Total 58,118 skills, AI & Machine Learning has 9659 skills
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Interactive installer for Everything Claude Code — guides users through selecting and installing skills and rules to user-level or project-level directories, verifies paths, and optionally optimizes installed files.
Generates image prompts for Seedream 5.0/4.0 (Jimeng AI), and can call the API to generate images and automatically download them to the output/ directory. Workflow: describe your idea → the agent outputs a prompt for review → user confirms → the agent runs generate.py. It covers text-to-image, image editing, multi-image fusion, character consistency, knowledge cards, posters, PPT backgrounds, e-commerce images, avatars, and group/storyboard generation. Activate this tool when the user mentions terms like seedream, jimeng, AI image generation, text-to-image, image-to-image, seedream prompt, prompt keyword, one-click image generation, knowledge card, poster design, e-commerce image, character consistency, or image generation.
Predict construction project costs using Machine Learning. Use Linear Regression, K-Nearest Neighbors, and Random Forest models on historical project data. Train, evaluate, and deploy cost prediction models.
Fiscaliste IA pour la fiscalité personnelle des particuliers français : optimisation et déclaration de l'impôt sur le revenu, IFI, revenus du capital, revenus fonciers, equity salarial, crypto-actifs et PER. Couvre le calcul de l'IR (barème, quotient familial, décote, PAS, CEHR, revenus exceptionnels), la déclaration 2042 et annexes, les revenus du capital (PFU vs barème, PEA, assurance-vie, dividendes, plus-values), les revenus fonciers (micro/réel, déficit, LMNP, SCI IR), l'equity startup (RSU, BSPCE, stock-options, PEE/PERCO), la fiscalité crypto (PAMC, 2086), l'IFI et les déductions (PER, pension alimentaire). Triggers: impôt sur le revenu, IR, 2042, quotient familial, décote, PAS, PFU, flat tax, PEA, assurance-vie, LMNP, revenus fonciers, déficit foncier, SCI IR, RSU, BSPCE, stock-options, PEE/PERCO, crypto, 2086, IFI, PER, plafond PER, niche fiscale, optimisation fiscale, simulation IR, TMI. Hors scope : succession/donation (notaire), IS/SASU/arbitrage dividende-salaire/SCI IS (comptable).
Routes the weakest VCN samples (output of `tao-analyze-gaps-visual-changenet`) into per-augmentation-module subsets — one parquet for k-NN mining, one for AnomalyGen (Cosmos SDG) — based on each module's label eligibility. Use as the immediate next step after DEFT gap analysis in a VCN AOI SDA iteration.
Mask Grounding DINO for grounded instance segmentation. Extends Grounding DINO with a mask-prediction head for open-set segmentation guided by text prompts. Use when training, evaluating, exporting, quantizing, or running inference for a TAO Mask-Grounding-DINO model. Trigger phrases include "train Mask Grounding DINO", "open-vocabulary segmentation", "text-prompted instance segmentation", "grounded mask DETR".
Verify claims in agent responses against sources using semantic similarity and web fact-checking.
Use when an agent needs to delegate a task to the OpenAI Codex CLI from another agent environment such as Claude Code, OpenClaw, or similar. Covers checking whether Codex CLI is installed, running one-off Codex prompts with `codex exec`, resuming sessions, collecting outputs, attaching images or files as input with `-i`/stdin, and handling Codex image generation including finding and reporting generated image file paths.
Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.
A deep concept anatomist that deconstructs any concept through 8 exploration dimensions (history, dialectics, phenomenology, linguistics, formalization, existentialism, aesthetics, meta-philosophy) and compresses insights into an epiphany. Use this when users ask to explain, dissect, or deeply understand a concept, term, or idea. Produces org-mode output.