AI & ML

peterfei Bundle

技术负责人 Agent — 技术架构决策、代码审查、团队技术管理、技术规划与路线图

peterfei Bundle

产品经理 Agent — 产品规划、需求分析、用户研究、路线图制定、竞品分析

peterfei Bundle

全栈开发 Agent — 前后端一体化开发、API 集成、端到端功能实现

juneleetime Skill

管理 Work Agent 平台能力。通过 `workctl schema` 发现动态产品和命令，再用结构化输出执行操作。

juneleetime Bundle

用于 Amazon 新品广告初始化与 SIF 数据增强广告启动方案。适用于新品广告投放方案、广告初始化、广告投放策略 Skill、SIF MCP 广告数据分析、SP 自动/手动广告结构、ASIN 定向、关键词分组、预算分配，或把广告 SOP 转成可执行初始化方案。

juneleetime Bundle

为精品开发型亚马逊卖家构建、审查和迭代证据驱动的广告关键词方案。用于通过优麦云（SellerSpace）MCP 读取已授权 Amazon 店铺/站点、商品 ASIN、销售、库存和广告一方数据，并与卖家精灵 MCP 的类目、竞品、关键词和趋势估算合并；也用于新品冷启动词包、搜索词收割、Sponsored Products 或 Sponsored Brands 关键词分层、匹配方式、否定词、ACoS/CPC、预算实验、Listing 承接和产品定义反馈。缺少商品成本时仍可完成方向、相关性和表现判断，但必须把利润与精确竞价标为待确认；不得保存密钥、跨店擅自查询或把第三方估算冒充店铺事实。

tma1-ai Skill

Query TMA1 observability data (Claude Code, Codex, GitHub Copilot CLI, OpenClaw, any OTel SDK). Use when the user asks: how much did I spend, token usage, what has my agent been doing, agent cost, show me traces, show me events, check for errors, model comparison, tool usage.

tma1-ai Skill

List recent sessions on this project by agent — peer agents (Codex, OpenClaw, Copilot CLI) or your own past sessions. Invoke when the user wants you to read another agent's review feedback, see what someone else tried, act on cross-agent context, or recall your own earlier work here. Trigger phrases: "what did codex do", "what did openclaw do", "what did copilot do", "peer sessions", "cross-agent context", "what did I do earlier", "我之前在这个项目做了什么", "/tma1-peer".

tma1-ai Skill

Install and configure TMA1 local observability (Claude Code, Codex, GitHub Copilot CLI, OpenClaw, any OTel SDK). Use when the user says: install tma1, setup observability, monitor my agent, track token usage, set up telemetry.

tma1-ai Skill

Search past agent sessions on this project by keyword, and read back any session's conversation. Invoke when the answer lives in an earlier session — how something was fixed before, what was decided, what an agent tried last week — or when the user hands you a session id. Trigger phrases: "what did we decide about", "how did we fix this before", "search my sessions", "find that session", "read session <id>", "之前是怎么解决的", "上次讨论过", "找一下以前的 session", "/tma1-search".

tma1-ai Skill

List recent sessions on this project by agent — peers (Claude Code, OpenClaw, Copilot CLI) or your own past sessions. Invoke this skill when the user asks you to read another agent's review feedback, see what someone else tried, act on cross-agent context, or recall your own earlier work here. Trigger phrases: "what did claude do", "what did openclaw do", "what did copilot do", "peer sessions", "cross-agent context", "what did I do earlier", "/tma1-peer".

tma1-ai Skill

Search past agent sessions on this project by keyword, and read back any session's conversation. Invoke this skill when the answer lives in an earlier session — how something was fixed before, what was decided, what another agent tried last week — or when the user hands you a session id. Trigger phrases: "what did we decide about", "how did we fix this before", "search my sessions", "find that session", "read session <id>", "/tma1-search".

valentierro Skill

Install and configure MCP servers in Cursor projects — mcp.json templates, dependency-based servers, and verification. Use when adding MCP servers, configuring Cursor integrations, or setting up agent tooling.

valentierro Skill

GitHub workflow via gh CLI and @valentierro/mcp-github — list repos, clone, branch, pull, push, and create PRs. Use when working with GitHub repos, git operations, pull requests, or the valentierro account.

valentierro Skill

Orchestrates multi-agent workflows in Cursor — delegates tasks to subagents, coordinates MCP tools and skills, and tracks progress. Use when the user wants to orchestrate agents, delegate subtasks, or run parallel agent workflows.

frankxai Skill

Design and implement Model Context Protocol servers for standardized AI-to-data integration with resources, tools, prompts, and security best practices

frankxai Bundle

Intelligent model selection - routes tasks to Haiku (fast/cheap), Sonnet (balanced), or Opus (complex/strategic) based on task complexity analysis

frankxai Skill

Build autonomous AI agents using Claude Agent SDK with computer use, tool calling, MCP integration, and production best practices for Anthropic models

frankxai Skill

Current best practices for Model Context Protocol server design, implementation, and integration. Updated patterns for 2025 MCP ecosystem including multi-server orchestration, security, and performance.

frankxai Skill

Build production-ready multi-agent systems using OpenAI AgentKit and Agents SDK with best practices for agent orchestration, handoffs, and routines

frankxai Skill

Patterns for multi-agent coordination, task decomposition, handoffs, and workflow orchestration. Best practices for building and managing agent systems.

amkessler Bundle

Guide for creating effective skills. This skill should be used when users want to create a new skill or update an existing skill that extends Claude Code, Codex, or another agent with specialized knowledge, workflows, or tool integrations.

tavily-ai Skill

Set up, authenticate, update, troubleshoot, or choose between Tavily CLI web commands. Use when the user asks about the Tavily CLI, installing Tavily skills, first-time setup, authentication, keyless limits, CLI updates, or which Tavily command to use. For an ordinary web task, use the specific search, extract, map, crawl, research, or dynamic-search skill instead.

tavily-ai Skill

Search the web with LLM-optimized results via the Tavily CLI. Use this skill when the user wants to search the web, find articles, look up information, get recent news, discover sources, or says "search for", "find me", "look up", "what's the latest on", "find articles about", or needs current information from the internet. Returns relevant results with content snippets, relevance scores, and metadata — optimized for LLM consumption. Supports domain filtering, time ranges, and multiple search depths.

tavily-ai Skill

Extract clean markdown or text content from specific URLs via the Tavily CLI. Use this skill when the user has one or more URLs and wants their content, says "extract", "grab the content from", "pull the text from", "get the page at", "read this webpage", or needs clean text from web pages. Handles JavaScript-rendered pages, returns LLM-optimized markdown, and supports query-focused chunking for targeted extraction. Can process up to 20 URLs in a single call.

tavily-ai Skill

Programmatic Tavily search with context isolation. Use when web search or extraction will return large results that need filtering, deduplication, or multi-step triage before they enter the model context. Use tavily-search for ordinary lookups and tavily-research for end-to-end cited synthesis.

dzhng Skill

Use the local Codex CLI as an independent second agent. Two branches — (1) proactively run `codex review` for a second opinion after completing a substantive change, before presenting it as done or committing; (2) delegate a well-defined implementation task via `codex exec`, ONLY when the user explicitly asks for Codex to do it. Also covers how to prompt Codex.

dzhng Skill

Use the unprimed sub agent as a second set of eyes before accepting visual work — MANDATORY before declaring any user-reported visual bug fixed or claiming a visual change verified; primed eyes pass defects fresh eyes catch.

harshsinghmp Bundle

Bidirectional agent handoff and session resumption engine with ambient continuity. Generates lean, bounded context packets before dispatching subagents, resumes previous sessions with boundary-safe directory matching and unanswered questions leading, and maintains an always-current HANDOFF.md live-state file with a state-source ladder (live file, memory recall, project context, git forensics) probed on every workspace entry so any new conversation or agent continues prior work at lowest token cost — even after abrupt endings, with no explicit handoff requested. Dispatches embed the worktree-lease gate for shared checkouts.

dokhacgiakhoa Skill

Code review guidelines covering quality, security, and best practices.

pproenca Bundle

Use this skill whenever designing, building, reviewing, or diagnosing the pre-member journey of a two-sided trust marketplace — from anonymous landing through onboarding, registration, and the paid-membership paywall. Covers anonymous signal inference, what pet owners specifically need to validate before paying (safety, availability, competence, effort, local cost comparison), what pet sitters specifically need to validate (opportunity, first-stay path, daily commitment, hidden costs), information-asymmetry closure, progressive profile building, social proof, conversion psychology, onboarding intent capture, identity stitching, and pre-member measurement. Every rule is grounded in published consumer-trust and decision research — Cialdini, Kahneman, Roth, Fogg, Bandura, Slovic, Nielsen Norman Group, and the Airbnb / DoorDash engineering literature. Triggers on tasks involving visitor-to-member conversion, anonymous personalisation, onboarding flow design, paywall timing, pre-member ranking, or any question abo

pproenca Bundle

Use this skill whenever deciding what features to extract from raw marketplace assets — listing photos, owner-entered listing metadata, sitter wizard responses — to power item-to-item (similar listings), user-to-item (homefeed ranking), or user-to-user (mutual-fit matching) recommenders in a two-sided trust marketplace. Covers asset auditing, first-principles feature decomposition from the decision the user is making, vision-feature extraction (CLIP, room-type classification, amenity detection, aesthetic and quality scoring), listing text and metadata encoding (categoricals, multi-hot amenities, H3 geo-hashing, sentence-transformer description embeddings, structured pet triples), sitter wizard design (information-gain ordering, multiple-choice over free text, genuine skippability, hard constraint versus soft preference), derived-composition patterns for i2i / u2i / u2u (precomputed ANN shelves, multi-modal fusion, two-tower affinity, symmetric mutual-fit scoring, interpretable subscores), feature quality gove

frankxai Bundle

Advanced swarm-orchestration patterns for research, development, testing, and complex distributed workflows. Use when scaling beyond a few agents, designing dynamic swarm topologies, or coordinating large parallel agent workloads.

frankxai Bundle

Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows. Use when configuring Claude Code hooks, automating pre/post-task coordination, or wiring Git and memory hooks.

frankxai Skill

Use Claude's extended/adaptive thinking for deep reasoning, complex analysis, and multi-step synthesis — both the prompt patterns that elicit deliberation and the current thinking API (adaptive thinking + the effort parameter). Use when a problem needs thorough deliberation before answering, or when configuring thinking on a Claude request.

arcanedge-ai Bundle

Manage Git worktrees created or used during a Codex task. Use when an agent proposes an isolated checkout, parallel writers may need filesystem isolation, a task already owns auxiliary worktrees, or the root must integrate, preserve, or remove task-created worktrees before completion.