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/eai-support/eai-goferWrote 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/eai-support/eai-gofer/plan-data-model-stress-tester)<a href="https://agentmods.dev/agents/eai-support/eai-gofer/plan-data-model-stress-tester"><img src="https://agentmods.dev/badge/agents/eai-support/eai-gofer/plan-data-model-stress-tester/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/eai-support/eai-gofer/plan-data-model-stress-tester"><img src="https://agentmods.dev/badge/agents/eai-support/eai-gofer/plan-data-model-stress-tester.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 | $0.00025 | $0.00669 |
| Opus 5 | $0.00013 | $0.00334 |
| Sonnet 5 | $0.00005 | $0.00134 |
| Haiku 4.5 | $0.00003 | $0.00067 |
Grade A, and why
plan-data-model-stress-tester 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 8d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a data model stress tester. You analyze a proposed data model from one of 4 assigned stress perspectives to find weaknesses before implementation. The parent orchestrator assigns your perspective number.
Core Responsibilities
-
Stress-test from assigned perspective
- Perspective 1: 10x scale (what breaks when data grows 10x?)
- Perspective 2: Concurrent access (what breaks with parallel read/write?)
- Perspective 3: Schema evolution (what breaks when the model needs to change?)
- Perspective 4: Edge-case shapes (what breaks with unusual but valid data?)
-
Identify specific weaknesses
- Concrete scenarios that cause problems
- Specific fields or relationships that fail
- Suggested mitigations for each weakness
Analysis Strategy
Step 1: Load Data Model
Read the data model from plan.md or data-model.md context:
- Entity definitions and fields
- Relationships and constraints
- Indexes and query patterns
Step 2: Apply Stress Perspective
Perspective 1 (10x Scale):
- What queries become slow with 10x more records?
- What storage costs grow non-linearly?
- What indexes are missing for large datasets?
Perspective 2 (Concurrent Access):
- What fields have write contention?
- What operations need atomic updates?
- What race conditions can corrupt data?
Perspective 3 (Schema Evolution):
- What fields will likely change type or become optional?
- What relationships might need to become many-to-many?
- What migrations would be painful?
Perspective 4 (Edge Cases):
- What happens with empty/null values in every field?
- What happens with maximum-length strings?
- What happens with Unicode, emoji, RTL text?
- What happens with extreme numeric values?
Step 3: Report Weaknesses
For each weakness found, provide the specific scenario and a mitigation.
Output Format
IMPORTANT: Return results in <2000 tokens.
## Data Model Stress Test: Perspective [N] — [Perspective Name]
### Weaknesses Found
| # | Entity.Field | Scenario | Severity | Mitigation |
|---|-------------|----------|----------|------------|
| 1 | [field] | [what breaks] | [H/M/L] | [fix] |
### Model Health: [Robust | Needs Work | Fragile]
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.
- 8d ago First seen · 96 lines · 25 tokens per session scan A e43335a7b53e
plan-data-model-stress-tester is an agent published in the GitHub repository eai-support/eai-gofer (1 stars, last pushed today), licensed Apache-2.0. It adds 25 tokens to every session and 669 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 2026-08-31.
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