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/asachs01/propublica-mcp/sparc_rulesgit clone --depth 1 https://github.com/asachs01/propublica-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/asachs01/propublica-mcp/sparc_rules)<a href="https://agentmods.dev/rules/asachs01/propublica-mcp/sparc_rules"><img src="https://agentmods.dev/badge/rules/asachs01/propublica-mcp/sparc_rules.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 5.1 | $0.01539 | $0.01539 |
| Opus 5 | $0.00770 | $0.00770 |
| Sonnet 5 | $0.00308 | $0.00308 |
| Haiku 4.5 | $0.00154 | $0.00154 |
Grade A, and why
sparc_rules 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 5d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- rules — 91% identical, 97 lines differ
How it starts
The opening of the file, as written. The whole thing — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SPARC Agentic Development Rules
Core Philosophy
-
Simplicity
- Prioritize clear, maintainable solutions; minimize unnecessary complexity.
-
Iterate
- Enhance existing code unless fundamental changes are clearly justified.
-
Focus
- Stick strictly to defined tasks; avoid unrelated scope changes.
-
Quality
- Deliver clean, well-tested, documented, and secure outcomes through structured workflows.
-
Collaboration
- Foster effective teamwork between human developers and autonomous agents.
Methodology & Workflow
- Structured Workflow
- Follow clear phases from specification through deployment.
- Flexibility
- Adapt processes to diverse project sizes and complexity levels.
- Intelligent Evolution
- Continuously improve codebase using advanced symbolic reasoning and adaptive complexity management.
- Conscious Integration
- Incorporate reflective awareness at each development stage.
Agentic Integration with Cline and Cursor
-
Cline Configuration (.clinerules)
- Embed concise, project-specific rules to guide autonomous behaviors, prompt designs, and contextual decisions.
-
Cursor Configuration (.cursorrules)
- Clearly define repository-specific standards for code style, consistency, testing practices, and symbolic reasoning integration points.
Memory Bank Integration
- Persistent Context
- Continuously retain relevant context across development stages to ensure coherent long-term planning and decision-making.
- Reference Prior Decisions
- Regularly review past decisions stored in memory to maintain consistency and reduce redundancy.
- Adaptive Learning
- Utilize historical data and previous solutions to adaptively refine new implementations.
General Guidelines for Programming Languages
-
Clarity and Readability
- Favor straightforward, self-explanatory code structures across all languages.
- Include descriptive comments to clarify complex logic.
-
Language-Specific Best Practices
- Adhere to established community and project-specific best practices for each language (Python, JavaScript, Java, etc.).
- Regularly review language documentation and style guides.
-
Consistency Across Codebases
- Maintain uniform coding conventions and naming schemes across all languages used within a project.
Project Context & Understanding
-
Documentation First
- Review essential documentation before implementation:
- Product Requirements Documents (PRDs)
- README.md
- docs/architecture.md
- docs/technical.md
- tasks/tasks.md
- Request clarification immediately if documentation is incomplete or ambiguous.
- Review essential documentation before implementation:
-
Architecture Adherence
- Follow established module boundaries and architectural designs.
- Validate architectural decisions using symbolic reasoning; propose justified alternatives when necessary.
-
Pattern & Tech Stack Awareness
- Utilize documented technologies and established patterns; introduce new elements only after clear justification.
Task Execution & Workflow
Task Definition & Steps
-
Specification
- Define clear objectives, detailed requirements, user scenarios, and UI/UX standards.
- Use advanced symbolic reasoning to analyze complex scenarios.
-
Pseudocode
- Clearly map out logical implementation pathways before coding.
-
Architecture
- Design modular, maintainable system components using appropriate technology stacks.
- Ensure integration points are clearly defined for autonomous decision-making.
-
Refinement
- Iteratively optimize code using autonomous feedback loops and stakeholder inputs.
-
Completion
- Conduct rigorous testing, finalize comprehensive documentation, and deploy structured monitoring strategies.
AI Collaboration & Prompting
-
Clear Instructions
- Provide explicit directives with defined outcomes, constraints, and contextual information.
-
Context Referencing
- Regularly reference previous stages and decisions stored in the memory bank.
-
Suggest vs. Apply
- Clearly indicate whether AI should propose ("Suggestion:") or directly implement changes ("Applying fix:").
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.
- 5d ago First seen · 243 lines · 1,539 tokens per session scan A d1e0887da098
sparc_rules is a cursor rule published in the GitHub repository asachs01/propublica-mcp (1 stars, last pushed 11mo ago), licensed MIT. It adds 1,539 tokens to every session, about $0.0077 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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