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: audience-segmentation

Annuaire en anglais

A comprehensive set of 38 marketing skills and 5 commands for Claude Code covering SEO/GEO and influencer marketing with evaluation frameworks.

2.6K
Stars
86/100
Confiance
Catégorie: productivityAudit

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

13K
Stars
87/100
Confiance
Catégorie: ml-automationAudit

Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages

7.8K
Stars
77/100
Confiance
Catégorie: ml-automationAudit

RF-DETR is a real-time object detection and segmentation model architecture developed by Roboflow, SOTA on COCO, designed for fine-tuning. [ICLR 2026]

7.8K
Stars
86/100
Confiance
Catégorie: ml-automationAudit

AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.

1.8K
Stars
77/100
Confiance
Catégorie: developmentAudit

Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds

2.1K
Stars
85/100
Confiance
Catégorie: ml-automationAudit

Retentioneering: product analytics, data-driven CJM optimization, marketing analytics, web analytics, transaction analytics, graph visualization, process mining, and behavioral segmentation in Python. Predictive analytics over clickstream, AB tests, machine learning, and Markov Chain simulations.

907
Stars
69/100
Confiance
Catégorie: data-analysisAudit

Use when user wants to create a GitHub issue for the current repository. Must read and follow the repository's issue template format.

51K
Stars
76/100
Confiance
Catégorie: coding-agentsAudit

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

34K
Stars
80/100
Confiance
Catégorie: researchAudit

When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.

25K
Stars
77/100
Confiance
Catégorie: design-creativeAudit

Generate highly simplified personified IP mascot logos with Flat-first geometry, rounded heavy forms, two IP colors plus one background color by default, and extremely subtle neo-skeuomorphic shading. Use when creating an animal, creature, robot, ghost, plant, object, or other character as a minimal square logo or app-icon artwork, including when the agent should infer three distinct IP directions from product-repository context.

2.0K
Stars
73/100
Confiance
Catégorie: automationAudit

When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.

25K
Stars
77/100
Confiance
Catégorie: design-creativeAudit