3.7K skills · sorted by relevance
Design n8n automations for service integrations, scheduled tasks, databases, and AI agents. Choose from six workflow patterns, plan data flow and error handling, check connections before activation, and improve workflows that process large numbers of items.
Use n8n-mcp tools to find workflow steps, check settings, and create or update automations. Provides guidance on tool arguments, templates, folders, credentials, and security audits so agents can choose appropriate operations.
Author and review GitHub Actions workflow YAML safely so syntactically-valid YAML can't ship a workflow that GitHub Actions refuses to run. USE FOR: editing, adding, or reviewing any file under .github/workflows/, writing run-name/name/if/env/run values that contain ${{ }} expressions, diagnosing a run that fails with 'This run likely failed because of a workflow file issue' and no jobs starting, deciding when a workflow scalar must be quoted, validating workflows with actionlint. DO NOT USE FOR: authoring application YAML unrelated to GitHub Actions, Azure Pipelines, GitLab CI, or non-workflow YAML. SCOPE: this skill covers *syntactic/structural* correctness of workflow YAML (quoting, parsing, actionlint); for *semantic and functional* workflow design (what a workflow should do, agentic-workflow behavior), see .github/agents/agentic-workflows.agent.md — the two are complementary. INVOKES: actionlint (downloaded pinned binary) plus git/grep for inspection.
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Author and review GitHub Actions workflow YAML safely so syntactically-valid YAML can't ship a workflow that GitHub Actions refuses to run. USE FOR: editing, adding, or reviewing any file under .github/workflows/, writing run-name/name/if/env/run values that contain ${{ }} expressions, diagnosing a run that fails with 'This run likely failed because of a workflow file issue' and no jobs starting, deciding when a workflow scalar must be quoted, validating workflows with actionlint. DO NOT USE FOR: authoring application YAML unrelated to GitHub Actions, Azure Pipelines, GitLab CI, or non-workflow YAML. SCOPE: this skill covers *syntactic/structural* correctness of workflow YAML (quoting, parsing, actionlint); for *semantic and functional* workflow design (what a workflow should do, agentic-workflow behavior), see .github/agents/agentic-workflows.agent.md — the two are complementary. INVOKES: actionlint (downloaded pinned binary) plus git/grep for inspection.
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Use when working with Argo Workflows — argo Workflows management for Kubernetes-native workflow orchestration. Covers workflow templates, cron workflow scheduling, artifact management, workflow execution, parameter handling, and resource monitoring. Use when checking workflow status, investigating step failures, managing templates, or auditing cron workflows.
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Creates, debugs, and optimizes GitHub Actions workflow YAML files. Recommends current action versions with SHA pinning from a daily-updated index of 120+ actions. Configures matrix builds, dependency caching, reusable workflows, OIDC authentication, secrets management, and runner selection. Reviews existing workflows for outdated actions, security issues, and performance problems. Use when the user asks about GitHub Actions, workflows, CI/CD, .github/workflows, any action by name (actions/checkout, setup-node, setup-python, etc.), or workflow optimization.
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Investigate performance bottlenecks in REA analysis workflows and improve caching, queries, or shared processing. Compare completed tasks before and after changes while preserving correct results, examined coverage, and access to supporting evidence.
Audit repository settings, connected tools, and the agent's runtime setup to show what is usable now. Identify missing workflows and integrations, then recommend prioritized ECC skills, automation hooks, agents, or tool connections.
Review prediction-market and trading-agent workflows before they handle account access, portfolio data, or trade planning. Produces structured findings on venue rules, data quality, security, privacy, and execution risks, with required safeguards and a safe next step.
Turn repeated local Hermes workflows into reusable ECC skills and release materials. Remove private data, credentials, account names, and local paths, then document public inputs, expected outputs, and remaining risks before sharing.
Guides electronic medical record development, covering visit workflows, prescriptions, and clinical data entry. Provides patterns for medication interaction alerts, abnormal result displays, audit trails, and accessible interfaces that keep patient information visible during care.
Coordinate parallel AI agent sessions with dmux, a tool that manages terminal panes. Split development, research, testing, or review into separate tasks, isolate overlapping file changes, and review results before merging them.
Run ComfyUI workflows to generate images, video, audio, and 3D content. Prepare models and processing nodes, adjust generation settings, create batches of variations, monitor progress, and download results from a local server or Comfy Cloud.
Find LLM workflows in a codebase and label them by the job they perform, so Caveman Cloud can separate spending by workflow. Present a labeling proposal for approval, apply focused code changes, and check an affected execution path.
Use the Venice.ai API for video generation and transcription workflows. This catalogue entry helps agents discover the skill; running the complete workflow requires installing the upstream bundle with its scripts, assets, and references.
Submit groups of AI requests as batch jobs with the OmniRoute command line. Manage input files, monitor or cancel jobs, save results and errors, and incorporate batch processing into automated build and deployment workflows.
Create workflows that pause for approval and resume later, or scripts that distribute work across multiple agents. Choose a saved template for ongoing processes or a JavaScript script to collect structured results from parallel tasks.
Identify and test critical workflows where APIs, services, databases, and interface components work together. Review normal behavior, important failures, and CI checks to find gaps that isolated unit tests can miss and keep those interactions continuously verified.
Manage ComfyUI image generation from the command line. Validate workflows, which define generation steps, submit them for execution, monitor queued jobs, inspect available models, and download images from completed runs.
Manage n8n automation workflows from the command line. Create, update, and activate workflows, inspect or retry executions, and manage credentials, variables, and tags through n8n's public API with structured JSON output.
Build and troubleshoot TorchDrug 0.2.1 workflows for molecular property prediction, molecule generation, protein modeling, and knowledge graph reasoning. Connect datasets, models, training tasks, and evaluation while checking version compatibility and common implementation errors.
Run molecular property predictions, explore molecular shapes, and estimate how compounds bind to proteins through a cloud Python API. Process compound batches and connect modeling steps without maintaining local high-performance computing infrastructure.
Inspect microscopy data stored on OMERO.server, export selected image metadata, and plan imports or exports. The skill uses explicit data scopes and limits, with dry-run helpers and reviewed targets for workflows that change server data.
Build and operate genomics workloads on DNAnexus using its command-line tools and Python SDK. The skill covers data transfers, app configuration checks, workflow imports, execution monitoring, and explicit cost controls for reproducible analysis workflows.