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Found 11,879 Skills
Webexpenses integration. Manage data, records, and automate workflows. Use when the user wants to interact with Webexpenses data.
Use when an academic research repository task could involve research design, sources, conversion, bibliography, SOTA, reviews, ethics, experiments, papers, reproduction, MCP tools, or project maintenance and the correct workflow is not obvious.
SEO & content marketing slash command suite for Claude with keyword research, audits, SERP analysis, and workflow automation
Use when creating a new Elastic integration package, scaffolding data streams, answering package layout or structure questions, or running the end-to-end integration build workflow. Covers package topology, scaffold commands, post-scaffold edits, and full orchestration of CEL/pipeline/test subagents.
Use when the user reaches for a Code node, mentions writing JavaScript or Python in n8n, or any custom logic comes up in workflow design. Triggers on "Code node", "Code", "JavaScript", "Python", "custom logic", "transform data", "$input", "$json transformation", "loop in code", "write a function", or any time the obvious answer seems to be "just put it in code."
Selects, deploys, and customizes AI models on Amazon SageMaker. Fine-tuning (SFT, DPO, RLVR, RLAIF), model selection, dataset preparation, evaluation, deployment to SageMaker endpoints or Bedrock, and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, selecting base models from SageMaker Hub, finding a model to deploy without fine-tuning, transforming datasets for training, checking data readiness, evaluating model quality, deploying to endpoints, setting up IAM roles and S3 buckets for training jobs, or managing a SageMaker Managed MLflow app. Also use to check endpoint health, diagnose failures, debug latency or errors, or view container logs and CloudWatch metrics. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3 usage. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.
Use when building AI agent storage workflows on Tigris — forks for isolated dataset copies, workspaces for per-agent buckets with TTL, checkpoints for snapshot/restore, and coordination for event-driven pipelines via bucket webhooks. Triggers on "@tigrisdata/agent-kit", "agent storage", "agent workspace", "agent fork", "isolated agent environment", "checkpoint and restore", "bucket webhook", "multi-agent pipeline"
Use this skill ONLY when the user explicitly asks for help designing or architecting a process ("help me design", "what's the best structure", "how do I organize this flow?"). For direct execution requests ("create a pipe for X", "build a reimbursement process", or any message with a detailed spec), skip this skill and execute directly using the domain skills (pipes-and-cards, automations, etc.).
Use when building, editing, validating, testing, or debugging an n8n workflow through the n8n-mcp MCP server — designing a flow, configuring a node, writing an expression or Code node, wiring credentials, or fixing one that misbehaves. The entry-point skill for the n8n-mcp-skills pack: it routes you to the right specialist skill, gives working knowledge of every n8n-mcp tool from turn one, and states the rules that keep workflows from breaking in production. Always consult it first on any n8n, workflow, node, or automation task — even a quick one-off, and even when the user names no skill — because n8n's surface drifts between versions and the specialist skills prevent silent failures.
Reference skill for building production-ready crw integrations. Covers verb selection, call surfaces (CLI/MCP/REST), post-filtering strategies, context-window hygiene, Hybrid RAG patterns, common pitfalls, and crw-specific operational considerations (search backend limits, renderer pool, proxy rotation). Load this when writing application code that embeds crw, designing a multi-step agent workflow, or debugging an integration that isn't behaving as expected.
Orchestrate Zoen product engineering from discovery to production using the curated Matt Pocock skill set, Extreme Programming, ontology-first architecture, and risk-controlled delivery. Use for any Zoen product, architecture, feature, bug, refactor, AI-agent, integration, release, or engineering-workflow decision, especially nouns-links-verbs-evidence, AgentRun/ToolCall, Action Gateway, approvals, audit and provenance, tenancy, client-owned export, or production readiness.
SAP Analytics Cloud (SAC) planning guidance for planning models, planning-enabled stories, data actions, multi actions, version management, data locking, calendar/input workflows, allocations, value driver trees, BPC live planning, and Seamless Planning with SAP Datasphere. Use this for planning design, planning APIs, data action debugging, planning performance reviews, and authenticated SAC planning story triage in Microsoft Edge via CDP; use sap-sac-scripting for non-planning SAC scripts and sap-datasphere for Datasphere modeling.