temporal
Automate the migration of Netflix Conductor workflows to Temporal Python, including server orchestration, worker management, and workflow troubleshooting.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
316 skills found
Automate the migration of Netflix Conductor workflows to Temporal Python, including server orchestration, worker management, and workflow troubleshooting.
Preprocessing and cleaning astronomical light curves using Lightkurve. Tools for outlier removal, flattening, trend detrending, and quality flag handling for time-series analysis.
Facilitate structured product discovery with Opportunity Solution Trees, assumption mapping, and hypothesis-driven experimentation to de-risk product development.
Base ecosystem skill for Refly. Creates, discovers, and runs domain-specific skills, routes user intent to workflows via symlinks, and automates multi-step pipelines via the Refly CLI.
Build AI agents with tool calling and multi-step reasoning. Generate, manage, and orchestrate custom skill files for Claude Code, Cursor, Cline, and other AI assistants to standardize your development workflows.
Evidence-based debugging for Python, Node.js, and Java applications using runtime execution traces and diagnostic MCP tools.
Audit UI code for Web Interface Guidelines compliance. Automatically checks accessibility, design standards, and UX best practices.
Monitor US-Iran strike probability via real-time open-source signals including market odds, flight traffic, energy prices, and geopolitical alerts.
Security advisory monitoring for NanoClaw WhatsApp bots, providing vulnerability scanning, skill safety checks, and integrity protection through MCP tools.
Transforms complex information into structured study notes, summaries, and practice questions for effective learning and information retention.
Audit Packmind documentation by cross-referencing MDX files against the codebase to detect broken links, outdated CLI references, and missing coverage.
Track and execute code implementation using Mighty (mt) tasks, with progress comments, linked evidence, recorded design decisions, and clean closeout workflows.