- 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_fetchto 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:
- Key metrics — Identify top-line numbers (revenue, growth, conversions, etc.)
- Trends — Period-over-period changes (up/down/flat + percentage)
- Highlights — Best-performing items, records, milestones
- Concerns — Underperforming areas, declining trends, anomalies
- 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.pyfor 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