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/Empreiteiro/langflow-factoryWrote 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/empreiteiro/langflow-factory/langflow-input-types)<a href="https://agentmods.dev/rules/empreiteiro/langflow-factory/langflow-input-types"><img src="https://agentmods.dev/badge/rules/empreiteiro/langflow-factory/langflow-input-types/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/empreiteiro/langflow-factory/langflow-input-types"><img src="https://agentmods.dev/badge/rules/empreiteiro/langflow-factory/langflow-input-types.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 | $0.05974 | $0.05974 |
| Opus 5.5 | $0.02390 | $0.02390 |
| Sonnet 5.5 | $0.01195 | $0.01195 |
| Haiku 4.5 | $0.00597 | $0.00597 |
Grade A, and why
langflow-input-types 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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 · 1,040 lines · 5,974 tokens per session scan A 38ea801e9316
langflow-input-types is a cursor rule published in the GitHub repository Empreiteiro/langflow-factory (66 stars, last pushed 1mo ago), with no licence file. It adds 5,974 tokens to every session, about $0.0239 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
get-more-from-ai
Level up how you actually use AI — from basic one-shot questions to the techniques that get dramatically better results — matched to what you already do. Use when asked how do I get better at using AI, how do power users use AI, I feel like I'm using AI at 10%, or teach me to use AI better. Produces an honest read of…
spot-ai-mistakes
Learn to recognize where and how AI tends to go wrong — the specific failure patterns — so you catch its mistakes on sight instead of getting burned by confident errors. Use when asked how do I know when AI is wrong, what are AI's common mistakes, how do I catch AI errors, or where does AI mess up. Produces the…
pytorch-deep-learning
A set of Python guidelines for building and training deep-learning models with PyTorch, a machine-learning framework. It covers model structures, training loops, loss calculation, optimization, and choosing available hardware.
scikit-learn-machine-learning
A set of Python guidelines for building machine-learning models with scikit-learn, a library for training models from data. It covers preparing data, linking processing steps into pipelines, training models, and checking results with cross-validation.
tensorflow-handbook
A set of guidelines for building and training neural networks with TensorFlow and Keras, Python tools for machine learning. It covers sequential models, model training, distributed training, and using multiple GPUs.
dask-notebook
Create a Jupyter notebook that sets up a Dask distributed LocalCluster and walks through an ETL workflow.