The Agent Skills Directory

[EXTERNAL_DOWNLOADS]: The skill references standard, well-known libraries including langgraph, langchain, and langchain_openai. These are industry-standard packages from the LangChain ecosystem and do not pose a security risk in this context.
[COMMAND_EXECUTION]: The code examples demonstrate the use of tools, such as a calculator and a search tool. The calculator example uses a placeholder function safe_math_evaluator, which implies the use of a secure evaluation method rather than unsafe direct execution of user strings.
[DATA_EXFILTRATION]: No evidence of data exfiltration was found. The skill describes persistence using SqliteSaver and PostgresSaver, which are standard mechanisms for maintaining agent state across sessions within the user's controlled environment.
[INDIRECT_PROMPT_INJECTION]: The skill describes agents that ingest untrusted user input via message lists and query strings.
Ingestion points: User input enters the graph through the app.invoke method and is stored in AgentState under the messages key.
Boundary markers: The provided code examples do not explicitly demonstrate the use of system prompts or delimiters to isolate user input, which is typical for simplified educational snippets.
Capability inventory: The agents are capable of calling LLMs, executing predefined tools (search/calculator), and reading/writing state to a database.
Sanitization: Input sanitization is not explicitly implemented in the example code, representing a standard surface for indirect prompt injection common to LLM application development. This is documented here as an inherent architectural risk of building agents, rather than a malicious feature of the skill.

langgraph