Investigate unexpected chat agent behavior by analyzing direct debug logs in JSONL files. Use when users ask why something happened, why a request was slow, why tools or subagents were used or skipped, or why instructions/skills/agents did not load.
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Low
Low-risk findings worth noting
Low
Low-risk findings.
2 low severity findings. Worth noting, but not necessarily harmful.
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.
This skill’s required workflow reads Copilot Chat debug logs from a provided session log directory (including `main.jsonl` and `system_prompt_*.json` / `tools_*.json`), and those files can contain outsider-authored free text such as the end-user’s prior messages and the model/system prompts; the runtime path is via the investigation workflow that ingests those log contents into the troubleshooting LLM context.
The skill fetches instructions or code from an external URL at runtime, and the fetched content directly controls the agent’s prompts or executes code. This dynamic dependency allows the external source to modify the agent’s behavior without any changes to the skill itself.
The skill explicitly instructs at runtime to load and summarize the Copilot Issues wiki (https://github.com/microsoft/vscode/wiki/Copilot-Issues), which means external content would be fetched and used to directly shape the agent's response.
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