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langbot-plugin-dev

Develop, debug, and test LangBot plugins. Use when creating new LangBot plugins, fixing plugin bugs, setting up a LangBot test environment, or testing plugins via WebSocket. Covers plugin component architecture (EventListener, Command, Tool), the plugin SDK API (invoke_llm, get_llm_models, send_message, plugin storage), common pitfalls, and automated WebSocket-based testing. Triggers on "langbot plugin", "lbp", "GroupChatSummary", "plugin debug", "langbot test".

68

Quality

81%

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SecuritybySnyk

High

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SKILL.md
Quality
Evals
Security

Security

1 high severity finding. You should review these findings carefully before considering using this skill.

High

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

The prompt includes explicit example secrets and commands that embed tokens/passwords (e.g., "test123", "sk-xxx", "lbpat_xxx", "eyJ...") and shows passing tokens on the CLI or in JSON bodies, which would require an LLM to handle or reproduce secret values verbatim if real credentials are inserted.

Report incorrect finding

Low

Low-risk findings.

1 low severity finding. Worth noting, but not necessarily harmful.

Low

W011: Third-party content exposure detected (indirect prompt injection risk).

What this means

The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.

Why it was flagged

LangBot pipeline/events ingest outsider-authored chat message text at runtime via the `*MessageReceived` / `*NormalMessageReceived` event flow (e.g., WebSocket `.../pipelines/<pipeline_uuid>/ws/connect?session_type=group` sending `type: "Plain", text: ...`), which the LLM context can then consume.

Repository
langbot-app/LangBot
Audited
Security analysis
Snyk

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