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/sfc-gh-cconner/support-rules-mcpWrote 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/rules/sfc-gh-cconner/support-rules-mcp/ml-python)<a href="https://agentmods.dev/rules/sfc-gh-cconner/support-rules-mcp/ml-python"><img src="https://agentmods.dev/badge/rules/sfc-gh-cconner/support-rules-mcp/ml-python/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/rules/sfc-gh-cconner/support-rules-mcp/ml-python"><img src="https://agentmods.dev/badge/rules/sfc-gh-cconner/support-rules-mcp/ml-python.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 | $0.00669 | $0.00669 |
| Opus 5.5 | $0.00268 | $0.00268 |
| Sonnet 5.5 | $0.00134 | $0.00134 |
| Haiku 4.5 | $0.00067 | $0.00067 |
Grade A, and why
ml-python 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Snowflake ML Python Investigation Guide
PURPOSE: Snowflake ML Python library patterns for machine learning workflows and model deployment.
Access Method: GitHub MCP API tools
Repository: snowflakedb/snowflake-ml-python
🔍 Quick Investigation Patterns
ML Modeling
# Model registry
mcp_github_search_code(
query='ModelRegistry OR register_model repo:snowflakedb/snowflake-ml-python'
)
# Training functions
mcp_github_search_code(
query='fit OR train OR predict repo:snowflakedb/snowflake-ml-python'
)
# Feature engineering
mcp_github_search_code(
query='FeatureStore OR transform repo:snowflakedb/snowflake-ml-python'
)
ML Operations
# Model deployment
mcp_github_search_code(
query='deploy OR serve OR endpoint repo:snowflakedb/snowflake-ml-python'
)
# Model versioning
mcp_github_search_code(
query='version OR tag repo:snowflakedb/snowflake-ml-python'
)
# Monitoring
mcp_github_search_code(
query='metrics OR monitor OR drift repo:snowflakedb/snowflake-ml-python'
)
Integrations
# Scikit-learn
mcp_github_search_code(
query='sklearn OR scikit repo:snowflakedb/snowflake-ml-python'
)
# XGBoost
mcp_github_search_code(
query='xgboost OR XGB repo:snowflakedb/snowflake-ml-python'
)
# TensorFlow/PyTorch
mcp_github_search_code(
query='tensorflow OR pytorch OR torch repo:snowflakedb/snowflake-ml-python'
)
🎯 Key Components
Core Modules
# Model registry
mcp_github_search_code(
query='path:snowflake/ml/registry repo:snowflakedb/snowflake-ml-python'
)
# Modeling APIs
mcp_github_search_code(
query='path:snowflake/ml/modeling repo:snowflakedb/snowflake-ml-python'
)
# Feature store
mcp_github_search_code(
query='path:snowflake/ml/feature_store repo:snowflakedb/snowflake-ml-python'
)
🗺️ Directory Structure
snowflake/ml/
├── registry/ # Model registry
├── modeling/ # ML algorithms
├── feature_store/ # Feature management
├── ops/ # ML operations
└── utils/ # Utilities
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 · 109 lines · 669 tokens per session scan A 79ace05ec479
ml-python is a cursor rule published in the GitHub repository sfc-gh-cconner/support-rules-mcp (0 stars, last pushed 1y ago), licensed Apache-2.0. It adds 669 tokens to every session, about $0.0027 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 2026-08-31.
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