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/technical-decisions)<a href="https://agentmods.dev/rules/sfc-gh-cconner/support-rules-mcp/technical-decisions"><img src="https://agentmods.dev/badge/rules/sfc-gh-cconner/support-rules-mcp/technical-decisions/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/technical-decisions"><img src="https://agentmods.dev/badge/rules/sfc-gh-cconner/support-rules-mcp/technical-decisions.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 | $0.01896 | $0.01896 |
| Opus 5.5 | $0.00758 | $0.00758 |
| Sonnet 5.5 | $0.00379 | $0.00379 |
| Haiku 4.5 | $0.00190 | $0.00190 |
Grade A, and why
technical-decisions 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 — 317 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical Decisions & Architecture
PURPOSE: Architectural principles and technical stack decisions for MCP server and tooling development.
🛠️ Technology Stack
Core Technologies
- Language: Python 3.8+
- MCP Framework: Model Context Protocol Server
- Rule Format: Markdown (.mdc files)
- Code Quality: pre-commit hooks (black, isort, flake8)
- Testing: pytest with mocking
- CI/CD: GitHub Actions (when applicable)
Python Ecosystem
# Standard dependencies
python >= 3.8
mcp >= 0.1.0 # MCP server framework
pydantic >= 2.0 # Data validation
pyyaml >= 6.0 # Configuration parsing
🏗️ Architecture Principles
1. MCP Server Design
Principle: Rules are the source of truth for AI assistant behavior.
# Rule structure
rules/
├── core/ # Fundamental patterns
│ ├── 01-*.mdc # Numbered for ordering
│ └── ...
├── connectors/ # Driver-specific
├── spcs/ # SPCS-specific
├── workflows/ # Process documentation
└── _meta/ # Complete workflows
Benefits:
- Modular rule organization
- Easy to add/update rules
- Clear separation of concerns
- Rules can include other rules
2. Rule Composition
Principle: Rules can reference and include other rules.
# In a meta rule
@include core/01-github-mcp
@include core/06-snowflake-cli
@include core/08-mono-repo-mcp
This rule combines GitHub MCP, Snowflake CLI, and mono repo
navigation patterns for complete investigations.
Benefits:
- Reusability
- Consistency
- Easier maintenance
- Logical grouping
3. Separation of Concerns
Principle: Each rule has a single, clear purpose.
# Good - Focused rules
01-github-mcp.mdc → GitHub MCP integration patterns
06-snowflake-cli.mdc → Snow CLI usage
08-mono-repo-mcp.mdc → Mono repo navigation with GitHub MCP
# Bad - Mixed concerns
snowflake-investigation.mdc → Everything about investigating
Benefits:
- Easier to find relevant rules
- Simpler to update individual rules
- Clearer documentation
- Better testability
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 · 317 lines · 1,896 tokens per session scan A e2a4f8692881
technical-decisions 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 1,896 tokens to every session, about $0.0076 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.
Other cursor rules, from other repositories
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
prefer-assertions-over-defensive-checks
Prefer assertions over defensive checks when data is guaranteed to be valid.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.
config-validation-and-normalization
Config validation and normalization responsibilities (defineConfig normalizes, the orm section schema validates and resolves paths, the loader reports per-section diagnostics).