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deep-review

Analyzes long-term structured work data to generate project roadmap, trend, and research insights.

Updated Aug 23, 2026
Search Tags:#productivity#report generation#retrospective#roadmap#trend analysis#work analysis#project review
One-click install
npx skills add https://github.com/chhpt/skills --skill deep-review-chhpt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-review
Source: https://github.com/chhpt/skills/tree/main/skills/deep-review
Command: npx skills add https://github.com/chhpt/skills --skill deep-review-chhpt

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Reviewing a week or month of work data manually makes it hard to spot project drift, time allocation problems, and knowledge gaps. This Skill turns long-span structured work logs into an organized deep analysis report. ## Core Features & Use Cases - Project Roadmap Analysis: Evaluates progress against goals for each active project, flags risks and blockers, and proposes concrete next actions. - Work Trend Analysis: Tracks time allocation shifts, work mode ratios (debugging/development/planning/learning), efficiency metrics, token consumption, and productivity rhythms. - Technical Research Insights: Identifies hot technical topics, maps relationships between themes, and recommends learning directions based on detected knowledge gaps. - Use Case: At the end of a month, feed your structured conversation and work logs in and receive a Markdown report covering each project's status, your efficiency trends, and a prioritized list of action items. ## Quick Start Analyze my work data from the past month and generate a deep review report covering roadmap, trends, and research modules.

Frequently Asked Questions about deep-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze my monthly work data for project insights?▼

Provide your structured work data in the same JSON format used for daily reviews, covering a week or month. Specify which modules to include (roadmap, trends, research) and the analysis returns a Markdown report with progress assessments, risks, and action items per project.

What input format does long-term work analysis require?▼

The input uses the same structured JSON format as the daily review, but spans a longer period such as a week or month with a larger data volume. The modules field controls which analysis dimensions appear in the output.

What analysis modules are available in a deep work review?▼

Three modules are available: roadmap for per-project progress and risk assessment, trends for time allocation and efficiency patterns, and research for technical topics and learning recommendations. You select modules via the modules field.

Can the analysis track token consumption and efficiency trends?▼

Yes, the trends module covers token consumption trends for cost control, conversation round counts, tool usage efficiency, and the distribution of high-productivity versus low-efficiency periods across the analyzed span.

What are the limitations of long-span work data analysis?▼

Conclusions depend on the completeness and quality of the input logs, since project direction and risk inferences are drawn from conversation content. Sparse or inconsistent data over the period reduces the reliability of trend and roadmap assessments.

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