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 skills add ihatesea69/kiro-kit --skill tensorflow-kerasgit clone --depth 1 https://github.com/ihatesea69/kiro-kitWrote 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/skills/ihatesea69/kiro-kit/tensorflow-keras)<a href="https://agentmods.dev/skills/ihatesea69/kiro-kit/tensorflow-keras"><img src="https://agentmods.dev/badge/skills/ihatesea69/kiro-kit/tensorflow-keras/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/skills/ihatesea69/kiro-kit/tensorflow-keras"><img src="https://agentmods.dev/badge/skills/ihatesea69/kiro-kit/tensorflow-keras.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00033 | $0.00295 |
| Opus 5 | $0.00016 | $0.00148 |
| Sonnet 5 | $0.00007 | $0.00059 |
| Haiku 4.5 | $0.00003 | $0.00030 |
Grade A, and why
tensorflow-keras 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.
What it actually says
TensorFlow / Keras
Activate this skill when building models with TensorFlow or Keras.
When to Use
- Building production-ready ML models
- Implementing efficient data pipelines with tf.data
- Deploying models with TensorFlow Serving
- Using pre-trained models from TF Hub
- Converting models to TFLite for edge deployment
Patterns
import tensorflow as tf
from tensorflow import keras
model = keras.Sequential([
keras.layers.Dense(128, activation="relu"),
keras.layers.Dropout(0.3),
keras.layers.Dense(num_classes, activation="softmax"),
])
model.compile(
optimizer=keras.optimizers.Adam(1e-3),
loss="sparse_categorical_crossentropy",
metrics=["accuracy"],
)
model.fit(
train_ds,
validation_data=val_ds,
epochs=50,
callbacks=[
keras.callbacks.EarlyStopping(patience=5),
keras.callbacks.ModelCheckpoint("best_model.keras"),
],
)
Rules
- Use tf.data for efficient input pipelines
- Prefer Keras API over raw TF ops
- Enable mixed precision for GPU training
- Use callbacks for checkpointing and early stopping
- Profile with TensorBoard before optimizing
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 · 55 lines · 33 tokens per session scan A 1e4e80cae09f
tensorflow-keras is a skill published in the GitHub repository ihatesea69/kiro-kit (18 stars, last pushed 20d ago), licensed MIT. It adds 33 tokens to every session and 295 once invoked, about $0.0002 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 2026-09-03.
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