Fetch GitHub issues, spawn sub-agents to implement fixes and open PRs, then monitor and address PR review comments. Usage: /gh-issues [owner/repo] [--label bug] [--limit 5] [--milestone v1.0] [--assignee @me] [--fork user/repo] [--watch] [--interval 5] [--reviews-only] [--cron] [--dry-run] [--model glm-5] [--notify-channel -1002381931352]
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Low
Low-risk findings worth noting
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tessl review fix ./apps/desktop/static/bundled-skills/gh-issues/SKILL.mdLow
Low-risk findings.
1 low severity finding. 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.
The required runtime workflow fetches GitHub Issue/PR text (including outsider-authored `body`, `title`, and review/comment `body` fields) via curl in Phase 2/Phase 6 and injects it into each sub-agent’s LLM context through the constructed task prompt variables like `{body}` and `{json_array_of_actionable_comments}`.
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