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.

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Annuaire en anglais

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

4.7K
Stars
76/100
Confiance
Catégorie: ml-automationAudit

Plumb a PDF for detailed information about each char, rectangle, line, et cetera — and easily extract text and tables.

10K
Stars
83/100
Confiance
Catégorie: document-processingAudit

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 simple cross platform ACME client (for use with Let's Encrypt et al.)

549
Stars
67/100
Confiance
Catégorie: automationAudit

A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021 (Bianchi et al.).

1.3K
Stars
73/100
Confiance
Catégorie: rag-knowledgeAudit

L'application libre et open source ultime pour gérer toute ta vie scolaire sans compromis.

285
Stars
70/100
Confiance
Catégorie: educationAudit

Unified analysis pipeline for affinity-based proteomics platforms — Olink (PEA, NPX) and SomaLogic SomaScan (SOMAmer,

1.1K
Stars
67/100
Confiance
Catégorie: automationAudit

Catalogue francophone de skills portables pour Claude Code, Codex, Cursor, le Figma Agent et Figma Make, fondé sur le standard Agent Skills.

10
Stars
66/100
Confiance
Catégorie: utilityAudit

Protein-Ligand Interaction Profiler - Analyze and visualize non-covalent protein-ligand interactions in PDB files according to 📝 Schake, Bolz, et al. (2025), https://doi.org/10.1093/nar/gkaf361

689
Stars
69/100
Confiance
Catégorie: geo-scienceAudit

🌱 Skill éco-responsable pour Claude Code : sobriété numérique, audit d'éco-conception (RGESN, GR491, Green Software Foundation) et pratiques de sobriété IA

41
Stars
66/100
Confiance
Catégorie: utilityAudit

Pytorch implementation of Evolutionary Policy Optimization, from Wang et al. of the Robotics Institute at Carnegie Mellon University

110
Stars
68/100
Confiance
Catégorie: robotics-iotAudit