenRouter, Anthropic, Google, Qwen)
- Configuring API keys and authentication for multiple providers
- Optimizing LLM costs across projects
- Implementing rate limiting for API calls
- Managing LLM usage across multiple projects
- Choosing the right model for specific tasks
- Setting up multi-provider fallback strategies
- Implementing cost tracking and monitoring
- Building API key rotation and management
- Creating LLM usage dashboards
- Implementing token counting and budgeting
- Setting up alerting for unusual usage
- Building LLM caching strategies
- Implementing prompt caching where supported
- Managing model versions and upgrades
- Setting up staging vs production API keys
- Implementing LLM performance monitoring
- Building LLM observability with tracing
- Creating LLM security policies
- Managing rate limits across providers
Do NOT use this skill when:
- Building stateful workflows with complex state (use langgraph skill)
- Designing database schema (use database-design skill)
- Creating UI components (use frontend-design skill)
- Implementing provider-specific agent features (use qwen-agent, langchain, or langgraph skills)
- Managing agent teams and coordination (use agent-task-coordinator skill)
- Building Qwen-specific integrations (use qwen-agent skill)
- Implementing LangChain-specific patterns (use langchain skill)
Why avoid: LLM Integrations is for configuration and management. For actual agent/chain implementation, use domain-specific skills.
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.
- 6d ago First seen · 531 lines · 73 tokens per session scan A 26f7a36d0b12
llm-integrations is a skill published in the GitHub repository Omar-Obando/qwen-orchestrator (49 stars, last pushed 2mo ago), licensed MIT. It adds 73 tokens to every session and 3,658 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 2026-09-03.
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