研究员技能 是 AI Skill Hub 本期精选Prompt模板之一。综合评分 7.5 分，整体质量较高。我们推荐使用将其纳入你的 AI 工具库，帮助提升工作效率。
研究员技能 是经过精心设计和反复验证的专业 Prompt 模板集合。这些 Prompt 框架能够有效激活 Claude、ChatGPT 等大型语言模型的深层能力，让 AI 生成更准确、更有价值的输出结果。无需任何安装，直接复制模板内容到 AI 对话框即可使用。
研究员技能 是经过精心设计和反复验证的专业 Prompt 模板集合。这些 Prompt 框架能够有效激活 Claude、ChatGPT 等大型语言模型的深层能力，让 AI 生成更准确、更有价值的输出结果。无需任何安装，直接复制模板内容到 AI 对话框即可使用。
# Prompt 无需安装，直接复制使用 # 支持：Claude / ChatGPT / Gemini / 通义千问 等主流模型 # 使用步骤 # 1. 复制 Prompt 模板内容 # 2. 粘贴到 AI 对话框 # 3. 替换 [占位符] 为实际内容 # 4. 发送后获取结构化输出 # 获取原始文件 git clone https://github.com/krzysztofdudek/ResearcherSkill
# 粘贴到 Claude/ChatGPT 使用 # 示例 Prompt 结构： 你是一位 [角色]，擅长 [领域]。 请根据以下要求完成任务： 任务背景：[描述背景] 具体要求：[详细说明] 输出格式：[期望格式] # 将 [] 内内容替换为实际需求
# researcherskill 配置文件示例（config.yml） app: name: "researcherskill" debug: false log_level: "INFO" # 运行时指定配置文件 researcherskill --config config.yml # 或通过环境变量配置 export RESEARCHERSKILL_API_KEY="your-key" export RESEARCHERSKILL_OUTPUT_DIR="./output"
<img width="1376" height="768" alt="Gemini_Generated_Image_5z6cxx5z6cxx5z6c" src="https://github.com/user-attachments/assets/02c3c4cd-fe2a-4fcd-b414-29bd84f5a741" />
Two slash commands inside Claude Code — first registers this repo as a marketplace, second installs the plugin from it:
/plugin marketplace add krzysztofdudek/ResearcherSkill
/plugin install researcher@researcher-marketplaceRestart Claude Code (or run /plugin reload) and trigger the skill with /researcher or by asking the agent to run a research loop on something.
To upgrade later: /plugin marketplace update researcher-marketplace then /plugin install researcher@researcher-marketplace again.
How is this different from autoresearch? Autoresearch's core loop is universal, but the repo is wired to train.py, val_bpb, and GPU training. To use it on something else you'd rewrite the setup. This gives you that loop ready to go for any codebase.
When would I use this instead of ML? It's not instead of ML. ML is one possible domain. This works on anything where the agent can try things, measure, and iterate. Code, scripts, documents, configs. Slow builds, flaky tests, API latency, prompt accuracy.
How does it measure success for non-ML code? Whatever you can measure. Test pass rate, benchmark output, type check errors, build time. You set it up in the discovery phase. The agent asks what to measure and how. If you can run a command and get a number, that's your metric. For cases where there's no command to run, the agent scores against a qualitative rubric you define together.
How does convergence detection work? The agent checks a table of signals after every experiment. If it sees 5+ failures in a row, a metric plateau, or the same area modified too many times, it knows to change approach. Some signals are advisory (consider pivoting), others are hard guardrails (you must pivot). Details in the guide.
Can it improve itself? Sort of. The skill was optimized using the skill itself. A research document about how LLMs process instructions (attention decay, primacy/recency, instruction budgets) was used as criteria, and the agent ran the loop against its own prompt. Not fully recursive, but the loop was: research → skill → use skill to improve skill.
Can't I just ask Claude to build this from the autoresearch repo? You can try. This saves you the work and includes things autoresearch doesn't have: thought experiments, non-linear branching, convergence detection, qualitative metrics, and session resume.
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一，可自由商用、修改、分发，仅需保留版权声明。
经综合评估，研究员技能 在Prompt模板赛道中表现稳健，质量良好。如果你已有明确的使用需求，可以直接上手体验；如果还在评估阶段，建议对比同类工具后再做决策。
| 原始名称 | ResearcherSkill |
| 原始描述 | 开源Prompt模板：One file. Your AI coding agent becomes a scientist. 30+ experiments while you sl。⭐225 · Python |
| Topics | ai-agentai-codingautonomousautoresearch |
| GitHub | https://github.com/krzysztofdudek/ResearcherSkill |
| License | MIT |
| 语言 | Python |
收录时间：2026-05-25 · 更新时间：2026-05-26 · License：MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型，复制安装指令后粘贴到对应客户端