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 lightgbmgit 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/lightgbm)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/lightgbm"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/lightgbm/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/lightgbm"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/lightgbm.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 | $0.00016 | $0.00235 |
| Opus 5 | $0.00008 | $0.00118 |
| Sonnet 5 | $0.00003 | $0.00047 |
| Haiku 4.5 | $0.00002 | $0.00023 |
Grade A, and why
lightgbm 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
LightGBM
LightGBM is Microsoft's gradient boosting library. It is often faster and uses less memory than XGBoost due to leaf-wise tree growth.
When to Use
- Huge Datasets: Optimized for efficiency.
- Ranking:
LGBMRankeris excellent for search/recommendation systems.
Core Concepts
Leaf-wise Growth
Grows the tree by splitting the leaf with max loss delta (creates deeper, unbalanced trees) vs Level-wise (balanced).
Histogram-based
Buckets continuous values into discrete bins for speed.
Best Practices (2025)
Do:
- Tune
num_leaves: The most important parameter for controlling complexity. - Use Categorical Features: Pass indexes of categorical columns directly.
Don't:
- Don't overfit: Leaf-wise growth overfits easily on small data. Limit
max_depth.
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 · 39 lines · 16 tokens per session scan A 0f6ba7d56675
lightgbm is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 7mo ago), licensed MIT. It adds 16 tokens to every session and 235 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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