zapier-integration-helper
Skills(SKILL.md)は、AIエージェント(Claude Code、Cursor、Codexなど)に特定の能力を追加するための設定ファイルです。
詳しく見る →zapier-integration-helper
acceptance-criteria-creator
asana-task-creator
audit-trail-helper
backlog-grooming-assistant
change-request-generator
confluence-page-generator
definition-of-done-generator
executive-summary-creator
github-issue-creator
github-project-setup
gitlab-epic-creator
governance-checklist-generator
impact-analysis-helper
jira-ticket-generator
jira-workflow-creator
kpi-dashboard-template
linear-issue-generator
okr-tracker-creator
risk-assessment-creator
roadmap-generator
sla-monitor-setup
sprint-planning-helper
stakeholder-communication-template
status-report-generator
user-story-generator
example-skill
test-skill
schema-optimization-orchestrator
Coordinate multiple Claude Code sessions as a team — lead + teammates with shared task lists, mailbox messaging, and file-lock claiming. Patterns for team sizing, task decomposition, and when to use teams vs sub-agents vs worktrees.
Turn a conversation into an issue that still reads correctly after a
Master the four operations of context engineering — Write, Select, Compress, Isolate. Manage token budgets, compaction strategies, and context partitioning to keep AI sessions sharp and efficient.
Optimize token usage and context management. Use when sessions feel slow, context is degraded, or you're running out of budget.
Remove AI-generated code slop, unnecessary comments, and over-engineering from the current branch diff. Cleans up boilerplate, simplifies abstractions, and strips defensive code. Use when cleaning up code, simplifying, removing boilerplate, or before committing.
LLM-powered quality verification using prompt hooks. Validates commit messages, code patterns, and conventions using AI before allowing operations. Use to set up intelligent guardrails.
Orient fast in unfamiliar code. The deliverable is a map, not a tour.
Wire Commands, Agents, and Skills together for complex features. Use when building features that need research, planning, and implementation phases.
Stress-test a plan by walking its decision tree one question at a time. Use when the user wants to pressure-test a design before implementation.
Complete AI coding workflow system. Orchestration patterns, 18 hook events, 5 agents, cross-agent support, reference guides, and searchable learnings. Works with Claude Code, Cursor, and 32+ agents.
Different from wrap-up. Wrap-up is a checklist for *you*. Handoff is a document written for the *next session*.
Use when saying "commit", "save changes", or ready to commit after making changes.
Score every decision point with a Thoroughness Rating (1-10). AI makes the marginal cost of doing things properly near-zero — pick the higher-rated option every time. Includes scope checks to distinguish contained vs unbounded work.
Reduce token waste by 40-60% through anti-sycophancy rules, tool-call budgets, one-pass coding, task profiles, and read-before-write enforcement. Inspired by drona23/claude-token-efficient.
Adapt and debug existing or new models for vLLM on Ascend NPU. Implement in /vllm-workspace/vllm and /vllm-workspace/vllm-ascend, validate via direct vllm serve from /workspace, and deliver one signed commit in the current repo.
Enhance your AI coding agent with knowledge about AI Elements.
Initial setup of Vercel Workflow SDK **before** `workflow` is installed. Fetch the official getting-started guide for the user's framework.
Your knowledge of `workflow` is outdated.
Create custom tools, toolkits, and MCP integrations for Neuron AI agents. Use this skill when the user mentions creating tools, building toolkits, extending Tool class, defining tool properties, implementing tool execution, MCP server integration, Model Context Protocol, connecting external tools, or tool guidelines. Also trigger for any task involving ToolProperty, ArrayProperty, ObjectProperty, AbstractToolkit, McpConnector, or StdioTransport/SseHttpTransport/StreamableHttpTransport.
>
Run VMM tests locally with cargo xflowey vmm-tests-run. Load when running or debugging VMM tests, or when you need to understand the petri test framework, artifact handling, or cross-compilation for VMM tests.