Agent Auto Sci Ai Ml
机器学习与人工智能科研子 skill。用于地理学+体育学项目中的 classical ML、深度学习、时间序列、模型评估、数据泄露检查、基线设计、超参数搜索、SHAP/可解释 AI、公平性诊断、实验复现和论文方法写作。触发于 machine learning、AI、scikit-learn、PyTorch Lightning、transformers、SHAP、model evaluation、XAI、spatial ML、time series 等任务。
Install
$ agentstack add skill-lzy599775-agent-auto-sci-skills-agent-auto-sci-ai-ml ✓ 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-lzy599775-agent-auto-sci-skills-agent-auto-sci-ai-ml) 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
Agent Auto Sci AI ML
Use this subskill when the project needs modeling rather than only descriptive analysis.
Fast Workflow
- Define prediction or explanation target.
- Build a data dictionary and leakage checklist.
- Establish simple baselines before complex models.
- Choose model family: classical ML, deep learning, time series, spatial ML, or NLP/transformer.
- Split data correctly: spatial, temporal, group, or external validation as needed.
- Evaluate with task-appropriate metrics and uncertainty.
- Interpret with SHAP, permutation importance, partial dependence, or ablation.
- Translate model results into manuscript claims only after robustness checks.
Read references/ml_ai_workflows.md.
For full paper projects, this skill owns baseline design, model/method selection, leakage checks, train/test split logic, hyperparameter search, ablation, robustness, error-case analysis, XAI, and methods/results reporting boundaries. It must work with agent-auto-sci-methodology before turning feature importance into mechanism language.
For deeper K-Dense-style encapsulation:
references/k_dense_ml_ai_mapping.md: how scikit-learn, PyTorch Lightning, transformers, SHAP, time-series, and XAI skills are adapted.references/sport_geography_ml_playbook.md: sport geography modeling patterns, leakage checks, baselines, and manuscript reporting.
Related Helper Skills
Use installed helper skills when useful:
scikit-learnpytorch-lightningtransformersshapaeontimesfm-forecastingstatistical-analysisagent-auto-sci-data-vizagent-auto-sci-geospatial
Must Not Do
- Do not use ML if the research question only needs transparent spatial/statistical analysis.
- Do not report accuracy without baselines and uncertainty.
- Do not mix train/test spatial units when spatial leakage is possible.
- Do not use SHAP as causal evidence.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Lzy599775
- Source: Lzy599775/agent-auto-sci-skills
- License: MIT
- Homepage: https://lzy599775.github.io/agent-auto-sci-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.