Ai Tip From Hwchase17

v1.0.0

Provides AI learning tips by demonstrating how to create custom LangChain agents with structured outputs using Pydantic models.

0· 117·0 current·0 all-time
MIT-0
Download zip
LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
VirusTotalVirusTotal
Benign
View report →
OpenClawOpenClaw
Benign
high confidence
Purpose & Capability
The name/description promise (an AI tip about creating LangChain agents with structured outputs) matches the SKILL.md: a short code example showing a Pydantic model and llm.with_structured_output. No unrelated dependencies, credentials, or binaries are requested.
Instruction Scope
SKILL.md contains only a brief code example and description. It does not instruct the agent to read files, access environment variables, call external endpoints, or perform system-level actions outside the example.
Install Mechanism
There is no install spec and no code files beyond the SKILL.md and metadata. This minimizes risk; the only implied requirement is installing Python and the appropriate LangChain/Pydantic libraries if the user chooses to run the snippet.
Credentials
The skill declares no environment variables, credentials, or config paths. That is proportional for a learning/example skill. Note: actually running the example will require installing libraries but not secrets.
Persistence & Privilege
always is false and there are no instructions to modify agent/system configuration or persist credentials. The skill does not request elevated or persistent privileges.
Assessment
This skill is low-risk and simply shows a LangChain + Pydantic example. If you plan to run the code, install libraries from official sources (pip install from PyPI or the project's docs) and verify package names (e.g., langchain_core) match official packages. Because the skill has no provenance (source/homepage unknown), treat it as an unverified tip rather than authoritative documentation. Never paste secrets or credentials into example code or interactive prompts, and confirm any LLM/agent you connect to is from a trusted provider.

Like a lobster shell, security has layers — review code before you run it.

latestvk97ewk51zww4xk3vkvycazbdc983fgfg

License

MIT-0
Free to use, modify, and redistribute. No attribution required.

Comments