Detection engine for Gmail email watches: iterates the watch list, polls gws for new matching mail per watch, dedups, drops automated/self, and hands matches (labeled by sender) to the shared drafting turn. Provisioned and run automatically by the shared email-watch backing job.

Create, search, triage, label, assign, comment on, and close GitHub issues using the gh CLI, with a curl REST fallback.

File a locally-captured, already-redacted Gini crash report as a GitHub issue, with the user's consent. Reads the pending crash queue and delegates the actual filing to the github-issues skill.

Manage Apple Notes via memo CLI: create, search, edit.

Gini's self-knowledge: how Gini configures, extends, and operates on its own state via /api/* and registered tools. Load when the user asks Gini about its own capabilities or asks Gini to modify its own configuration.

Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.

Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using Resemble AI

Turns product or tech specs into concrete Notion tasks that Claude code can implement. Breaks down spec pages into detailed implementation plans with clear tasks, acceptance criteria, and progress tracking to guide development from requirements to completion.

Searches across your Notion workspace, synthesizes findings from multiple pages, and creates comprehensive research documentation saved as new Notion pages. Turns scattered information into structured reports with proper citations and actionable insights.

Prepares meeting materials by gathering context from Notion, enriching with Claude research, and creating both an internal pre-read and external agenda saved to Notion. Helps you arrive prepared with comprehensive background and structured meeting docs.

Generate educational practice questions from lecture notes to test student understanding. Use when users request practice questions, exam preparation materials, study guides, or assessment items based on lecture content.

Analyze weekly marketing campaign performance data across channels. Use when analyzing multi-channel digital marketing data to calculate funnel metrics (CTR, CVR) and compare to benchmarks, compute cost and revenue efficiency metrics (ROAS, CPA, Net Profit), or get budget reallocation recommendations based on performance rules.

Multi-agent swarm coordination via the ClawTeam CLI. Use when the user wants to create agent teams, spawn multiple agents to work in parallel, coordinate tasks with dependencies, broadcast messages between agents, monitor progress via kanban board, or launch pre-built team templates (hedge-fund, code-review, research-paper). ClawTeam uses git worktree isolation + tmux + filesystem-based messaging. Trigger phrases: team, swarm, multi-agent, clawteam, spawn agents, parallel agents, agent team.

Multi-agent swarm orchestration. USE THIS (not delegate_task) when the user says team/swarm/multi-agent/clawteam/parallel-agents/agent-team, or asks for multi-perspective analysis (stocks, research, code review, strategy). Spawns N Hermes workers in tmux windows with git worktree isolation, file-based inboxes, and a kanban board. Four built-in templates: hedge-fund (7 analyst agents), research-paper, code-review, strategy-room.

Use this skill when working inside the ClawTeam repository itself: local development, debugging, reviewing, testing, validating multi-agent flows, or checking whether a code change actually works end-to-end. Use the repository bootstrap scripts to standardize the local `clawteam` command and to wire project-local `.agents` and `.claude` skills back to this repository. This skill is repository-development oriented, not a general end-user usage guide.

通用学习器 - 从任何领域的Prompt中自动提取可复用元素，持续学习和积累知识

提示词分析与洞察 - 查看Prompt详情、对比差异、推荐相似提示词、元素库统计

智能提示词生成器 v2.0 - 支持人像/跨domain/设计三种模式，语义理解、常识推理、一致性检查

Converts an arxiv paper into a minimal, citation-anchored Python implementation. Trigger when user runs /paper2code with an arxiv URL or paper ID, says "implement this paper", or pastes an arxiv link asking for implementation. Flags all ambiguities honestly. Never invents implementation details not stated in the paper.

Get a new developer up and running with the Chops codebase — prerequisites, build, architecture, and common tasks.