Automate Your Weekly Report with Golang & Genkit: AI‑Powered Workflow

Problem Statement

Developers often spend Friday afternoons manually gathering Git commits, Excel task tables, and document summaries to write a structured weekly report, which is time‑consuming and error‑prone, sometimes leading to missed tasks and performance penalties.

Solution Overview

The proposed solution is an automated weekly‑report generator that leverages Golang for high‑performance backend processing and Genkit to orchestrate AI model calls, turning raw data into a structured Markdown report.

Technology Stack & Architecture

Golang is chosen for its concurrency and easy deployment. Genkit connects AI models with custom tools that extract information from Git, Excel, and documentation sources. The data flow is:

Git repository → Git Tool →
Excel table → Excel Tool →
Document directory → Doc Tool →
Genkit Flow → AI generates structured JSON →
Export Tool → Markdown report

Data Structure Design

Two Go structs define the JSON schema required by Genkit:

type Metrics struct {
    Commits        string `json:"commits"`
    TestsAdded    string `json:"testsAdded"`
    CoverageChange string `json:"coverageChange"`
}

type WeeklyReport struct {
    Week           string   `json:"week"`
    Summary        string   `json:"summary"`
    KeyAchievements []string `json:"keyAchievements"`
    Issues         []string `json:"issues"`
    NextWeekPlan   []string `json:"nextWeekPlan"`
    Metrics        Metrics  `json:"metrics"`
}

All fields must be explicitly defined; otherwise Genkit returns a 400 error.

Flow Implementation

The core Genkit flow gathers data, builds a prompt, generates the report JSON, and exports it as Markdown:

reportFlow := genkit.DefineFlow(g, "generateWeeklyReport",
    func(ctx context.Context, input *types.WeeklyInput) (*types.WeeklyReport, error) {
        repoSum, _ := tools.GetGitSummary(ctx, input.RepoPath)
        excelSum, _ := tools.GetExcelSummary(ctx, input.ExcelPath)
        docSum := input.DocsSummary
        if docSum == "" {
            docSum, _ = tools.SummarizeDocs(ctx, g, "./docs")
        }
        prompt := fmt.Sprintf(`
请生成周报 JSON：
Git提交：%s
文档摘要：%s
Excel数据：%s
字段要求：week, summary, keyAchievements, issues, nextWeekPlan, metrics
`, repoSum, docSum, excelSum)
        report, _, err := genkit.GenerateData[types.WeeklyReport](ctx, g, ai.WithPrompt(prompt))
        if err != nil { return nil, err }
        report.Week = time.Now().Format("2006-01-02")
        _, _ = tools.ExportMarkdown(ctx, report)
        return report, nil
    })

This flow acts like a smart assistant: you feed it raw Git, Excel, and document inputs, and it outputs a ready‑to‑publish Markdown report.

Sample Output

# 本周周报（2025-11-04）

## 概述
本周完成接口优化与报表导出功能初步上线。

## 主要成果
- API重构，缓存优化
- 测试覆盖率提升
- 文档自动摘要完成

## 问题与风险
- 测试环境偶发延迟

## 下周计划
- 日志监控优化
- 报表功能完善

## 关键指标
- 提交次数：24
- 新增测试：18
- 覆盖率变化：+3.2%

The automation saves at least 30 minutes of repetitive work each week.

Practical Tips

Register custom tools : Connect Git, Excel, and document sources to the flow.

Define explicit JSON schema : Ensure all fields in Metrics are non‑empty.

Document summarization : AI‑driven summarization is several times faster than manual reading.

Extensibility : Use RAG to reference historical reports for consistent style.

Cross‑platform considerations : Handle PowerShell JSON escaping and configure domestic proxies if needed.

Conclusion

By combining Golang, Genkit, and a few minutes of configuration, developers can replace manual weekly‑report writing with an AI‑assisted pipeline, freeing time for core development tasks. The source code is available on GitHub and Gitee for further customization.