Bash scripting workflow for creating production-ready shell scripts with defensive patterns, error handling, and testing.
日本語の概要は準備中です。原文の説明を表示しています。
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概要と使いどころ
Bash scripting workflow for creating production-ready shell scripts with defensive patterns, error handling, and testing.
日本語の概要は準備中です。原文の説明を表示しています。
Write copy following Sentry brand guidelines. Use when writing UI text, error messages, empty states, onboarding flows, 404 pages, documentation, marketing copy, or any user-facing content. Covers both Plain Speech (default) and Sentry Voice tones.
日本語の概要は準備中です。原文の説明を表示しています。
Build server-side call automation workflows including IVR systems, call routing, recording, and AI-powered interactions.
日本語の概要は準備中です。原文の説明を表示しています。
API documentation workflow for generating OpenAPI specs, creating developer guides, and maintaining comprehensive API documentation.
日本語の概要は準備中です。原文の説明を表示しています。
API security testing workflow for REST and GraphQL APIs covering authentication, authorization, rate limiting, input validation, and security best practices.
日本語の概要は準備中です。原文の説明を表示しています。
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
日本語の概要は準備中です。原文の説明を表示しています。
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
日本語の概要は準備中です。原文の説明を表示しています。
Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.
日本語の概要は準備中です。原文の説明を表示しています。
AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
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Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches. AI agents running in CI/CD pipelines.
日本語の概要は準備中です。原文の説明を表示しています。
Runs the daytime maintainer loop for NemoClaw, prioritizing items labeled with the current version target. Picks the highest-value item, executes the right workflow (merge gate, salvage, security sweep, test gaps, hotspot cooling, or sequencing), and reports progress. Use during the workday to land PRs and close issues. Designed for /loop (e.g. /loop 10m /nemoclaw-maintainer-day). Trigger keywords - maintainer day, work on PRs, land PRs, make progress, what's next, keep going, maintainer loop.
日本語の概要は準備中です。原文の説明を表示しています。
Explains how to run NemoClaw on a remote GPU instance, including the deprecated Brev compatibility path and the preferred installer plus onboard flow. Use when deploying NemoClaw to a remote VM, onboarding a Brev instance, or migrating away from the legacy `nemoclaw deploy` wrapper. Trigger keywords - deploy nemoclaw remote gpu, nemoclaw brev cloud deployment, nemoclaw sandbox hardening, container security, docker capabilities, process limits, nemoclaw telegram, telegram bot openclaw agent, openshell channel messaging.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when implementing, refactoring, or extending the internal table runtime inside this repository. Covers the current architecture layers, state model, composable flow, where different responsibilities live, how to add features cleanly, and what kinds of refactors are encouraged.
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Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.
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Continue working on an OpenSpec change by creating the next artifact. Use when the user wants to progress their change, create the next artifact, or continue their workflow.
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Start a new OpenSpec change using the experimental artifact workflow. Use when the user wants to create a new feature, fix, or modification with a structured step-by-step approach.
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Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
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Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework
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Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
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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.
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Create hand-drawn style diagrams using Excalidraw JSON format. Generate .excalidraw files for architecture diagrams, flowcharts, sequence diagrams, concept maps, and more. Files can be opened at excalidraw.com or uploaded for shareable links.
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Optional vendor skill for Parallel CLI — agent-native web search, extraction, deep research, enrichment, FindAll, and monitoring. Prefer JSON output and non-interactive flows.
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Full pull request lifecycle — create branches, commit changes, open PRs, monitor CI status, auto-fix failures, and merge. Works with gh CLI or falls back to git + GitHub REST API via curl.
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Set up GitHub authentication for the agent using git (universally available) or the gh CLI. Covers HTTPS tokens, SSH keys, credential helpers, and gh auth — with a detection flow to pick the right method automatically.
日本語の概要は準備中です。原文の説明を表示しています。