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 rules/jimmypocock/cursor-rules/aws-sagemakergit clone --depth 1 https://github.com/jimmypocock/cursor-rulesWhat 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 | $0.00005 | $0.00993 |
| Opus 5 | $0.00003 | $0.00496 |
| Sonnet 5 | $0.00001 | $0.00199 |
| Haiku 4.5 | $0.00001 | $0.00099 |
Grade A, and why
aws-sagemaker 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 yesterday.
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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Description: AWS SageMaker Development Standards Globs: /sagemaker//, /ml//, /models//, /notebooks//
AWS SageMaker Development Standards
@base.mdc @typescript.mdc
SageMaker Project Structure
- Organize projects with clear separation of concerns
- Implement proper model training/inference code separation
- Create reproducible experiment environments
- Design modular processing pipelines
- Implement proper data preprocessing components
- Create appropriate model evaluation modules
- Design proper feature engineering pipelines
- Implement proper deployment configurations
Model Development
- Implement standardized model interfaces
- Create proper model parameter configurations
- Design appropriate hyperparameter optimization
- Implement proper validation strategies
- Create appropriate model evaluation metrics
- Design proper model versioning
- Implement appropriate model documentation
- Create proper reproducibility controls
Data Management
- Implement proper data versioning
- Create appropriate data preprocessing pipelines
- Design efficient data loading mechanisms
- Implement proper feature extraction
- Create appropriate data validation checks
- Design proper data labeling processes
- Implement efficient data augmentation
- Create appropriate data splitting strategies
Training Jobs
- Configure appropriate training job parameters
- Implement proper distributed training
- Create appropriate checkpoint strategies
- Design efficient hyperparameter tuning jobs
- Implement proper early stopping
- Create appropriate resource configurations
- Design proper training metrics collection
- Implement appropriate experiment tracking
Model Deployment
- Implement proper model serving configurations
- Create appropriate endpoint configurations
- Design efficient auto-scaling policies
- Implement proper model monitoring
- Create appropriate deployment strategies
- Design proper A/B testing mechanisms
- Implement efficient batch transform jobs
- Create appropriate multi-model endpoints when applicable
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.
- yesterday First seen · 163 lines · 5 tokens per session scan A 06ac9f811d2e
aws-sagemaker is a cursor rule published in the GitHub repository jimmypocock/cursor-rules (8 stars, last pushed 1y ago), licensed MIT. It adds 5 tokens to every session and 993 once invoked, about $0.0000 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.
Other cursor rules, from other repositories
90-devops-deployment
Docker, CI/CD, AWS, Vercel, and VPS deployment rules.
00-global-architect
Global default behavior for the entire repository.
55-data-model-versioning
Dataset versioning, model checkpoint management, and training reproducibility rules.
85-error-observability
Error handling, logging, and observability rules.
30-database-postgres
PostgreSQL and persistence rules.
35-api-contracts
API versioning, contracts, and schema evolution rules.