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Found 8,794 Skills
1Kosmos BlockID integration. Manage data, records, and automate workflows. Use when the user wants to interact with 1Kosmos BlockID data.
Manage parallel development with Git worktrees. Covers worktree creation with port allocation, environment sync, branch isolation for multi-agent workflows, cleanup automation, and Docker Compose integration. Use when working on multiple branches simultaneously, running parallel CI validations, or isolating agent workspaces.
Builder.io integration. Manage data, records, and automate workflows. Use when the user wants to interact with Builder.io data.
Reference skill for Zoom Video SDK. Use after routing to a custom-session workflow when the user needs full control over the video experience rather than an actual Zoom meeting.
Set up or update the agent-first engineering harness for any repository. Implements the complete scaffolding that makes AI coding agents effective: knowledge maps (AGENTS.md as a concise TOC), structured documentation, architecture boundaries, enforcement rules (.harness/*.yml specs), quality scoring, and process patterns for agent-driven development. Use this skill whenever someone wants to make a repo agent-ready, set up AGENTS.md or docs/ structure, define domain boundaries or golden principles, generate .harness/ configuration, audit agent readiness, or update an existing harness. Also trigger when a user reports problems with agent effectiveness, context management, or architectural drift — these are symptoms of a missing or stale harness. Trigger on: "harness this repo", "set up harness", "agent-first setup", "make this agent-ready", "update the harness", "assess agent readiness", "set up AGENTS.md", "organize for agents", or any discussion about structuring a codebase for AI agent workflows.
Complete toolkit for Huawei Ascend NPU model conversion and end-to-end inference adaptation. Workflow 1 auto-discovers input shapes and parameters from user source code. Workflow 2 exports PyTorch models to ONNX. Workflow 3 converts ONNX to .om via ATC with multi-CANN version support. Workflow 4 adapts the user's full inference pipeline (preprocessing + model + postprocessing) to run end-to-end on NPU. Workflow 5 verifies precision between ONNX and OM outputs. Workflow 6 generates a reproducible README. Supports any standard PyTorch/ONNX model. Use when converting, testing, or deploying models on Ascend AI processors.
Step-by-step wallet investigation workflow using Range AI MCP tools (risk score, sanctions, connections, transfers, funded-by, entities, cross-chain pivots) plus a one-shot prompt template. Use when the user runs investigations inside an MCP-connected client with Range enabled, or needs a structured checklist alongside crypto-investigation-compliance—not as legal advice or a substitute for Range’s live docs and API scopes.
Companion CLIs for Runpod workflows — HuggingFace, GitHub, Docker, and AWS.
Low-Code Generation uses AI to produce forms, tables, dashboards, and workflow UIs from natural language descriptions or schema definitions.
Grafana Alerting, Incident Response Management (IRM), and SLOs. Covers Grafana-managed and data source-managed alert rules, notification policies, contact points (Slack/PagerDuty/email/webhook), silences, muting, on-call scheduling, incident management workflows, and SLO configuration with burn-rate alerts. Use when configuring alerts, debugging notification routing, setting up on-call rotations, managing incidents, defining SLOs, or provisioning alerting via YAML/API.
Workflow for learning CuTe Python DSL by reading, importing, profiling, and extracting reusable patterns from CUTLASS Blackwell example kernels. Use when: (1) studying CUTLASS CuTe DSL reference implementations, (2) importing CUTLASS examples into the project runtime infrastructure, (3) building CuTe DSL knowledge base entries from profiling experiments, (4) understanding CuTe DSL API patterns, TMA pipelining, warpgroup scheduling, or persistent kernel structure.
Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.