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概要と使いどころ
Knowledge systematization engine — analyze codebases, generate Personas, JTBD, Process Flows, technical docs, SOP user guides, API references. Output as Markdown or VitePress Premium. SEO-optimized, AI/LLM-readable. One scan = complete knowledge base.
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。
Scaffold, run, inspect, and evaluate model-agnostic agents that connect to MCP servers, skills, shell tools, and workflow packs.
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 Browserbase when an agent needs hosted browser sessions, remote automation, session recording, and scalable browser infrastructure without managing Chromium fleets directly. This skill packages the Browserbase API and SDK into a concrete workflow for running, monitoring, and extracting results from cloud browser sessions.
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。
Scan a repository for secrets, keep an auditable baseline, and review only newly introduced findings during commits or CI checks.
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。
Scan staged changes, commits, or repositories for secrets before they leave the workstation or CI job, instead of relying on a later platform-side catch.
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。
Add a runtime guard that evaluates agent actions, blocks dangerous commands or secret exposure, and audits new skills before they run.
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。
Creates Spotify Backstage software templates using template.yaml definitions with Scaffolder actions including fetch:template, publish:github, and catalog:register. Manages the Backstage Software Catalog via its REST API.
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。
Scan Python requirements and environments for known vulnerable or malicious packages before they move further through delivery or promotion workflows.
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 Deptry when an agent needs to verify that a Python project's declared dependencies still match the imports the code actually uses. The agent scans the codebase, flags unused direct dependencies, missing declarations, and transitive imports that only work by accident, then turns the findings into cleanup commits or release blockers.
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。
Uses Lynis to run an on-host security audit and turn the findings into a prioritized hardening checklist for an agent or operator. Invoke it when a machine is about to become internet-facing, after base image changes, or whenever you need a quick read on hardening drift instead of a generic vulnerability scan.
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。
This ASE skill uses zizmor to audit GitHub Actions workflows and composite actions for security mistakes before they ship. An agent can scan local repos or remote GitHub repositories, flag risky permission scopes and unsafe workflow patterns, and return plain output, GitHub-native findings, or SARIF for follow-up automation.
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 Cloudsplaining when an agent needs to flag privilege-escalation paths and overbroad IAM permissions before an AWS policy change reaches production.
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。
Orchestrates Apify actors for large-scale web scraping via the Apify Client SDK. Manages actor runs, dataset exports, and proxy configuration through the Apify API v2.
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Deploys intelligent web scraping actors on the Apify platform using the Apify SDK with RequestQueue and Dataset APIs. Handles dynamic content via Apify CheerioCrawler and PlaywrightCrawler with automatic scaling.
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。
Builds Apify Actors for scalable cloud scraping with automatic proxy management and storage. Uses the Apify SDK (Actor, Dataset, KeyValueStore, RequestQueue) and Crawlee library for robust crawling.
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Run K8sGPT as an MCP server so an agent can scan a Kubernetes cluster, explain unhealthy resources, and return prioritized remediation clues in natural language.
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。
Automates image editing workflows via the Adobe Photoshop API (Firefly Services). Supports smart object replacement, action playback, and PSD layer manipulation at scale.
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。
Inspect captured RAM images to enumerate processes, modules, handles, and suspicious in-memory behavior before escalation or evidence handoff.
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。
Agno is a high-performance Python framework for building, running, and managing agentic software at scale. It provides a three-layer architecture — framework, runtime, and control plane — enabling developers to go from agent prototype to production API in roughly 20 lines of code.
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。
Amplication is an open source developer platform for generating production-ready backend services and scaffolding from a visual workflow. This skill fits agents that need to help with service generation, codebase setup, schema-driven backend work, or self-hosted Amplication environments.
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。
Catch insensitive, inconsiderate writing with alex. This CLI tool and Node.js library scans Markdown and plain text for gender-biased, polarizing, or exclusionary language and suggests inclusive alternatives.
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, register, and gate AI-callable tool endpoints using the OpenSea Tool Registry (ERC-8257) on Base. Scaffold HTTPS tools with JSON Schema interfaces, register them onchain, gate access via NFT ownership, subscriptions, trait gating, or x402 pay-per-call (USDC), and call gated tools. For querying OpenSea marketplace data use opensea-api 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。
Decompose dense codebase-wide, multi-document, PDF, and aggregation work even when the input fits the context window, following Recursive Language Models (Zhang, Kraska, Khattab, 2025). Use when the user asks to analyse all files, a whole repo, all docs, large PDFs, or to aggregate or multi-hop across scattered sources. Skip one file, one function, a single needle, or a one-page PDF conversion. Triggers: long context, context rot, large codebase, many files, all files, big document, multi-document, PDF, aggregate, summarize everything, codebase-wide, multi-hop, recursive, sub-agents, map-reduce.
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 support operations guidance for customer service excellence. Use when designing ticket management systems, creating SLA policies, building support tier structures (L1/L2/L3), optimizing knowledge bases, defining severity levels and escalation procedures, implementing support metrics (CSAT, FRT, TTR, FCR), configuring support tool stacks, or building support-to-CS feedback loops. Covers Zendesk, Intercom, Freshdesk, and help desk 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。