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Found 422 Skills
Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, conservative training verification, default routing, verified SOTA claims, or implicit experimentation.
Sub-skill for the intake phase of README-first AI repo reproduction. Use when the task is specifically to scan a repository, read README and common project files, extract documented commands, classify inference or evaluation or training candidates, and return a minimum trustworthy plan to the main skill. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.
Sub-skill for the execution-evidence and reporting phase of README-first AI repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files including patch notes when repository files changed. Do not use for initial repo intake, generic environment setup, paper lookup, target selection, or end-to-end orchestration by itself.
Sub-skill for environment and asset preparation in README-first AI repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target.
Use the Orca CLI to coordinate multiple coding agents via inter-agent messaging, task DAGs, dispatch with preamble injection, decision gates, and coordinator loops. Use when an agent needs to send or check inter-agent messages; create, dispatch, or track orchestration tasks; coordinate multi-agent workflows; or act as a coordinator dispatching work across terminals. Triggers include "orchestrate agents", "dispatch task", "send message to agent", "check inbox", "coordinate agents", "multi-agent", "create task DAG", "worker_done", "escalation", or any task involving inter-agent coordination through Orca.
Recommend Azure VM sizes, VM Scale Sets (VMSS), and configurations based on workload requirements, performance needs, and budget constraints. No Azure account required — uses public documentation and the Azure Retail Prices API. USE FOR: recommend VM size, which VM should I use, choose Azure VM, VM for web/database/ML/batch/HPC, GPU VM, compare VM sizes, cheapest VM, best VM for workload, VM pricing, cost estimate, burstable/compute/memory/storage optimized VM, confidential computing, VM trade-offs, VM families, VMSS, scale set recommendation, autoscale VMs, load balanced VMs, VMSS vs VM, scale out, horizontal scaling, flexible orchestration. DO NOT USE FOR: deploying VMs or VMSS, deploying apps (use azure-deploy), looking up existing VMs (use azure-resource-lookup), cost optimization of running VMs (use azure-cost-optimization), non-VM services like App Service or AKS.
This skill should be used when the user wants to "write agent code", "build an agent with ADK", "add a tool", "create a callback", "define an agent", "use state management", or needs ADK (Agent Development Kit) Python API patterns and code examples. Part of the Google ADK skills suite. It provides a quick reference for agent types, tool definitions, orchestration patterns, callbacks, and state management. Do NOT use for creating new projects (use google-agents-cli-scaffold) or deployment (use google-agents-cli-deploy).
oh-my-claudecode — Teams-first multi-agent orchestration layer for Claude Code. 32 specialized agents, smart model routing, persistent execution loops, and real-time HUD visibility. Zero learning curve.
Multi-agent orchestration layer for OpenAI Codex CLI. Provides 30 specialized agents, 40+ workflow skills, team orchestration in tmux, persistent MCP servers, and staged pipeline execution.
Produce programmable videos with Remotion using scene planning, asset orchestration, and validation gates for automated, brand-consistent video content.
Generate a Product Requirements Document (PRD) for ralph-tui task orchestration. Creates PRDs with user stories that can be converted to beads issues or prd.json for automated execution. Triggers on: create a prd, write prd for, plan this feature, requirements for, spec out.
Build automated AI workflows combining multiple models and services. Patterns: batch processing, scheduled tasks, event-driven pipelines, agent loops. Tools: inference.sh CLI, bash scripting, Python SDK, webhook integration. Use for: content automation, data processing, monitoring, scheduled generation. Triggers: ai automation, workflow automation, batch processing, ai pipeline, automated content, scheduled ai, ai cron, ai batch job, automated generation, ai workflow, content at scale, automation script, ai orchestration