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Client Report Generator

这是一款专业客户报告生成工具，核心功能是从CSV、JSON、文本、网页等各类原始数据中提取分析关键指标、趋势等信息，生成符合需求的客户交付报告。典型场景涵盖周/月绩效回顾、营销活动结果、项目状态更新、数据分析总结等。支持Markdown、HTML、纯文本、JSON等多种输出格式，还可自定义客户名称、报告周期、语气等，适配不同需求。

charlie-morrison
charlie-morrison
热度 53.27
最近更新 2026-05-11 17:42:01
分类：效率依赖：代码执行 / 联网浏览搜索 / 读写文件 / 内部调用大模型应用场景：报告撰写 / 数据处理 / 摘要总结 / 模板

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add client-report-generator
Metadata
name
client-report-generator
description
Generate professional client-facing reports from raw data, metrics, and KPIs. Supports analytics summaries, project status reports, monthly/weekly performance reviews, and campaign results. Use when asked to create a client report, generate a performance report, summarize metrics for a client, build a weekly/monthly report, create a project status update, format analytics data into a report, or produce a deliverable report from raw data. Triggers on "client report", "performance report", "weekly report", "monthly report", "status report", "generate report from data", "metrics report", "campaign report", "analytics summary".

Client Report Generator

Generate polished, client-ready reports from raw data. Feed it CSV, JSON, analytics exports, or plain text metrics — get back a professional report formatted for delivery.

Workflow

1. Ingest Data

Determine input type and extract data:

  • CSV/TSV file → Read and parse into structured data
  • JSON file/API response → Parse and extract key metrics
  • Pasted text/numbers → Parse inline data
  • URL (dashboard/analytics) → Use web_fetch to extract visible data
  • Multiple sources → Combine into unified dataset

Run scripts/parse_data.py to normalize any structured input:

python3 scripts/parse_data.py <input-file> [--format csv|json|auto]

Output: normalized JSON with detected metrics, dimensions, and time ranges.

2. Analyze & Summarize

Before generating the report, analyze the data:

  1. Key metrics — Identify top-line numbers (revenue, growth, conversions, etc.)
  2. Trends — Period-over-period changes (up/down/flat + percentage)
  3. Highlights — Best-performing items, records, milestones
  4. Concerns — Underperforming areas, declining trends, anomalies
  5. Context — Infer reporting period, industry, and audience from data

3. Select Report Template

Choose based on user request or data type. See references/report-templates.md for detailed templates.

Template Best For
Performance Review Monthly/weekly KPI summaries
Campaign Report Marketing campaign results
Project Status Development/project progress updates
Analytics Summary Website/app analytics overview
Custom User-specified structure

4. Generate Report

Structure every report with:

# [Report Title]
**Period:** [date range]  |  **Prepared for:** [client name]  |  **Date:** [today]

## Executive Summary
[2-3 sentences: what happened, key takeaway, recommendation]

## Key Metrics
| Metric | Current | Previous | Change |
|--------|---------|----------|--------|
| ...    | ...     | ...      | +X%    |

## [Detailed Sections — template-specific]

## Highlights & Wins
- ...

## Areas for Improvement
- ...

## Recommendations & Next Steps
1. ...

5. Format Output

Default output: Markdown (clean, portable, renders in most tools)

Other formats on request:

  • HTML → Run scripts/report_to_html.py for styled HTML with inline CSS
  • Plain text → Stripped formatting for email body
  • Structured data → JSON summary of all metrics and analysis
python3 scripts/report_to_html.py <report.md> [--template default|minimal|branded]

Customization Options

Users can specify:

  • Client name — appears in header and throughout
  • Reporting period — "last week", "March 2026", "Q1 2026"
  • Tone — professional (default), friendly, executive-brief
  • Sections — include/exclude specific sections
  • Branding — company name, colors (for HTML output)
  • Comparison — vs previous period, vs target/goal, vs benchmark
  • Charts — include ASCII/text charts for key metrics (when data supports it)
  • Language — generate in specified language

Data Handling

  • Automatically detect metric types (currency, percentages, counts, rates)
  • Format numbers appropriately (commas, decimal places, currency symbols)
  • Calculate period-over-period changes when historical data is available
  • Flag statistical anomalies or significant changes (>20% swings)
  • Round appropriately for audience (executives get rounded numbers, analysts get precision)

Tips

  • For executive audiences: lead with the bottom line, keep it to 1 page equivalent
  • For marketing reports: emphasize ROI and conversion metrics
  • For project status: focus on timeline, blockers, and deliverables
  • When data is incomplete: note gaps clearly, don't fabricate numbers
  • Include "So what?" after every metric — explain why the number matters