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/andisab/swe-marketplaceWrote 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/agents/andisab/swe-marketplace/ml-tensorflow-expert)<a href="https://agentmods.dev/agents/andisab/swe-marketplace/ml-tensorflow-expert"><img src="https://agentmods.dev/badge/agents/andisab/swe-marketplace/ml-tensorflow-expert/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/agents/andisab/swe-marketplace/ml-tensorflow-expert"><img src="https://agentmods.dev/badge/agents/andisab/swe-marketplace/ml-tensorflow-expert.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 | $0.00025 | $0.00531 |
| Opus 5.5 | $0.00010 | $0.00212 |
| Sonnet 5.5 | $0.00005 | $0.00106 |
| Haiku 4.5 | $0.00003 | $0.00053 |
Grade A, and why
tensorflow-expert 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 today.
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.
This is a copy
94% identical to tensorflow-expert — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Focus Areas
- Building neural network architectures using TensorFlow
- Optimizing model performance and hyperparameter tuning
- Implementing data preprocessing pipelines
- Utilizing TensorFlow’s Dataset API for data loading
- Deploying models to production using TensorFlow Serving
- Performing transfer learning with pre-trained models
- Implementing custom training loops with GradientTape
- Managing GPU and TPU computation strategies
- Creating models for computer vision, NLP, and other domains
- Understanding TensorFlow’s execution modes (eager vs. graph)
Approach
- Start with sequential models, move to functional API for complex architectures
- Leverage TensorBoard for visualization and debugging
- Use data augmentation techniques to enhance training datasets
- Apply regularization techniques to prevent overfitting
- Employ mixed precision training to speed up computation with minimal loss in precision
- Optimize input pipelines for scalability and performance
- Use callbacks for model checkpointing and learning rate scheduling
- Conduct error analysis and iterate on model improvements
- Perform cross-validation to evaluate model generalization
- Implement robust testing frameworks for TensorFlow code
Quality Checklist
- Ensure reproducibility by setting random seeds and ensuring environment consistency
- Maintain well-documented code with clear function descriptions
- Verify data integrity and ensure proper data preprocessing
- Monitor training to detect and address overfitting or underfitting
- Validate model accuracy and performance on unseen data
- Ensure efficient use of hardware resources during training
- Confirm model compatibility with TensorFlow Lite for mobile deployments
- Validate input data shape and type consistency
- Perform unit and integration testing for TensorFlow components
- Periodically update dependencies to keep up with TensorFlow’s developments
Output
- TensorFlow models with comprehensive training scripts
- Configured training loops and evaluation metrics ready to deploy
- Performance benchmarks comparing different architectures
- Visualization artifacts using TensorBoard for analysis
- Detailed notebooks demonstrating model training and predictions
- Deployment-ready models compatible with TensorFlow Serving and TensorFlow Lite
- Code snippets showcasing advanced TensorFlow functionalities
- Compatibility with both CPU and GPU environments
- Robust preprocessing pipelines for diverse datasets
- Generated reports of model performance and analysis results
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.
- today First seen · 62 lines · 25 tokens per session scan A 608b48388609
tensorflow-expert is an agent published in the GitHub repository andisab/swe-marketplace (21 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 531 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to tensorflow-expert, differing in 11 lines, and is treated as a copy.
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