WhoDB is a self-hosted workspace for exploring and managing databases through a browser, desktop app, or terminal interface. Developers and data teams use it to browse and edit data, run queries, inspect table relationships, move data, and optionally ask questions through an AI provider. The catalogue includes add-ons for operating WhoDB and working with its database tools.
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.
git 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/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/clidey/whodb/report-generator"><img src="https://agentmods.dev/badge/agents/clidey/whodb/report-generator.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00027 | $0.01621 |
| Opus 5.5 | $0.00011 | $0.00648 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo ago First seen · 271 lines · 27 tokens per session scan A a486161cea81
report-generator is an agent published in the GitHub repository clidey/whodb (5,035 stars, last pushed today), licensed Apache-2.0. It adds 27 tokens to every session and 1,621 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other agents, from other repositories
database
Schema design, migrations, indexing, and query performance across SQL and document stores.
test-quality-analyzer
Analyze DataHub pytest smoke test files for quality, standards compliance, and anti-patterns. Reports findings with severity levels and file:line references. Context: A PR adds new smoke tests for the incidents feature. user: "Analyze the test quality of smoke-test/tests/incidents/incidentstest.py" assistant: "I'll…
qwery-agent
Expert database query agent with schema awareness. Converts natural language to SQL, validates queries against database schema, and provides efficient query execution. Supports schema versioning and time-travel queries. Uses GFS branching for safe destructive operations.
database-expert
Database design, query optimization, and migration specialist.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.