abp-integration-testing
Generate integration tests for ASP.NET Core ABP Framework application services and HTTP APIs. Use when the user requests integration tests, end-to-end tests, API tests, or wants to verify ABP framework integration points (repositories, authorization, validation, multi-tenancy, unit-of-work, data filters). Trigger even if the user just says "add tests" for an ApplicationService — ask if they want unit or integration tests.
abp-service-patterns
ABP Framework application layer patterns including AppServices, DTOs, Mapperly mapping, Unit of Work, and common patterns like Filter DTOs and ResponseModel. Use when: (1) creating AppServices, (2) mapping DTOs with Mapperly, (3) implementing list filtering, (4) wrapping API responses.
Convex Agents RAG
Implements Retrieval-Augmented Generation (RAG) patterns to enhance agents with custom knowledge bases. Use this when agents need to search through documents, retrieve context from a knowledge base, or ground responses in specific data.
Convex Agents Streaming
Streams agent responses in real-time to clients without blocking. Use this for responsive UIs, long-running generations, and asynchronous streaming to multiple clients.
Convex Agents Workflows
Orchestrates multi-step agent operations with durable execution, automatic retries, and recovery from failures. Use this for complex workflows that need to survive server restarts or coordinate multiple agents.
Convex Agents Rate Limiting
Controls message frequency and token usage to prevent abuse and manage API budgets. Use this to implement per-user limits, global caps, burst capacity, and token quota management.
Convex Agents Usage Tracking
Tracks LLM token consumption and usage metrics for billing, monitoring, and optimization. Use this to log token usage, calculate costs, generate invoices, and understand which agents or users consume the most resources.
Convex Agents Context
Customizes what information the LLM receives for each generation. Use this to control message history, implement RAG context injection, search across threads, and provide custom context.
convex-actions-general
This skill should be used when working with Convex actions, HTTP endpoints, validators, schemas, environment variables, scheduling, file storage, and TypeScript patterns. It provides comprehensive guidelines for function definitions, API design, database limits, and advanced Convex features.
betterauth-tanstack-convex
Step-by-step guide for setting up Better Auth authentication with Convex and TanStack Start. This skill should be used when configuring authentication in a Convex + TanStack Start project, troubleshooting auth issues, or implementing sign up/sign in/sign out flows. Covers installation, environment variables, SSR authentication, route handlers, and the expectAuth pattern.
Convex Agents Playground
Sets up a web UI for testing, debugging, and developing agents without code. Use this to manually test agents, browse conversation history, and verify behavior in real-time.
Convex Agents Files
Handles file uploads, image attachments, and media processing in agent conversations. Use this when agents analyze images, process documents, or generate files.
ai-elements-workflow
This skill provides guidance for building workflow visualizations using Vercel AI Elements and React Flow. It should be used when implementing interactive node-based interfaces, workflow diagrams, or process flow visualizations in Next.js applications. Covers Canvas, Node, Edge, Connection, Controls, Panel, and Toolbar components.
convex-tanstack
Comprehensive guide for building full-stack applications with Convex and TanStack Start. This skill should be used when working on projects that use Convex as the backend database with TanStack Start (React meta-framework). Covers schema design, queries, mutations, actions, authentication with Better Auth, routing, data fetching patterns, SSR, file storage, scheduling, AI agents, and frontend patterns. Use this when implementing features, debugging issues, or needing guidance on Convex + TanStack Start best practices.
vercel-ai-elements
This skill provides comprehensive documentation for all 23 Vercel AI Elements components organized by category (Message, Conversation, Input/Interaction, Content Display, AI Processing, Advanced Features). Use when users ask about building AI chatbots, need component documentation, want API references for Vercel AI Elements, or need integration examples with the AI SDK.
Convex Agents Fundamentals
Sets up and configures Convex agents for chat-based AI interactions. Use this when initializing agent instances, creating conversation threads, and generating basic text or structured responses from LLMs. Essential foundation for any Convex agent implementation.
Convex Agents Human Agents
Integrates human agents into automated workflows for human-in-the-loop interactions. Use this when humans need to respond alongside AI agents, handle escalations, or provide context that AI cannot determine.
Convex Agents Debugging
Troubleshoots agent behavior, logs LLM interactions, and inspects database state. Use this when responses are unexpected, to understand context the LLM receives, or to diagnose data issues.
Convex Agents Messages
Sends, saves, retrieves, and manages messages within agent conversations. Use this when handling user messages, displaying conversation history, and working with UIMessages for rich rendering.
Convex Agents Threads
Manages conversation threads to group messages into linear histories. Use this when organizing multi-turn conversations, managing per-user history, and handling thread metadata.
convex-mutations
This skill should be used when implementing Convex mutation functions. It provides comprehensive guidelines for defining, registering, calling, and scheduling mutations, including database operations, transactions, and scheduled job patterns.
convex-queries
This skill should be used when implementing Convex query functions. It provides comprehensive guidelines for defining, registering, calling, and optimizing queries, including pagination, full text search, and indexing patterns.
Convex Agents Tools
Enables agents to call external functions, APIs, and database operations through tool definitions. Use this when agents need to fetch data, perform actions, or integrate with external services while maintaining clean separation.
testing-strategy
Guidelines for writing effective tests in this project
frontend-design
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.
rp-why
Load when reflecting on AI collaboration quality or tracking growth over time. Analyzes session history to surface how deeply you think with AI tools and whether your orchestration sophistication matches your cognitive depth. Provides actionable nudges to push toward more strategic work.
code-review
Comprehensive code review checklist for pull requests
goose-blog-post
Write and publish blog posts for the block/goose open source project
api-setup
Set up API integration with configuration and helper scripts
beads
Use beads (bd) for persistent task tracking in coding projects. A git-backed issue tracker designed for AI agents with dependency graphs, hierarchical tasks, and multi-agent coordination.
kali-docker-pentesting
Comprehensive pentesting toolkit using Kali Linux Docker container. Provides direct access to 200+ security tools without MCP overhead. Use when conducting security assessments, penetration testing, vulnerability scanning, or security research. Works via direct docker exec commands for maximum efficiency.
chroma-local
Use when the user needs self-hosted or local Chroma for semantic search, including `ChromaClient`, `HttpClient`, or Python `EphemeralClient`, local persistence, Docker or `chroma run`, or OSS Chroma without Chroma Cloud features.
chroma-cloud
Provides expertise on Chroma Cloud integration for semantic search and hybrid search applications. Use when the user is working with Chroma Cloud, CloudClient, managed collections, Schema(), Search(), hybrid search, or Chroma Cloud CLI workflows.
python-execution
编写或运行 PEP 723 临时 Python 脚本、处理 Python 执行环境，以及选择 Python 格式与 lint 检查方式时使用。已有 CLI 按其自身入口运行；创建具有稳定接口的单文件 CLI 使用 uv-cli-creator。
repository-workflow
处理从本地 Git 变更到 GitHub/GitLab 协作发布的完整工作流；适用于创建或更新分支、语义化 commit message、commit、push、Issue/PR/MR 文案、inline review reply、Breaking Change 与提交范围核对。纯只读的平台查询不使用本 skill。
review-fix-loop
用三个相互隔离的干净 subagent 并行做代码审查、由主 agent 判断审查意见价值、修复有效问题并提交推送，直到同一批三个 reviewer 都没有有价值审查意见。适用于用户要求 review/fix loop、clean review cycle、创建新 subagent 审查当前修改、反复 review 到没有问题、或“三个独立 reviewer 都没有有效建议”这类任务。
teach-me
以学习者控制的节奏，自顶向下、逐层讲解复杂主题。适用于用户希望“教我”“带我理解”“一步步讲”“先讲顶层”，或明确要求不要一次性展示全部信息的场景。
ticktick-cli
使用 Python CLI 与 Dida365 Open API 交互以管理滴答清单任务/项目，适用于需要通过脚本或命令行调用滴答清单接口的场景（如项目/任务的查询、创建、更新、完成、删除）。
google-mail-cli
使用 Python CLI 与 Gmail API 交互以搜索和读取邮件、整理标签、创建或更新草稿、发送邮件及管理过滤规则；适用于用户明确要求操作自己的 Gmail 账户时，不用于其它邮件服务。
upstream-pr-staging
在向 GitHub 上游提交 PR 前，先用用户 fork 中的预审 PR 审查 AI 辅助产出的代码、提交、PR 文案和 CI 证据，并通过独立 comment 收敛内部记录；适用于 fork 预审、低干扰验证、内部 review/CI、red/green 证据和正式上游 PR 重放。
uv-cli-creator
创建或修改基于 PEP 723、由 uv 管理且可直接执行的单文件 Python CLI。一次性分析脚本和一般执行环境问题使用 python-execution。
codex-thread-namer
为当前 Codex thread 设置名称；仅当用户手动调用或明确要求命名、重命名、整理当前 Codex 会话标题时使用，永远不要自动调用。
grill-me
针对用户的计划、决策或想法持续深入追问，并按决策树前沿分轮做压力测试。适用于用户希望检验自己的思路，或使用“grill”“拷问”“盘问”等触发词的场景。
dcjanus-merge-policy
将 GitHub 或 GitLab 仓库配置为 DCjanus 偏好的合并策略，包括 squash、合并方式、提交消息模板和源分支删除设置。适用于明确要求采用 DCjanus 个人合并策略的场景，不用于一般 PR/MR 合并操作。
dcjanus-tasklog
当需要使用 DCjanus 的个人任务模型查询或管理 Linear Issue、Comment、Team、Workflow status、原生关系或 Custom View 时使用。
google-sheets-cli
使用 Python CLI 与 Google Sheets API 交互以新建、读取、更新、批量写入、追加或清空 Google Sheets 在线表格；适用于需要通过 OAuth 授权操作 Google Workspace 表格的场景。
dependency-management
新增、移除或升级项目及长期维护脚本的依赖，以及修改项目版本时使用。一次性 Python 脚本使用 python-execution；库的选型比较使用 dcjanus-preferences。
domain-modeling
构建并持续校准项目的领域模型。适用于讨论代码库术语、编写或编辑 GLOSSARY.md，或记录或编辑 ADR 的场景。
google-calendar-cli
使用 Python CLI 与 Google Calendar API 交互以查看日历和事件、查询空闲忙碌时间、创建、更新或删除事件；适用于用户明确要求操作自己的 Google 日历时，不用于其它日历服务。
jira-cli
通过内置 Python CLI 直接调用 Jira Server/Data Center REST API v2，查询、创建、编辑、流转和删除 Issue、Epic、Sub-task、评论、附件、关联、Watcher、Vote 与 Worklog，并查询 Saved Filter、项目、字段、Board 和 Sprint。适用于需要可控地操作 Jira、保留 Jira wiki markup 并精确管理请求与输出的场景。
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