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 weights-biasesgit 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/weights-biases)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/weights-biases"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/weights-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/g1joshi/agent-skills/weights-biases"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/weights-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 | $0.00018 | $0.00250 |
| Opus 5 | $0.00009 | $0.00125 |
| Sonnet 5 | $0.00004 | $0.00050 |
| Haiku 4.5 | $0.00002 | $0.00025 |
Grade A, and why
weights-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 10d 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
Weights & Biases (W&B)
W&B is the "Github for ML models". It tracks every run, hyperparameter, and artifact. 2025 brings W&B Inference and Weave.
When to Use
- Visualization: Live loss curves during training.
- Collaboration: Sharing reports ("The loss spiked here") with teammates.
- Model Registry: Managing lifecycle.
Core Concepts
W&B Run
A single experiment.
Sweeps
Hyperparameter optimization engine.
Weave
A toolkit for logging and debugging interactive GenAI applications (like Tracing).
Best Practices (2025)
Do:
- Use Weave: For LLM apps. It replaces simple text logging.
- Log Model Artifacts: Save
best_model.ptto W&B, not local disk.
Don't:
- Don't leak secrets: W&B logs configs. Ensure API keys aren't in your
configobject.
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.
- 10d ago First seen · 44 lines · 18 tokens per session scan A 2488639280e5
weights-biases is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 7mo ago), licensed MIT. It adds 18 tokens to every session and 250 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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