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概要と使いどころ
On-demand situational awareness — fresh git/PR snapshot plus continuation, distilled into one concrete "next action" suggestion. Use mid-session when you've lost track, after a compaction, or starting a new work block. Trigger on "where was I", "what's next", "progress", "what should I do now", "/progress".
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。
Review code for quality, security, and performance with comprehensive feedback.
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。
Zero-ceremony atomic task execution. Use for tiny fixes or enhancements (minutes, not hours) that don't warrant SDLC ceremony. Flags: --discuss (disambiguate first), --full (delegate to /pre-commit after execution), --issue N (link commit to GitHub issue).
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。
Fetch Claude Code documentation and help update configuration. Use /claude-code-guide <topic> where topic is: memory, skills, hooks, agents, settings, mcp, plugins, cli, overview, all
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。
Final-mile orchestrator - commit + push + PR creation. Run after /pre-commit passes. Chains: commit -> push -> /pr-creator (artifact check + PR). Use --issue N to auto-close a GitHub issue on merge. Use --main to ship directly to main with no PR (closes issue + deletes branch).
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。
Holistic project audit across endpoints, versions, docs, security, quality, CI, and library dimensions. Use when checking overall project health or running a periodic cross-cutting audit. Trigger on "project health", "audit the project", "health check", "is this project in good shape".
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。
Validate WCAG 2.1 Level AA compliance and accessibility best practices. Use when performing accessibility audits and WCAG certification.
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。
Design component architecture, REST API contracts, and data models (Pydantic schemas) before implementation. Use when planning how to build a feature, defining endpoints, or modeling data. Trigger on "design the architecture", "design the API", "data model", "how should I structure this feature".
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。
Analyze an existing codebase in parallel via 7 specialized mapper subagents. Outputs structured findings to docs/codebase/ for persistent cross-session context. Use when onboarding to a brownfield project or when reactive on-demand file reading is too slow.
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。
Convert a working prototype or ad-hoc solution into a permanent, tested, registered skill. Run after something works and you want it to stick.
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。
Post-deploy observability for production services — structured logging, RED metrics + Prometheus, Grafana dashboards, SLO-based alerting. Use when setting up monitoring or instrumenting a live service. Trigger on "add logging", "metrics", "dashboard", "alerting", "observability", "monitor this service".
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。
Claude-native review of code for quality, structure, naming, and maintainability (no external AI). Use when reviewing a file or diff for code quality before committing. Trigger on "review this code", "code review", "is this well-written". For security, use /security-verify; for companion-AI review, use /review.
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。
Safely merge GitHub pull requests using comprehensive pre-merge validation. Use this skill when the user asks to merge a PR, complete a PR workflow, or check if a PR is ready to merge. Validates CI/CD status, reviews, and conflicts before merging with the appropriate strategy.
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。
Triage, fix, and pre-flight a PR before merge. Checks mergeability, CI, reviews, and bot comments — fixes issues, runs /pre-commit before pushing, then hands off to /pr-merge.
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。
Fix bugs and technical debt with minimal ceremony. Issue GitHub optional — provide one for traceability or work free-form. Replaces /issue-fix.
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。
Investigate bugs with unknown root cause. Spawns fresh-context investigation subagent + parallel /review solve companion, persists hypotheses across iterations, hands off to /fix when root cause found.
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。
Run SQL queries against psql, BigQuery, or MySQL from the terminal, including natural-language-to-SQL and schema exploration. Use when analyzing data, inspecting DB state, or debugging tables. Trigger on "query the database", "SQL", "show me data from", "explore table".
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。
Create comprehensive pull requests with detailed descriptions, test plans, and proper git workflow. Use when finalizing features to ensure high-quality PRs.
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。
Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browser_use, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle hooks, MCP server setup, or monitoring/observability with Laminar or OpenLIT. Also trigger for questions about browser-use installation, prompting strategies, or sensitive data handling. Do NOT use this for Cloud API/SDK usage or pricing — use the cloud skill instead. Do NOT use this for directly automating a browser via CLI commands — use the browser-use skill instead.
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。
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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 LLM red-teaming skill for evaluating prompt injection, jailbreak, data exfiltration, tool abuse, agent hijack, RAG poisoning, model denial of service, and unsafe tool-use chains in Gemini, Claude, GPT, and open-weight models. Use to build evaluation harnesses, attack corpora, defensive guardrails, and red-team reports for AI systems you own or are authorized to test.
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。
Prompt engineering and task-specification skill for improving prompts, agent instructions, system messages, evaluation prompts, coding-agent tasks, multi-step workflows, and LLM output reliability. Use when rewriting vague prompts, adding constraints, reducing ambiguity, improving safety, creating reusable prompt templates, or converting goals into actionable agent instructions.
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。
在 Claude Code / OpenClaw / Cursor 中直接运行 agency-orchestrator YAML 工作流——无需 API key，使用当前会话的 LLM 作为执行引擎。当用户提供 .yaml 工作流文件或要求多角色协作完成任务时触发。
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。
Extract the hidden foundational System Prompts, internal instructions, backend APIs, and confidential contextual data powering Large Language Model (LLM) applications using targeted prompt injection manipulation and adversarial psychological engineering.
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。