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Found 190 Skills
Build messaging agents and apps with Spectrum — Photon's unified messaging SDK. Write your handler logic once and ship it across iMessage, WhatsApp Business, the terminal, or a custom platform. Spectrum is multi-platform by design and is becoming multi-language; the current SDK is `spectrum-ts` (TypeScript), with additional language SDKs planned. Use this skill for any Spectrum question — quickstart, multi-platform setup, receiving messages, content builders, spaces and users, reactions and replies, platform narrowing, the built-in providers (iMessage cloud/local/dedicated with message effects, Terminal TUI test harness, WhatsApp Business 1:1), custom event streams, graceful shutdown, building your own provider with `definePlatform`, and the production architecture patterns Photon uses internally to ship agents that live natively inside IM apps (five-stage inbound pipeline with debounce → batch flush → mark as read → generate → send, in-flight cancellation with abort signals, drain-in-handler, carry-forward, idempotent retries via stable client GUIDs and a startIndex resume cursor, per-resource memory scope `resourceId` vs `threadId`, durable job-failure audit log). This is the entry point for the skill; consult the topic files in this directory for full reference. Keywords: spectrum, spectrum-ts, photon, unified messaging, multi-platform, multi-language, im agent, messaging agent, imessage, whatsapp, whatsapp business, terminal, tuichat, definePlatform, custom platform, platform provider, platform narrowing, app.messages, Spectrum(), space, send, reply, react, tapback, typing indicator, responding, startTyping, stopTyping, content builder, text, attachment, voice, contact, richlink, poll, group, custom content, message effects, bubble effect, screen effect, line model, dedicated line, shared pool, custom events, app.stop, lifecycle, SIGINT, graceful shutdown, message queue, debounce, batch, in-flight, cancellation, abort controller, carry forward, idempotent retry, client guid, dedup, deduplication, startIndex, resume cursor, working memory, resourceId, threadId, per-resource memory, job failure, audit log, race condition, worker crash, retry, pg-boss, queue worker, conversational agent, chat agent, native messaging, agent architecture, production agent, spectrum patterns, best practices.
Configure AI coding agents like Cursor, GitHub Copilot, or Claude Code with project-specific patterns, coding guidelines, and MCP servers for consistent AI-assisted development.
API contract design conventions for FastAPI projects with Pydantic v2. Use during the design phase when planning new API endpoints, defining request/response contracts, designing pagination or filtering, standardizing error responses, or planning API versioning. Covers RESTful naming, HTTP method semantics, Pydantic v2 schema naming conventions (XxxCreate/XxxUpdate/XxxResponse), cursor-based pagination, standard error format, and OpenAPI documentation. Does NOT cover implementation details (use python-backend-expert) or system-level architecture (use system-architecture).
Create and manage AI agent sessions with multiple backends (SDK, Claude CLI, Codex, Cursor). Also supports multi-agent workflows with shared context, @mention coordination, and collaborative voting. Use for "start agent session", "create worker", "run agent", "multi-agent workflow", "agent collaboration", "test with tools", or when orchestrating AI conversations programmatically.
Guides setup and usage of the Zhin MCP (Model Context Protocol) server plugin. Covers configuration, available tools, resources, and prompts for AI assistant integration. Use when integrating Zhin with AI coding assistants like Claude or Cursor via MCP.
Generate AGENTS.md and CLAUDE.md files for a repository. AGENTS.md provides cross-tool agent instructions (supported by Claude Code, Cursor, Windsurf, Zed, Codex, and others). CLAUDE.md adds Claude-specific configuration and references AGENTS.md via @import. Use when a repo needs agent onboarding or when starting a new project.
Use this skill when handling user input in Phaser 4. Covers keyboard keys, mouse clicks and movement, touch events, pointer handling, drag and drop, hit areas, interactive objects, and gamepad support. Triggers on: keyboard, mouse, touch, pointer, drag, drop, click, input, gamepad, cursor keys.
This skill should be used when the user wants to check whether an agent skill is portable across providers. Common triggers include "is this skill cross-provider safe", "will my skill work in cursor", "audit skill compatibility", "check if this loads in codex", and "which providers support this skill". Spawns one agent per provider in parallel using bundled provider-doc snapshots (refreshed on cadence — never fetched at runtime) and produces a compatibility matrix plus a COMPAT.md report. Skip when authoring a new skill (use skill-creator) or rerunning baselines (use skill-eval).
Helm chart development agent skill and plugin for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw — chart scaffolding, values design, template patterns, dependency management, security hardening, and chart testing. Use when: user wants to create or improve Helm charts, design values.yaml files, implement template helpers, audit chart security (RBAC, network policies, pod security), manage subcharts, or run helm lint/test.
Portable .agent/ folder with memory, skills, and protocols that works across Claude Code, Cursor, Windsurf, and other AI coding harnesses
Write a high-quality prompt for any LLM or AI assistant — Claude, Claude Code, ChatGPT, Gemini, Cursor, Windsurf, Copilot, or any coding / chat agent. Use this skill whenever the user asks to write, improve, refine, shorten, or rewrite a prompt; asks "how should I phrase this for [model]" or "what's a good prompt for [task]"; describes a task they want an AI to do but hasn't yet formulated it as a prompt; or pastes an existing prompt and asks for revision. Based on Boris's (Anthropic, Claude Code creator) prompt methodology — short and accurate prompts, plan-before-code, feedback loops, persistent context in files. The universal principles (short, plan-first, feedback-loop, no-padding) apply to any LLM; the Claude-Code-specific anchors (CLAUDE.md, @file, slash commands) only apply when the target is Claude Code. If the user's intent is unclear (target model, deliverable, scope, or whether the AI has a way to self-verify is missing), ask 1–3 targeted clarifying questions via AskUserQuestion before writing the prompt.
Use when you need to refactor Java code for high performance — including memory/allocation reduction, CPU hot-path optimization, and syntax/API/control-flow improvements. This should trigger for requests such as Review Java code for high performance; Optimize Java hot path; Reduce Java allocations; Improve Java latency/throughput. Part of cursor-rules-java project