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/run-llama/flow-makerWrote 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/run-llama/flow-maker/architecture)<a href="https://agentmods.dev/rules/run-llama/flow-maker/architecture"><img src="https://agentmods.dev/badge/rules/run-llama/flow-maker/architecture/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/run-llama/flow-maker/architecture"><img src="https://agentmods.dev/badge/rules/run-llama/flow-maker/architecture.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 | $0.00704 | $0.00704 |
| Opus 5.5 | $0.00282 | $0.00282 |
| Sonnet 5.5 | $0.00141 | $0.00141 |
| Haiku 4.5 | $0.00070 | $0.00070 |
Grade A, and why
architecture 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 — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Llama Agent Creator Architecture
This document outlines the core architecture of the Llama Agent Creator application, focusing on how the visual workflow graph is translated into executable logic.
Core Components and Data Flow
The application has a clear separation between the visual graph representation, an intermediate JSON format, and two distinct execution engines.
Visual Graph (React Flow) --> workflow-compiler.ts --> WorkflowJson (Intermediate State) --> [RunView.tsx OR typescript-compiler.ts]
-
Visual Graph Editor (
src/components/AgentFlow.tsx):- This is where the user builds the agent workflow using a drag-and-drop interface powered by
@xyflow/react. - The state of the graph (nodes and edges) is saved to
localStorage.
- This is where the user builds the agent workflow using a drag-and-drop interface powered by
-
Workflow Compiler (
src/lib/workflow-compiler.ts):- This is a critical module that acts as a bridge between the UI and the execution logic.
- It takes the raw nodes and edges from the React Flow state.
- It processes this graph structure and compiles it into a standardized intermediate representation,
WorkflowJson. This JSON object describes the nodes, their connections (via an event-based system), and their configurations.
-
Execution Engines: The
WorkflowJsoncan be consumed by two different parts of the application to execute the workflow:- Interactive Runner (
src/components/RunView.tsx):- Provides an in-browser, step-by-step execution of the workflow.
- It loads the graph from
localStorage, usesworkflow-compiler.tsto get theWorkflowJson, and then walks through the nodes. - It implements the logic for each node type (e.g., 'promptLLM', 'userInput', 'decision') directly, making API calls to the backend (
/api/...) as needed. - It visually highlights the currently active node on the graph and provides a chat interface for user interaction.
- This provides a way to debug and test the workflow interactively.
- Interactive Runner (
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 · 44 lines · 704 tokens per session scan A b65e3022522e
architecture is a cursor rule published in the GitHub repository run-llama/flow-maker (211 stars, last pushed 10mo ago), licensed MIT. It adds 704 tokens to every session, about $0.0028 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 2026-08-30.
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).