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Structured, hypothesis-driven research across large corpora. Use for complex research tasks, literature reviews, competitive analysis, or anywhere Claude needs to synthesize information from many sources with tracked confidence.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Make Claude default to implementing changes rather than just suggesting them. Use when the model keeps proposing edits instead of applying them, or when you want a bias toward action in an agentic coding context.
Raise recall in code review — get Claude to report every bug it finds, including low-confidence ones. Use for code review harnesses, security audits, or any workflow where missing a real bug is worse than a few false positives.
Stop Claude from leaving behind temporary scripts, scratch files, and iteration helpers after a task. Use when you want a clean workspace — no extra files beyond the requested deliverables.
Make Claude's responses shorter and more focused. Use when the model is over-explaining, adding non-essential context, or producing verbose answers for simple lookups.
Make Claude ask before taking destructive or hard-to-reverse actions (rm, force-push, dropping tables, posting publicly). Use in autonomous agents that touch shared systems or in any context where the blast radius of a wrong action is high.
Get visual variety by having Claude propose multiple design directions before building. Use for open-ended design briefs, landing pages, brand work, or any frontend where you want to pick a direction rather than accept the default.
| name | vojta-deep-research |
| description | Structured, hypothesis-driven research across large corpora. Use for complex research tasks, literature reviews, competitive analysis, or anywhere Claude needs to synthesize information from many sources with tracked confidence. |
Cheap way to upgrade ad-hoc searching into a systematic research loop.
Search for this information in a structured way. As you gather data, develop several competing hypotheses. Track your confidence levels in your progress notes to improve calibration. Regularly self-critique your approach and plan. Update a hypothesis tree or research notes file to persist information and provide transparency. Break down this complex research task systematically.
Before starting, define:
Hypothesis tracking forces the model out of the "one linear search path" trap and into branching exploration with self-critique. Confidence tracking gives you a dial for filtering findings later.