langchain-architecture
Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
74
66%
Does it follow best practices?
Impact
82%
2.34xAverage score across 3 eval scenarios
Risky
Do not use without reviewing
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/llm-application-dev/skills/langchain-architecture/SKILL.mdSecurity
1 high severity finding. You should review these findings carefully before considering using this skill.
W007: Insecure credential handling detected in skill instructions
What this means
The skill handles credentials insecurely by requiring the agent to include secret values verbatim in its generated output. This exposes credentials in the agent’s context and conversation history, creating a risk of data exfiltration.
Why it was flagged
Insecure credential handling detected (high risk: 0.80). The prompt includes insecure examples that hardcode credentials and plaintext secrets (e.g., os.environ["LANGCHAIN_API_KEY"] = "your-api-key", a postgres URL with user:pass, and a test where the agent remembers/outputs "12345"), which require the model to handle or reproduce secret values verbatim.
- Repository
- wshobson/agents
- Commit
91fe43e
- Audited
- Security analysis
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.