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 langgraphgit 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/langgraph)<a href="https://agentmods.dev/skills/omar-obando/qwen-orchestrator/langgraph"><img src="https://agentmods.dev/badge/skills/omar-obando/qwen-orchestrator/langgraph/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/langgraph"><img src="https://agentmods.dev/badge/skills/omar-obando/qwen-orchestrator/langgraph.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 | $0.00057 | $0.02662 |
| Opus 5 | $0.00028 | $0.01331 |
| Sonnet 5 | $0.00011 | $0.00532 |
| Haiku 4.5 | $0.00006 | $0.00266 |
Grade A, and why
langgraph 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 5d 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 — 437 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LangGraph Skill — Stateful Agents & Graph Workflows
Overview
This skill provides comprehensive guidance for building stateful agents and workflows with LangGraph, implementing graph-based architectures, managing state persistence, human-in-the-loop capabilities, and multi-agent systems. It includes Python and JavaScript implementations. Based on LangChain/LangGraph official documentation and agent development best practices.
When to Use
Use this skill when:
- Building stateful agents with LangGraph
- Implementing graph-based architectures with StateGraph
- Managing state persistence and checkpoints
- Creating human-in-the-loop workflows with interrupts
- Building multi-agent systems with handoffs
- Implementing durable execution and memory
- Using checkpointer strategies (InMemory, SQLite, PostgreSQL)
- Implementing thread management and session persistence
- Building agent swarms with LangGraph Swarm
- Creating conditional edges and routing logic
- Implementing agent communication patterns
- Setting up LangGraph Studio for debugging
- Using LangSmith for tracing and monitoring
- Building agents with tool calling capabilities
- Implementing retry and error handling patterns
- Creating agents with streaming output
- Building agents with context window management
- Implementing agent memory with vector stores
- Creating agents with external knowledge sources
- Building agents with multi-step reasoning
Do NOT use this skill when:
- Building basic LLM applications without state (use llm-integrations skill)
- Designing database schema (use database-design skill)
- Creating UI components (use frontend-design skill)
- Writing simple prompt templates (use langchain skill for basic LLM operations)
- Implementing non-agent workflows (use standard application development skill)
- Building simple chat applications without complex state (use qwen-agent skill for Qwen-specific apps)
- Managing agent teams across multiple agents (use agent-task-coordinator skill)
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.
- 5d ago First seen · 437 lines · 57 tokens per session scan A f7227fe9e5fb
langgraph is a skill published in the GitHub repository Omar-Obando/qwen-orchestrator (49 stars, last pushed 2mo ago), licensed MIT. It adds 57 tokens to every session and 2,662 once invoked, about $0.0003 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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