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概要と使いどころ
Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use language-specific skills), or infrastructure setup (use deployment-engineer).
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。
Route plain-language requests for Pi, Claude Code, Codex, OpenCode, Gemini CLI, or ACP harness work into either OpenClaw ACP runtime sessions or direct acpx-driven sessions ("telephone game" flow). For coding-agent thread requests, read this skill first, then use only `sessions_spawn` for thread creation.
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。
Expert copy editor for Quarto (.qmd) files. Checks grammar, spelling, punctuation, and academic writing quality. Produces a structured markdown report organized by document section — never modifies the source file. Use when asked to proofread, check grammar, fix typos, or review prose in a .qmd document. For APSA style rules (numbers, citations, capitalization, abbreviations, neutral language), use the apsa-style skill instead. Supports an optional output-file argument and an optional @sec-label argument to restrict checking to one section.
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。
"Optimize multilingual product listings for international e-commerce including SEO localization, machine translation workflows, and cultural adaptation. Use this skill when the user needs to create product listings in multiple languages, optimize for local search, or adapt marketing content for different markets — even if they say 'translate our listings', 'optimize for local SEO', 'adapt for the Japanese market', or 'our translated listings don't convert'.".
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。
Generate comprehensive unit tests with proper structure, mocking, and coverage. Use for general unit testing guidance across languages.
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。
Turn raw budget-versus-actual data into plain-language explanations for non-finance managers. Focus on why numbers moved, what it means for the team, and what action to take — without requiring accounting knowledge. Use when managers need to understand their cost center performance. Do not use for technical accounting analysis, tax preparation, or external audit deliverables.
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。
Draft concise follow-up messages, nudges, reminders, and recap emails from open decisions, stale requests, or recent meetings. Use when the work is repetitive business writing with known context. Do not use for sensitive legal language, high-stakes negotiation, or board-level communication without 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。
Natural-language orchestration of security tools through Gemini. Translates intent ("scan this scope for exposed admin panels") into safe, parameterized tool pipelines using nmap, masscan, naabu, httpx, nuclei, ffuf, gobuster, subfinder, amass, dnsx, katana, gau, semgrep, trivy, checkov, gitleaks, syft, grype, and custom scripts. Use when the user wants Gemini to drive a chain of CLI tools end to end with guardrails.
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。
Reverse shell techniques playbook. Use when establishing remote shells including language one-liners, encrypted shells (OpenSSL/socat/ncat), web shells, PTY upgrades, file transfer methods, PowerShell shells, and Windows payload generation.
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 sensitive training data (PII, API keys, intellectual property, or code) directly from a deployed Large Language Model (LLM). This AI Red Teaming skill focuses on forcing models to regurgitate memorized, unredacted data from their massive internet-scraped datasets through repetition attacks, prefix continuation, and context window manipulation.
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。
Test Model Context Protocol (MCP) servers and tool-calling systems for security vulnerabilities including tool injection, parameter manipulation, privilege escalation, and data exfiltration through AI agent tool interfaces. Use this skill when assessing MCP server implementations, AI agent tool integrations, or any system that exposes tools to language models. Covers tool confusion attacks, cross-tool exploitation, and MCP server hardening assessment.
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。
Execute Indirect Prompt Injection attacks against Large Language Models (LLMs) by subtly embedding malicious instructions within external data sources (e.g., websites, documents, databases) that the LLM autonomously ingests. This forces the model to execute attacker-controlled commands under the guise of processing legitimate user requests.
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。
Execute sophisticated Data Extraction and Privacy Leakage attacks explicitly against Large Language Models (LLMs) to natively force the neural network entirely into organically regurgitating exact, verbatim strings of Highly Confidential Personally Identifiable Information (PII), proprietary source code, or copyrighted material categorically memorized intrinsically during its foundational pre-training phase.
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。
Generates NeMo Guardrails Colang (.co) files and YAML config blocks from a plain-language description of a chatbot's purpose, allowed behaviors, and constraints. Use this skill whenever a user wants to build guardrails for a chatbot, define allowed intents for an LLM, create an AI firewall with NeMo Guardrails, generate Colang flow definitions, or configure a semantic allow-list for a bot. Trigger this skill even when the user just describes what their bot should and shouldn't do — generating the Colang and YAML is almost always what they need next.
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。
Systematically extract hidden system prompts, core directives, and invisible context intentionally concealed within Large Language Model (LLM) applications. This skill utilizes targeted linguistic engineering and boundary manipulation to bypass prompt opacity.
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。
Execute sophisticated Prompt Injection and Jailbreak techniques against Large Language Models (LLMs) to bypass safety filters, extract system prompts, and manipulate the AI's output to perform malicious or disallowed actions.
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。
Prevent common AI report pitfalls — bug blending, inflated threat models, and generic language. Train Claude with your best past reports for concise, technical submissions. Based on Critical Thinking Bug Bounty Podcast Episode 166.
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 when doing structural code search and rewriting - ast-grep linting, refactoring, multi-language patterns
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。
Search a named company's careers board, or resolve a natural-language query into a company list and search each. Extract job descriptions, score pool-anchored and channel-conditional fit against the user's profile, run a contrarian gate before tiers are finalized, and create role folders with assessments. Updates the job search tracker.
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。
Captures and evolves user preferences with confidence levels. Maintains Voice of the Customer model with exact language, dissatisfaction and aspiration signals. Handles promotion/demotion, global vs project scoping, auto memory system, and Omi wearable data integration.
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。
Graph-relational database with EdgeQL query language, code-first schema, link-based relations, computed properties, and fully typed TypeScript query builder
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 or revise Interpreter UI for non-developers with an ultra-minimal, utilitarian style inspired by the OpenAI website and ChatGPT, not the developer platform. Use when simplifying settings, onboarding, chat, forms, empty states, or helper copy; when removing extra containers, labels, or visual noise; when replacing technical wording with plain language; when clarifying hierarchy and making the main user action obvious; when making every visible label and status understandable to an average HR admin or other non-technical user; when hiding implementation details such as logs, IDs, endpoints, paths, or protocol names; or when a screen must respect the app's existing CSS variables and light/dark theme behavior.
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。