Annuaire de skills

Découvrez des skills réutilisables pour les AI agents.

Recherchez de vrais skills GitHub par tâche et vérifiez Stars, confiance, audit, catégorie et chemin d’installation avant de les utiliser.

Chaque recommandation reste clairement reliée à son dépôt, son audit et son chemin d’installation.

Résultats de recherche: exploration

Annuaire en anglais

Apache Superset is a Data Visualization and Data Exploration Platform

73K
Stars
82/100
Confiance
Catégorie: data-analysisAudit

FinceptTerminal is a modern finance application offering advanced market analytics, investment research, and economic data tools, designed for interactive exploration and data-driven decision-making in a user-friendly environment.

27K
Stars
75/100
Confiance
Catégorie: financeAudit

Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀

8.5K
Stars
83/100
Confiance
Catégorie: ml-automationAudit

AIDE: AI-Driven Exploration in the Space of Code. The machine Learning engineering agent that automates AI R&D.

1.4K
Stars
84/100
Confiance
Catégorie: agent-frameworksAudit

Turn project work into reusable knowledge — an AI-agent skill for Claude Code & Codex

196
Stars
73/100
Confiance
Catégorie: utilityAudit

A Claude Code plugin that provides a universal radial-tree exploration engine with swappable presets for divergent ideation, adversarial critique, design-space exploration, and code audit.

161
Stars
76/100
Confiance
Catégorie: coding-agentsAudit

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

1.8K
Stars
83/100
Confiance
Catégorie: data-analysisAudit

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

34K
Stars
77/100
Confiance
Catégorie: researchAudit

A friendly car security exploration tool for the CAN bus

918
Stars
71/100
Confiance
Catégorie: securityAudit

Business intelligence, data exploration and visualization web application for Druid, formerly known as Swiv and Pivot

769
Stars
73/100
Confiance
Catégorie: data-analysisAudit

A general F# SQL database erasing type provider, supporting LINQ queries, schema exploration, individuals, CRUD operations and much more besides.

626
Stars
68/100
Confiance
Catégorie: data-analysisAudit

Look up A-share, Hong Kong, and US stock tickers and retrieve historical OHLCV price data for research.

2.8K
Stars
69/100
Confiance
Catégorie: FinanceAudit