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 langchaingit 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/langchain)<a href="https://agentmods.dev/skills/omar-obando/qwen-orchestrator/langchain"><img src="https://agentmods.dev/badge/skills/omar-obando/qwen-orchestrator/langchain/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/langchain"><img src="https://agentmods.dev/badge/skills/omar-obando/qwen-orchestrator/langchain.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 | $0.02663 |
| Opus 5.5 | $0.00023 | $0.01065 |
| Sonnet 5.5 | $0.00011 | $0.00533 |
| Haiku 4.5 | $0.00006 | $0.00266 |
Grade A, and why
langchain 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 1mo 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 — 448 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LangChain Skill — LLM Applications & Agent Engineering
Overview
This skill provides comprehensive guidance for building LLM applications with LangChain, implementing chains, agents, tools, memory, prompts, and retrieval systems. It includes best practices for prompt engineering, tool integration, and agent development. Based on LangChain/LangGraph official documentation and agent development best practices.
When to Use
Use this skill when:
- Building LLM applications with LangChain
- Implementing chains (LLMChain, SequentialChain, RouterChain)
- Creating agents with tools and capabilities
- Integrating tools and external APIs (search, calculators, databases)
- Managing memory (ConversationBufferMemory, VectorStoreRetrieverMemory)
- Creating prompts and templates (PromptTemplate, FewShotPromptTemplate)
- Building retrieval-augmented generation (RAG) systems
- Implementing document loaders and parsers
- Using embeddings for semantic search
- Building chat applications with conversation history
- Implementing output parsers (StructuredOutputParser, JsonOutputParser)
- Creating prompt engineering patterns (few-shot, chain-of-thought)
- Building agents with tool calling capabilities
- Implementing agent memory with vector stores
- Creating agents with external knowledge sources
- Building agents with multi-step reasoning
- Using LangSmith for tracing and monitoring
- Implementing LangChain expression language (LCEL)
- Building agents with streaming output
- Creating agents with context window management
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 simple LLM calls without chains (use llm-integrations skill)
- Managing agent teams and coordination (use agent-task-coordinator skill)
- Building Qwen-specific agents (use qwen-agent skill)
- Implementing complex graph-based agent architectures (use langgraph 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.
- 1mo ago First seen · 448 lines · 57 tokens per session scan A 2ae346a4d9df
langchain is a skill published in the GitHub repository Omar-Obando/qwen-orchestrator (52 stars, last pushed 4mo ago), licensed MIT. It adds 57 tokens to every session and 2,663 once invoked, about $0.0002 per session on Opus 5.5. 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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langfuse
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