「lms」の検索結果

Analyze test coverage with detailed metrics, identify gaps, and generate comprehensive reports. Use when measuring code coverage, finding untested code, or validating coverage thresholds.

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 a durable feature spec before touching code. Reads the codebase map, reads related models/migrations to ask technical questions (not generic ones), then produces a structured spec doc in docs/specs/. Designed to pipeline with /implementation --issue N. Use before any feature implementation.

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。

TODO: Brief description of what the Skill does and when to use it

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。

Stack-specific security implementation guidance (backend, frontend, database, infrastructure, operations) with auto-detection from your changes. Use when implementing a feature securely or hardening code as you write it. Trigger on "how do I secure this", "security best practices", "is this secure", "harden this". For scanning existing code use /security-verify.

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 security verification — SAST pattern scanning, DAST against a running app, OWASP Top 10 compliance, CVSS assessment, and adversarial code audit. Use before commit/push or when checking code for vulnerabilities. Trigger on "security scan", "is this vulnerable", "OWASP check", "security verify", "scan for vulnerabilities".

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。

Dispatch reviews to external companion AIs (Codex, Gemini, Claude) for an independent second opinion on code changes, PR readiness, doc completeness, architecture, or implementation status. Use when you want a fresh-eyes review beyond Claude-native. Trigger on "get a second opinion", "companion review", "review my changes", "review the PR". For Claude-only quality checks use /code-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。

Plan version scope, verify release integrity (commit↔CHANGELOG, tag↔artifacts), generate changelogs, and run full releases. Use when cutting a release or bumping a version. Trigger on "cut a release", "bump the version", "generate changelog", "release plan", "verify the release".

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 REST APIs or function contracts with clear request/response specifications, error handling patterns, authentication strategies, and comprehensive 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。

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。

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。

Extract sensitive training data and internal capabilities from AI models through repeated programmatic querying. Use this skill when testing LLMs and ML models for data privacy leaks, intellectual property exposure, or unintended memorization. Covers membership inference, prompt extraction, training data reconstruction, and API boundary 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。

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。

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。

视频剪辑全链(download → transcribe → plan → cut → translate → karaoke)

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。

TTS + 词级对齐

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。

Write effective MCP server instructions for tool compaction support. Use when creating or updating the `instructions` parameter in MaaSProtectedFastMCP server initialization. Helps LLMs understand when to search for tools, workflow patterns, and cross-feature relationships.

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。

Master orchestrator for B2B SaaS content creation and the editorial production system behind it. Use this skill to produce blog articles, whitepapers, case studies, video scripts, newsletters and ghostwritten thought leadership, to develop content strategy, and to run the publishing line itself — content briefs, editorial calendars and production capacity, review and approval workflow, subject-matter-expert access, the style guide, an AI-drafting policy, and a content inventory with owners. Also triggers on: blog, article, case study, whitepaper, newsletter, video script, ghostwriting, content calendar, editorial, thought leadership, content strategy, content brief, content operations, content ops, editorial workflow, managing editor, who reviews this, our content pipeline is stuck, content is stuck in review, content audit, content inventory, content governance, style guide, house style, SME interview, expert interview, freelance writers, AI content policy, how much content can we actually produce. Also owns the customer academy and customer education: online courses, onboarding curriculum, tutorials, certification programs, digital credentials and badges, exam and pass-mark design, course freshness when the product UI changes, and whether the academy should sit behind a login. Triggers on: customer academy, customer education, online course, course library, learning path, curriculum, tutorial, certification program, certified users, digital badge, Open Badges, recertification, exam, pass mark, LMS, Skilljar, our tutorial shows the old UI, our course is out of date, should the academy be gated, do certified customers renew better.

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。

Transform vague inputs into precision-optimized AI prompts for Claude, ChatGPT, Gemini, or other LLMs. Use when user mentions "optimize prompt", "improve prompt", "lyra", "prompt engineering", or needs help crafting effective AI prompts.

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 content for AI Overviews (formerly SGE), ChatGPT web search, Perplexity, and other AI-powered search experiences. GEO analysis enriched with Ahrefs Brand Radar AI visibility data when available. Includes brand mention signals, AI crawler accessibility, llms.txt compliance, passage-level citability scoring, and platform-specific optimization. Use when user says "AI Overviews", "SGE", "GEO", "AI search", "LLM optimization", "Perplexity", "AI citations", "ChatGPT search", "AI visibility", or "Brand Radar".

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。

High-throughput inference on FriendliAI — optimized serving engine for low-latency, cost-efficient LLMs.

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。

Fine-tune large language models — data curation, SFT, parameter-efficient methods (LoRA), evaluation, and avoiding common training failures. Use when a base model needs task-specific behavior or domain knowledge.

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。