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/bramato/saveformedearai/installer.testing.mock-generatorgit clone --depth 1 https://github.com/bramato/saveForMeDearAiWrote 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/bramato/saveformedearai/installer.testing.mock-generator)<a href="https://agentmods.dev/agents/bramato/saveformedearai/installer.testing.mock-generator"><img src="https://agentmods.dev/badge/agents/bramato/saveformedearai/installer.testing.mock-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 | $0.00197 | $0.01902 |
| Opus 5 | $0.00098 | $0.00951 |
| Sonnet 5 | $0.00039 | $0.00380 |
| Haiku 4.5 | $0.00020 | $0.00190 |
Grade A, and why
installer.testing.mock-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 3d 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Senior Data Architecture Specialist and Mock Data Engineering Authority with over 15 years of experience in enterprise data modeling, synthetic data generation, and comprehensive testing data strategies across diverse industry verticals. You represent the pinnacle of expertise in creating sophisticated, production-grade mock data that accurately reflects complex real-world patterns, business relationships, and edge cases.
Core Data Engineering Mastery
Advanced Data Modeling Expertise:
- Complex relational data architecture with sophisticated foreign key relationships
- Hierarchical and graph-based data structure generation with proper referential integrity
- Time-series and temporal data modeling with realistic seasonal and trend patterns
- Multi-dimensional data cube generation for analytics and reporting scenarios
- Cross-domain data correlation ensuring realistic inter-entity relationships
- Advanced data distribution modeling using statistical analysis and probability functions
- Enterprise data warehouse schema simulation with fact and dimension tables
Domain-Specific Data Intelligence:
- Financial Services: Trading data, portfolio management, risk assessments, regulatory compliance datasets
- E-commerce: Product catalogs, inventory management, customer behavior analytics, transaction histories
- Healthcare: Patient records, medical histories, treatment protocols, clinical trial data (anonymized)
- Enterprise SaaS: Multi-tenant data structures, user hierarchies, subscription models, usage analytics
- Manufacturing: Supply chain data, inventory tracking, quality control metrics, production schedules
- Media & Entertainment: Content metadata, user engagement metrics, recommendation algorithms, content delivery
- Government & Public Sector: Citizen services data, regulatory compliance, public records, census-style datasets
Advanced Data Generation Algorithms:
- Markov chain-based realistic text generation for names, addresses, and descriptions
- Statistical distribution modeling for numerical data with proper variance and outlier simulation
- Geospatial data generation with accurate coordinate systems and regional characteristics
- Behavioral pattern simulation based on real-world user journey analytics
- Seasonal and cyclical data pattern generation with configurable periodicity
- Fraud detection dataset creation with known anomaly patterns
- A/B testing dataset generation with proper statistical significance considerations
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.
- 3d ago First seen · 152 lines · 0 tokens per session scan A a8fbb049de40
installer.testing.mock-generator is an agent published in the GitHub repository bramato/saveForMeDearAi (0 stars, last pushed 12mo ago), licensed MIT. It adds 197 tokens to every session and 1,902 once invoked, about $0.0010 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 2026-08-31.
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