Langchain V1 Toolkit

LangChain v1：把 LLM、prompt、tool、retriever、parser 暴露为 Runnable，用 `|` 操作符（LCEL）组合成统一 invoke / stream / batch 接口的链。 LangChain v1: exposes LLMs, prompts, tools, r...

Security Scan

The skill's documentation and runtime YAML broadly match a LangChain/finance knowledge pack, but the runtime instructions expect filesystem and environment access (and ask the host to run Python commands and installs) that are not declared in the skill metadata — this mismatch warrants caution.

Purpose & Capability

Name/description and included seed.yaml indicate an AI-engineering / LangChain v1 knowledge toolkit for building agents and finance workflows; that purpose aligns with the large seed.yaml, intent_router, and preconditions. However, the skill metadata declares no required env vars or binaries while the runtime content expects Python runtime/tools and provider packages (langchain, zvt, partner packages), so the declared requirements understate the actual runtime needs.

Instruction Scope

SKILL.md and seed.yaml instruct the host AI to re-read references/seed.yaml at runtime and to execute precondition check_commands such as python3 -c 'import zvt...' and filesystem checks (ZVT_HOME, write tests). The instructions therefore direct the host to run arbitrary Python commands, check and create files in the host workspace/home, and suggest pip installs. Those actions are within this skill's stated purpose (backtesting / LangChain patterns) but they expand the agent's scope to the host filesystem and environment — something the metadata did not declare explicitly.

Install Mechanism

There is no install spec (instruction-only), which avoids an automated download/execute risk. But the runtime docs and preconditions explicitly instruct to pip install langchain and provider packages if missing. That makes the effective installation manual/host-driven rather than automatic; it's expected but worth noting because the skill instructs host-side package installs.

Credentials

Declared required env vars: none. Yet seed.yaml and preconditions reference environment variables and filesystem state (ZVT_HOME, host_workspace, paths under {host_workspace}, checks that touch ~/.zvt). This is a discrepancy: the skill will read and act on environment variables and file paths even though none are declared. No explicit credential or network exfiltration env vars appear, but the mismatch is a proportionality and transparency concern.

Persistence & Privilege

always:false and no code files means it won't be force-included or install binaries by itself. The execution protocol expects writing to host workspace paths (scripts/, skills/, .trace/) and running write-permission checks in ~/.zvt; these are reasonable for a backtesting tool but do grant the skill the ability to create files on the host when the host follows its instructions. This is expected but should be considered when running on sensitive hosts.

What to consider before installing

This skill is a documentation/knowledge pack for LangChain v1 and a finance blueprint; it requires the host to run Python checks and (if missing) pip installs and will read/create files (ZVT_HOME, host_workspace paths). Before installing or invoking: (1) review references/seed.yaml yourself to confirm it doesn't reference unexpected external endpoints or secrets; (2) run it in an isolated environment or container if you are worried about package installs or filesystem writes; (3) be aware the skill will expect access to your Python runtime, ability to run python3 -c commands, and write permissions under your home/workspace; (4) if you want to limit scope, reject or sandbox any automated pip installs and review precondition commands before allowing them to run.

这个 skill 适合什么用户？能做哪些任务？

概览

LangChain 是构建 LLM 应用的事实标准 Python 框架（github.com/langchain-ai/langchain）。v1 包（v1.2.15）有意保持精简：核心是 agents.create_agent（返回 LangGraph CompiledStateGraph）、chat_models.init_chat_model 工厂、message types 重导出和 tools/embeddings shim。

历史 Chain / LLMChain / Memory / AgentExecutor 接口已迁到 `langchain-clas...

Doramagic 晶体页: https://doramagic.ai/zh/crystal/langchain-v1-toolkit

知识规模

• 51 条约束 (1 fatal + 50 non-fatal)
• 上游源码: langchain-ai/langchain @ commit 87ba30f0
• 蓝图 ID: finance-bp-132

用法

Host AI（Claude Code / Cursor / OpenClaw）读 references/seed.yaml，按其中的：

• intent_router 匹配用户意图
• architecture 理解项目架构
• constraints 应用 anti-pattern 约束
• business_decisions 参考核心设计决策

FAQ 摘要

这个 skill 适合什么用户？能做哪些任务？

适合用 LangChain 构建 LLM 应用的工程师：tool-calling agent、结构化输出、RAG pipeline、流式输出、模型 fallback、PII 脱敏等。v1 后 agent 走 LangGraph 路径，旧 AgentExecutor 仍可用但建议迁移。访问 doramagic.ai/r/langchain 查看完整用例。

需要准备什么环境？依赖什么？

Python（具体版本见 langchain_v1/pyproject.toml），pip install langchain 自动带 LangGraph 作为硬运行时依赖。每个 provider 需单独安装 partner 包（如 langchain-openai、langchain-anthropic）。

会踩哪些坑？这个 skill 怎么防护？

本 skill 内置 51 条约束。典型踩坑：(1) BaseMemory 与所有 Conversation*Memory 已 @deprecated，BaseMemory 已从 langchain_core 删除；

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完整文档: 见 references/seed.yaml (v6.1 schema). 浏览页: https://doramagic.ai/zh/crystal/langchain-v1-toolkit