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 graniet/kheish --skill weights-and-biasesgit clone --depth 1 https://github.com/graniet/kheishWrote 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/graniet/kheish/weights-and-biases)<a href="https://agentmods.dev/skills/graniet/kheish/weights-and-biases"><img src="https://agentmods.dev/badge/skills/graniet/kheish/weights-and-biases/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/graniet/kheish/weights-and-biases"><img src="https://agentmods.dev/badge/skills/graniet/kheish/weights-and-biases.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 | $0.00033 | $0.03423 |
| Opus 5 | $0.00016 | $0.01711 |
| Sonnet 5 | $0.00007 | $0.00685 |
| Haiku 4.5 | $0.00003 | $0.00342 |
Grade A, and why
weights-and-biases 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 11d 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.
This is a copy
84% identical to weights-and-biases — 56 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.
How it starts
The opening of the file, as written. The whole thing — 617 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
terminal=>bashweb_extract=>web_fetch, plusweb_searchwhen discovery is neededsearch_files=>grep_searchandglob_searchbrowser_*tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from ${KHEISH_SKILL_DIR}.
Weights & Biases: ML Experiment Tracking & MLOps
When to Use This Skill
Use Weights & Biases (W&B) when you need to:
- Track ML experiments with automatic metric logging
- Visualize training in real-time dashboards
- Compare runs across hyperparameters and configurations
- Optimize hyperparameters with automated sweeps
- Manage model registry with versioning and lineage
- Collaborate on ML projects with team workspaces
- Track artifacts (datasets, models, code) with lineage
Users: 200,000+ ML practitioners | GitHub Stars: 10.5k+ | Integrations: 100+
Installation
# Install W&B
pip install wandb
# Login (creates API key)
wandb login
# Or set API key programmatically
export WANDB_API_KEY=your_api_key_here
Quick Start
Basic Experiment Tracking
import wandb
# Initialize a run
run = wandb.init(
project="my-project",
config={
"learning_rate": 0.001,
"epochs": 10,
"batch_size": 32,
"architecture": "ResNet50"
}
)
# Training loop
for epoch in range(run.config.epochs):
# Your training code
train_loss = train_epoch()
val_loss = validate()
# Log metrics
wandb.log({
"epoch": epoch,
"train/loss": train_loss,
"val/loss": val_loss,
"train/accuracy": train_acc,
"val/accuracy": val_acc
})
# Finish the run
wandb.finish()
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 617 lines · 33 tokens per session scan A 35e768ff9ed5
weights-and-biases is a skill published in the GitHub repository graniet/kheish (227 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 33 tokens to every session and 3,423 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to weights-and-biases, differing in 56 lines, and is treated as a copy.
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