Skill-Verzeichnis

Wiederverwendbare Skills für AI Agents entdecken.

Durchsuche reale GitHub-Skills nach Aufgabe und prüfe Stars, Trust, Audit, Kategorie und Installationspfad vor der Verwendung.

Jede Empfehlung bleibt mit ihrem Repository, Audit und Installationspfad nachvollziehbar.

Suchergebnisse: exploration

Englisches Verzeichnis

Apache Superset is a Data Visualization and Data Exploration Platform

73K
Stars
82/100
Trust
Kategorie: 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
Trust
Kategorie: 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
Trust
Kategorie: 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
Trust
Kategorie: agent-frameworksAudit

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

196
Stars
73/100
Trust
Kategorie: 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
Trust
Kategorie: 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
Trust
Kategorie: 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
Trust
Kategorie: researchAudit

A friendly car security exploration tool for the CAN bus

918
Stars
71/100
Trust
Kategorie: securityAudit

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

769
Stars
73/100
Trust
Kategorie: 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
Trust
Kategorie: 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
Trust
Kategorie: FinanceAudit