Lightgbm
LightGBM gradient boosting framework. Use for fast ML.
Install
$ agentstack add skill-g1joshi-agent-skills-lightgbm ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-g1joshi-agent-skills-lightgbm) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: G1Joshi
- Source: G1Joshi/Agent-Skills
- License: MIT
- Homepage: https://skills.sh/g1joshi/agent-skills
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.