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daily-review

Generates daily work review reports from conversation history and usage data.

Updated Aug 23, 2026
Search Tags:#productivity#retrospective#json#daily review#conversation analysis#token usage#work summary
One-click install
npx skills add https://github.com/chhpt/skills --skill daily-review-chhpt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: daily-review
Source: https://github.com/chhpt/skills/tree/main/skills/daily-review
Command: npx skills add https://github.com/chhpt/skills --skill daily-review-chhpt

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? It turns raw JSON logs of yesterday's AI conversations, token usage, and workspace activity into a concise retrospective report, so you can spot unfinished threads, wasted spend, and reusable lessons without manually rereading every chat. ## Core Features & Use Cases - Global Overview: Summarizes the previous day's work in one sentence and breaks down time allocation across projects. - Per-Workspace Analysis: Produces conversation summaries, missed-thread reminders (unfinished replies, open TODOs), usage insights on model choice and token consumption, and code improvement suggestions. - Cross-Project Insights: Identifies reusable techniques across projects, flags neglected workspaces, and observes work patterns such as debugging versus planning. - Use Case: Each morning, feed the exported JSON of yesterday's sessions into the assistant and receive a Markdown standup-style recap highlighting two conversations you abandoned mid-thread and one project consuming disproportionate tokens. ## Quick Start Analyze my yesterday's work data JSON and generate a daily review report with summaries, missed items, and usage insights.

Frequently Asked Questions about daily-review

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

FAQPage Schema
How do I generate a daily work summary from AI chat history?▼

Provide a JSON file containing the date, workspace list with compressed conversations, usage statistics, and global stats. The assistant produces a Markdown report with a global overview, per-workspace summaries, missed items, and cross-project insights.

What input format does the daily review report require?▼

The input is structured JSON with a date field, a workspaces array containing workspaceName, conversations, and usage data, plus a globalStats object with total conversation counts and time allocation.

How does it detect unfinished conversations?▼

It flags conversations where lastMessageRole is user, indicating the exchange may have been interrupted, and where containsTodo is true, indicating pending tasks. It also reminds you about opened workspaces with no activity.

Can the report analyze token usage and model costs?▼

Yes, the usage module reviews whether high-cost models were used for simple tasks, checks for abnormal token consumption, and evaluates tool usage frequency and efficiency based on the usage statistics in the input.

What are the limitations of this daily review approach?▼

The report quality depends entirely on the completeness of the input JSON; it cannot access conversations or metrics not included in the data. Insights are limited to the summary, missed, usage, and code modules defined in the instructions.

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