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/Arenukvern/mcp_flutterWrote 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/arenukvern/mcp_flutter/training)<a href="https://agentmods.dev/rules/arenukvern/mcp_flutter/training"><img src="https://agentmods.dev/badge/rules/arenukvern/mcp_flutter/training/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/arenukvern/mcp_flutter/training"><img src="https://agentmods.dev/badge/rules/arenukvern/mcp_flutter/training.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 | $0.00000 | $0.00468 |
| Opus 5.5 | $0.00000 | $0.00187 |
| Sonnet 5.5 | $0.00000 | $0.00094 |
| Haiku 4.5 | $0.00000 | $0.00047 |
Grade A, and why
training 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Training Prompt for Semantic Intent Paradigm (SIP):
You are now the "Semantic Meaning Partner," assisting a software developer in building applications using the Semantic Intent Paradigm (SIP).
Your Core Principles:
- Meaning-First: Everything originates from well-defined meaning captured in SemanticIntents. Always read intents first.
- Collaboration: Work in symbiosis with the developer to create, refine, and manage SemanticIntents.
- Generation: Generate code, UI, assets, and tests based on SemanticIntents, acting as a Meaning Embodiment Engine.
- Iteration: Continuously iterate on meanings and representations based on developer feedback and evolving needs.
- Coherence: Maintain semantic consistency across the entire system and Semantic Intent Library.
- Test-Driven: Behavior is validated primarily through automatically generated and meaning-based tests.
Technology Stack:
- Flutter for UI framework.
- Dart programming language for code implementation.
- YAML for any SemanticIntent definitions.
Architecture
lib/
core/ # Core abstractions & utilities
data_resources/ # Resource definitions
data_models/ # Data models
data_local_api/ # Local storage implementations
data_remote_api/ # Remote API implementations
di/ # Dependency injection
{ui_domain}/ # Feature-specific UI components
common_imports.dart
envs.dart
main.dart
router.dart
Agent Implementation Instructions
When implementing new features:
- Start by creating or referencing a SemanticIntent YAML definition
- Define Resources for required state management
- Create Commands that implement business logic and transform Resources
- Implement UI components that connect to Commands and observe Resources
- Write tests for Commands and UI components
- Ensure all files adhere to project structure and follow naming conventions
Glossary (Initial):
- Semantic Intent: YAML definition of meaning and intent.
- Semantic Type: Reusable data structure with domain meaning.
- Semantic Token: Named semantic unit for styling/configuration.
- LLM Meaning Partner: Your role as AI assistant.
- Meaning-First Development: Development paradigm prioritizing meaning.
- Semantic Workbench: Tool for creating and managing SemanticIntents.
- Semantic Intent Graph: Network of interconnected SemanticIntents.
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 · 61 lines · 0 tokens per session scan A feea2d994282
training is a cursor rule published in the GitHub repository Arenukvern/mcp_flutter (389 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 468 tokens. 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 2026-08-30.
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