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概要と使いどころ
Automatically fix broken OpenCLI adapters when commands fail. Load this skill when an opencli command fails — it guides you through collecting a trace artifact, patching the adapter, retrying, and filing an upstream GitHub issue after a verified fix. Works with any AI agent.
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。
Assess a GitHub issue or PR for demonstrated need, supported alternatives, correctness, and maintainer action. Desk review only.
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。
Reference skill for Zoom Meeting SDK. Use after routing to a meeting-embed workflow when implementing real Zoom meeting joins, platform-specific SDK behavior, auth and join flows, waiting room issues, or meeting bot 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。
Reads a forwarded customer email or ticket, pulls order and refund status from a payments connector (PayPal, Square, or Stripe) or Shopify, account history from the CRM, and open tickets from a support desk (Zoho Desk), drafts a tone-matched reply in the owner's writing voice, and can issue a refund through the payments connector with explicit owner approval. With Shopify connected it also runs a proactive order-triage mode that surfaces orders needing attention — unfulfilled past the promised window, payment problems, pending refunds, stuck shipments — and drafts the next action for each before the customer has to ask. Use when the user says "draft a response," "answer this customer," "where's my order," "I want a refund," "check my orders," or "anything about to blow up."
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。
Assess and classify legal risks using a severity-by-likelihood framework with escalation criteria. Use when evaluating contract risk, assessing deal exposure, classifying issues by severity, or determining whether a matter needs senior counsel or outside legal 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。
Draft a professional customer-facing response tailored to the situation and relationship. Use when answering a product question, responding to an escalation or outage, delivering bad news like a delay or won't-fix, declining a feature request, or replying to a billing 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。
Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.
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。
FastGPT Docker Compose self-hosting deployment workflow. Use when a user asks an AI agent to deploy FastGPT with Docker by referencing /deploy/SKILL.md, including creating an empty fastgpt directory, running the install script in non-interactive mode, starting Docker Compose, monitoring service health, troubleshooting compose issues, and returning the access URL plus root credentials and next steps.
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 a Factory work item's issue — trace history, understand architecture, diagnose root cause, then advance the stage
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 early when debugging a medium or hard bug, especially when tests alone may not reveal the real runtime failure. Trigger this before extended TDD iteration when a bug involves runtime state, ordering, persistence, streaming, concurrency, UI/manual reproduction, external services, or when a red or newly passing test may not model the real issue. Skip only when the root cause is already directly proven by a stack trace or deterministic test that exercises the real runtime path.
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。
Authoring playbook for building agents that triage and reply to customer messages — support tickets, email inquiries, chat questions, refund requests, or product issues. Use this when the user wants an agent that handles inbound customer questions, drafts replies, escalates hard cases, summarizes tickets, or follows a support playbook.
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 indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.
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。
Hypothesis-driven debugging methodology for hard bugs. Use this skill whenever you're investigating non-trivial bugs, unexpected behavior, flaky tests, or tracing issues through complex systems. Activate proactively when debugging requires more than a quick glance — especially when the first attempt at a fix didn't work, when behavior seems "impossible", or when you're tempted to blame an external system (model, API, library) without evidence.
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。
Diagnose memory leaks in the Qwen Code CLI using heap snapshots and the chrome-devtools CLI. Use when investigating high memory usage, unbounded growth, or suspected object retention issues.
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 the scheduled repo-hygiene workflow runs from GitHub Actions (or an operator dry-run) to scan the repository for small, certain docs/test/code hygiene issues and fix them as one batched 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。
Review and repair current local changes until they converge, or run Qwen Code Autofix issue and review workflows from GitHub 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。
Use for a periodic repo-wide sweep of qwen-code for accumulated excess surface — dead components and files, orphaned locale keys, exports nothing consumes, added-then-removed scaffolding — filing candidates on a tracking issue and landing only what a maintainer has said yes to. Repo-wide and evidence-first; every consumer is named before anything is deleted. Not for tidying a diff you just wrote (that is bundled `/simplify`) and not for defects (that is `/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。
Guide for running end-to-end tests of the Qwen Code CLI, including headless mode, MCP server testing, and API traffic inspection. Use this skill whenever you need to verify CLI behavior with real model calls, reproduce user-reported bugs end-to-end, test MCP tool integrations, or inspect raw API request/response payloads. Trigger on mentions of E2E testing, headless testing, MCP tool testing, or reproducing issues.
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。
When the user wants to implement, audit, or validate structured data (schema markup) on their website. Use when the user mentions 'structured data,' 'schema.org,' 'JSON-LD,' 'rich results,' 'rich snippets,' 'schema markup,' 'FAQ schema,' 'Product schema,' 'HowTo schema,' or 'structured data errors in Search Console.' Also use when someone asks why their content isn't showing rich results or wants to improve AI search visibility. NOT for general SEO audits (use seo-audit) or technical SEO crawl issues (use site-architecture).
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。
When the user wants to create SEO-driven pages at scale using templates and data. Also use when the user mentions "programmatic SEO," "template pages," "pages at scale," "directory pages," "location pages," "[keyword] + [city] pages," "comparison pages," "integration pages," or "building many pages for SEO." For auditing existing SEO issues, see seo-audit.
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 the user asks to review pull requests, analyze code changes, check for security issues in PRs, or assess code quality of diffs.
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。
Navigate, manage, and optimize monorepos. Covers Turborepo, Nx, pnpm workspaces, and Lerna. Cross-package impact analysis, selective builds/tests on affected packages, remote caching, dependency graph visualization, and structured multi-repo to monorepo migrations. Use when setting up a new monorepo, optimizing CI for a large workspace, debugging cross-package dependency issues, or planning a multi-repo consolidation.
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。
Pre-production audit that scans a codebase for security, database, deployment, code quality, AI/LLM, dependency, frontend, and observability issues. Intercepts deploy commands and blocks until critical items pass. Stack-agnostic. Use for "run ship gate", "am I ready to ship", "pre-launch audit", "can I deploy", "push to production", "go live checklist", "preflight check". Not for CI/CD setup or infra provisioning.
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。
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
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。