Provide implementations of advanced data structures
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This skill should be used when the user asks to "babysit issues", "work on assigned issues", "check a5c-agent issues", "process babysitter issues", or wants to find and work on open GitHub issues assigned to a5c-agent in the babysitter repo.
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安装前可以查看交给 AI 代理的指令内容。
Fetch open GitHub issues from https://github.com/a5c-ai/babysitter/issues assigned to a5c-agent, then orchestrate work on each issue via /babysitter:call.
Use the gh CLI to list open issues assigned to a5c-agent:
gh issue list --repo a5c-ai/babysitter --assignee a5c-agent --state open --json number,title,url,labels --limit 50
If no issues are found, report that there are no open issues assigned to a5c-agent and stop.
Display the list of open issues to the user with their number, title, labels, and URL.
For each issue, invoke the babysitter:yolo skill with a prompt that includes the issue URL and context:
/babysitter:yolo work on this GitHub issue: <issue_url>
If multiple issues are selected, process them sequentially -- complete one before starting the next. Present a summary after each issue is processed. as part of the process, create a new branch from staging named issue-<number> and push commits to that branch. then create a pull request against staging with a meaningful name and a description of the work done (with link to the original issue). if the issue has a "bug" label, prioritize fixing the bug and include details about the bug and how it was fixed in the pull request description.
After all open a5c-assigned issues have been processed, provide a summary of what was done for each issue.
a5c-agent are fetched. Other assignees are ignored.gh CLI must be authenticated. If authentication fails, prompt the user to run gh auth login./babysitter:yolo which handles the actual implementation work.还没有评价。欢迎分享你的使用感受。
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Provide implementations of advanced data structures
中文简介正在准备中。当前显示原文说明。
Fresh adversarial code review with binary PASS/FAIL verdicts, evidence citations, and anchoring bias prevention via fresh reviewer spawning.
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WASM-based instant code transforms for simple tasks, achieving 352x speedup over LLM inference with zero cost.
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Coordinate Crew (persistent) and Polecat (transient) agents using Gas Town's hook-based work distribution and GUPP principle.
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Generate visual representations of algorithm execution
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Hierarchical coordination and drift detection with frequent checkpoints, shared memory coherence validation, role specialization enforcement, and short task cycles.
中文简介正在准备中。当前显示原文说明。