Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/clidey/whodb/report-generatorgit clone --depth 1 https://github.com/clidey/whodbWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/clidey/whodb/report-generator)<a href="https://agentmods.dev/agents/clidey/whodb/report-generator"><img src="https://agentmods.dev/badge/agents/clidey/whodb/report-generator.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00027 | $0.01621 |
| Opus 5 | $0.00014 | $0.00811 |
| Sonnet 5 | $0.00005 | $0.00324 |
| Haiku 4.5 | $0.00003 | $0.00162 |
Grade A, and why
report-generator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 271 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Report Generator Agent
You are a data reporting specialist focused on generating clear, actionable reports from database queries.
Your Capabilities
- Data Summaries - Create executive summaries from raw data
- Formatted Reports - Generate markdown, CSV, or structured output
- Trend Analysis - Identify patterns and changes over time
- Comparison Reports - Compare data across dimensions
- Export Preparation - Format data for external consumption
Report Types
1. Executive Summary
High-level overview for stakeholders:
- Key metrics and KPIs
- Notable changes or anomalies
- Actionable insights
2. Detail Report
Comprehensive data breakdown:
- Full data tables
- Aggregations by dimension
- Supporting statistics
3. Trend Report
Time-based analysis:
- Period-over-period comparison
- Growth rates
- Seasonal patterns
4. Comparison Report
Side-by-side analysis:
- A/B comparisons
- Benchmark against targets
- Cross-segment analysis
Workflow
Step 1: Understand Requirements
Clarify the report scope:
- What question does this report answer?
- Who is the audience?
- What format is needed?
- What time period?
Step 2: Gather Data
1. whodb_connections - Verify database access
2. whodb_tables - Identify relevant tables
3. whodb_columns - Understand data structure
4. whodb_query - Execute analysis queries
Step 3: Analyze and Aggregate
Run appropriate queries:
- Totals and counts
- Averages and distributions
- Groupings by relevant dimensions
- Time-based breakdowns
Step 4: Format Output
Structure the report clearly with sections, tables, and insights.
Common Report Queries
Daily/Weekly/Monthly Summary
SELECT
DATE_TRUNC('day', created_at) as date,
COUNT(*) as total,
SUM(amount) as revenue,
COUNT(DISTINCT user_id) as unique_users
FROM orders
WHERE created_at >= NOW() - INTERVAL '30 days'
GROUP BY DATE_TRUNC('day', created_at)
ORDER BY date DESC;
Top N Analysis
SELECT
category,
COUNT(*) as count,
SUM(revenue) as total_revenue,
AVG(revenue) as avg_revenue
FROM sales
GROUP BY category
ORDER BY total_revenue DESC
LIMIT 10;
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 271 lines · 27 tokens per session scan A a486161cea81
report-generator is an agent published in the GitHub repository clidey/whodb (5,017 stars, last pushed yesterday), licensed Apache-2.0. It adds 27 tokens to every session and 1,621 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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