Analyze weekly or monthly work JSON to produce roadmap, trends, and research insights.
npx skills add https://github.com/zephyrwang6/allSkills --skill deep-review-zephyrwang6
Please help me install this Agent Skill. Skill: deep-review Source: https://github.com/zephyrwang6/allSkills/tree/main/deep-review Command: npx skills add https://github.com/zephyrwang6/allSkills --skill deep-review-zephyrwang6
这个 Skill 用于分析较长时间跨度的结构化工作数据，帮助你从周报或月报中看清项目进展、工作节奏和技术关注点，避免只看到零散记录却看不到整体趋势。
Use the deep-review skill to analyze the attached weekly work JSON and return roadmap, trends, and research insights.
High-intent search queries and answers about installing and using this skill.
Long-term work logs are analyzed by processing structured weekly or monthly review datasets to identify project progress, shifting time allocation, and recurring technical themes. This approach extracts quantitative evidence and conversation citations to reveal overall work trends.
The best way to generate roadmap insights from weekly work data is to evaluate each active project's completion status, direction deviations, and potential risks. This process yields actionable recommendations for the next steps in your project roadmap.
Yes, you can extract recurring technical discussion themes from monthly review datasets by distilling repeated technical topics and mapping knowledge associations. This analysis provides targeted learning and practice recommendations for technical research.
Work trend analysis requires structured work data, such as weekly work JSON, containing multiple active projects and shifting time allocations. Processing this structured input allows for accurate comparison of efficiency metrics and Token consumption across periods.
Comparing time allocation and efficiency metrics across review periods involves analyzing longitudinal work data to contrast work patterns, Token consumption, and high or low efficiency time distributions. This comparison highlights shifting work trends and structural optimizations.
Longitudinal data analysis for work reviews is not suitable for unstructured or single-day records lacking multiple active projects. It requires module-based output selection and quantitative evidence, making it less effective for isolated, brief tasks without recurring themes.
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