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 G1Joshi/Agent-Skills --skill jaxgit clone --depth 1 https://github.com/G1Joshi/Agent-SkillsWrote 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/g1joshi/agent-skills/jax)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/jax"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/jax/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/g1joshi/agent-skills/jax"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/jax.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 | $0.00014 | $0.00312 |
| Opus 5 | $0.00007 | $0.00156 |
| Sonnet 5 | $0.00003 | $0.00062 |
| Haiku 4.5 | $0.00001 | $0.00031 |
Grade A, and why
jax 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 9d 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
JAX
JAX is "NumPy on steroids". It combines Autograd (automatic differentiation) with XLA (compilation). 2025 sees Flax NNX (PyTorch-style OOP) becoming standard.
When to Use
- TPU Training: JAX runs natively on Google TPUs.
- Research: If you need to compute 10th order derivatives or strange math.
- Massive Scale: DeepMind and OpenAI use JAX for training frontier models.
Core Concepts
Functional Transformations
grad(), jit(), vmap(), pmap().
Flax (NNX)
Neural network library. NNX introduces mutable state (OOP) to make JAX feel like PyTorch.
Statelessness
(Legacy Flax) parameters are stored separately from the model.
Best Practices (2025)
Do:
- Use
jit: Always compile your functions. - Use Flax NNX: Avoid the complexity of legacy immutable Flax/Haiku.
- Use
shard_map: For distributed training across devices.
Don't:
- Don't use side effects:
print()inside ajitfunction only runs once (during tracing).
References
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.
- 9d ago First seen · 45 lines · 14 tokens per session scan A 410a24148029
jax is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 7mo ago), licensed MIT. It adds 14 tokens to every session and 312 once invoked, about $0.0001 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 2026-08-30.
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9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
9router-stt
Speech-to-text via 9Router /v1/audio/transcriptions using OpenAI Whisper / Groq / Gemini / Deepgram / AssemblyAI / NVIDIA / HuggingFace models. Use when the user wants to transcribe audio, convert speech to text, or get subtitles from audio files.
yolo-training
This skill should be used when user asks to "improve my mAP", "why is my model overfitting", "my training is diverging", "read my results.csv", "interpret my training curves", "my AP50 is good but AP50-95 is bad", "my recall is low", "how do I pick learning rate", "which augmentations should I use", "should I use a…
fixing-prompt
Prompt: Prompt Refinement and Optimization.