Loading...
Loading...
Found 13,581 Skills
Orchestrates implementation of a plan file by delegating work to subagents in parallel. Verifies git branch state, tracks progress, and ensures high-quality implementation. Invoke with a plan file path and optional model override: /implement plans/my-plan.md [--model sonnet]
Installs NemoClaw, launches a sandbox, and runs the first agent prompt. Use when onboarding, installing, or launching a NemoClaw sandbox for the first time. Trigger keywords - nemoclaw quickstart, install nemoclaw openclaw sandbox, nemohermes quickstart, hermes agent nemoclaw, run hermes openshell sandbox, nemoclaw prerequisites, nemoclaw supported platforms, nemoclaw hardware software, nemoclaw windows wsl2 setup, nemoclaw install windows docker desktop.
Manage durable working-session memory for coding agents. Use when a user asks to preserve or recover agent context across disconnects, VS Code restarts, long-running work, handoffs, or any session where important state should be written periodically under the repo's session directory.
INTERNAL sub-agent for blind 9-dimensional rubric scoring. **NOT a user-facing skill — do NOT invoke from the main conversation.** It is called via the Task tool by cheat-score / cheat-predict / cheat-bump to generate a context-isolated score for a script. It ONLY accepts script_path + rubric_notes_path; any other input will be refused. It outputs strict JSON: 9 dimensions × {score 0-5, confidence enum, one-line reason}. **It strictly refuses to read** .cheat-state.json, predictions/*, retro sections, or any content that may leak post-publish data. This is Channel B in the 3-channel calibration model (A=main, B=blind sub-agent, C=cross-model).
Route agents to the right web access method only when built-in web access tools are unavailable or insufficient for the task. Use for public search/fetch, browser interaction, authenticated browsing, screenshots, web app testing, or Electron app control when built-in tools cannot handle the requirement.
Use when the user asks to research a topic in depth, map a competitive/market landscape, run a multi-source investigation, or "fan out" parallel research agents — anything where many findings must be gathered and then NOT lost. Enforces durable, detail-preserving research (write full findings to disk; keep a full appendix beside the synthesis).
Guide the Agent to consolidate case facts, legal provisions, court judgments, issues, parties, evidence, contract obligation relationships (Three-layer Model of Contract → Clause → Obligation, including Risk Rating Color Coding), and constitutive element subsumption results (Law → Element → Fact Subsumption Color Coding), generate superset legal relationship graph data compatible with law-powers' index.html/data.js, and write it into data.js.
Use when creating, updating, or improving agent skills.
Orchestrates design workflows by routing work through brainstorming, multi-agent review, and execution readiness in the correct order. Prevents premature implementation, skipped validation, and unreviewed high-risk designs.
Build real-time conversational AI voice engines using async worker pipelines, streaming transcription, LLM agents, and TTS synthesis with interrupt handling and multi-provider support
Intelligent agent for interpreting vague ERPNext development requests and producing concrete technical specifications. Use when receiving unclear requirements like 'make invoice auto-calculate', 'add approval workflow', 'sync with external system'. Triggers: user gives vague requirement, need to clarify scope, translate business need to technical spec, determine which ERPNext mechanisms to use, create implementation plan.
Agent-based declarative testing with YAML test specs. Tests run in sub-agents to preserve main context while executing many tests. Supports MCP servers, APIs, and browser automation. Use when: testing MCP servers, running integration tests, validating tool behavior after changes, or creating regression test suites. Keywords: yaml tests, agent testing, mcp test, integration tests.