3,111 artifacts
Skills, MCPs, agents, and plugins. Search to find fast, or page through the catalog.
Apply systematic problem-solving techniques when stuck. Use for complexity spirals, innovation blocks, recurring patterns, assumption constraints, simplification cascades, scale uncertainty.
5 expert personas debate proposed changes before implementation. Catches architectural, security, performance, and UX issues early. Use before major features or risky changes.
Discover related plans for the current task: same feature folder full depth, other features active-only, general-plans active. Like vc-context-discovery but for plan artifacts.
Clarify intent before RIPER-5 phase delegation. Scores ambiguity (4 signals); generates structured multi-choice questions for Tier 2. Two-mode: SIMPLE and DEEP.
Create or update a product-discovery SPEC (requirements doc) for user review. Use when turning RESEARCH findings plus user intent into a reviewable requirements artifact before INNOVATE/PLAN.
Create or update implementation plans in the repo's SIMPLE or COMPLEX format. Use when turning an idea, PRD, or approved direction into a saved plan artifact.
Generate kickoff artifacts for a multi-phase program: umbrella plan, Program Goal Charter, session-goal block, per-phase plan stubs, and the 7-step per-phase inner loop reference.
Generate or update the project's authoritative repository context at process/context/all-context.md. Use when repo context is missing, stale, or contradicted by code.
Generate the post-EXECUTE closeout packet for a plan or phase. Includes archive-readiness classification, drift signal scoring, commit checkpoint recommendation, and move-on next-state recommendation.
Create polished frontend interfaces from designs/screenshots/videos. Use for web components, 3D experiences, replicating UI designs, quick prototypes, immersive interfaces, avoiding AI slop.
Use when a SPEC, INNOVATE, or VALIDATE (Layer 2) approach hinges on an unverified runtime/library/external mechanism: run a probe from the 8-family taxonomy and produce a VIABLE/NOT-VIABLE/INCONCLUSIVE VERDICT artifact.
Search library/framework documentation via llms.txt (context7.com). Use for API docs, GitHub repository analysis, technical documentation lookup, latest library features.
Debug systematically with root-cause analysis before fixes. Use for bugs, test failures, unexpected behavior, performance issues, CI failures, or system investigation.
Discover and load all relevant context for the current task. Lists feature group nested files with full paths, loads process/context/ files by domain routing. Called at the start of every agent session.
Loop: find gaps → fix → repeat until agents find no gaps or a metric goal is hit. Shared loop primitive for PVL, EVL, and standalone quality runs.
Emit and validate the provisional goal block for Autopilot Mode. Owns the 9-field format and resume detection from a pasted goal block.
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Audit active project plan files for staleness, completion, and routing truth. Use when cleaning up plans, reconciling active work, or archiving completed artifacts.
Audit project context routing, shared-skill discoverability, and Claude/Codex wiring. Use when context docs or skill surfaces move, split, or drift.
Evaluate 4 execution strategies (sequential, parallel-subagents, workflow, agent-team) for a phase or fan-out task. Outputs 7-signal score table, agent count math, cost guards, and strategy recommendation.
AI-optimized browser automation CLI with context-efficient snapshots. Use for long autonomous sessions, self-verifying workflows, video recording, and cloud browser testing (Browserbase).
Pre-build reality check. Scans GitHub, HN, npm, PyPI, and Product Hunt for existing competitors before you build. Use when starting a new project, evaluating a side project idea, or doing a build-vs-buy decision.
测试完成后的扫尾清理流程。在向某个工作区发送测试指令（如长篇文章生成、耗时任务）后，用此流程彻底清除测试痕迹，防止测试消息残留在会话上下文中持续消耗 token 额度。
把用户的可复用需求封装成标准化的 Agent-CLI 工具，并用 `myagents tool add` 注册进 MyAgents 工具注册表——注册后所有未来会话（builtin / Claude Code / Codex / Gemini 全 runtime）的 AI 都会在 system prompt 里自动发现它。触发场景：(1) 用户说「把 XX 封装成工具」「做成一个工具」「注册个工具」「写个 CLI」「以后能直接用」；(2) 用户描述一个会反复出现的自动化需求——文档/文件批量处理、调用某个云 API、用某个多模态大模型做图像/视频理解等，即使没说"工具"两个字；(3) 你发现自己第二次为同类需求写几乎一样的脚本——这时要主动提议把它升格为注册工具，不要等用户开口。反向边界：一次性任务就地解决、不铸工具；接入现成的 MCP server 用 `myagents mcp`，不归这里。
Autonomous task execution driven by task documents from /task-alignment (living under `~/.myagents/tasks/<taskId>/`). Reads task.md as the goal, decomposes work, delegates to subagents when appropriate, runs independent verification, and delivers results. Acts as a UserProxy Agent — the human's representative during autonomous execution. Use when the user dispatches a task from the 任务 panel or explicitly wants to start execution. Trigger phrases include '/task-implement', 'start the task', 'go ahead and implement', 'execute the plan', or when the user confirms alignment documents and says something like 'looks good, go'.
Alignment conversation starting from a 想法/idea. Co-decides with the user whether the idea should be acted on directly in the current session, or fixed into a formal Task for independent dispatch (one-off or recurring). Handles lightweight 'do it now while we talk', heavyweight 'define precisely, run later or on a schedule', and 'just help me think about this' — all on the same skill. Use when the user arrives via the 想法 panel's 'AI 讨论' button (parameter dictionary in the first message), or says 'let's think this through', 'help me plan this', 'I want to explore X', 'I have an idea', '/task-alignment'. Also use proactively when a user jumps into a complex task without defining scope or success criteria — pause, align, and help them pick the right vessel (this session vs. a task).
Methodology for writing or improving prompts and system prompts that drive any LLM. Use when authoring or revising a prompt for a model task — grouping, classification, extraction, generation, copywriting, labeling, agent instructions, prompt templates, skill instructions — to decide how much to constrain the model based on the task type (open-ended vs single-correct-answer) and write the most fitting instructions. Triggers: "write a prompt", "help me write or improve a prompt", "how should I change this prompt", "this prompt isn't working", "write instructions for the model", "prompt-writer". Not for: answering the user's question directly, or writing articles and documents meant for human readers (those are not prompts that drive a model).
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Create, repair, validate, visually QA, and package MyAgents/Codex-compatible animated pets and pet spritesheets from character art, generated images, company or prospect brand cues, or visual references. Use when a user wants a lightweight-worker desktop pet workflow, a non-pixel custom pet style, a prospect or company mascot pet, or a full 8x9 animated pet atlas with transparent unused cells, QA contact sheets, and pet.json packaging. This skill composes the installed $imagegen system skill for visual generation and uses bundled scripts for deterministic spritesheet assembly.
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Use when the user wants to audit SpriteKit game code for common issues.
Use when the user mentions in-app purchase review, IAP audit, StoreKit issues, purchase bugs, transaction problems, or subscription management.
Use when the user mentions Core Data review, schema migration, production crashes, or data safety checking.
Use when the user mentions concurrency checking, Swift 6 compliance, data race prevention, or async code review.
Use when the user mentions Codable review, JSON encoding/decoding issues, data serialization audit, or modernizing legacy code.
Use this agent to scan Swift code for camera, video, and audio capture issues including deprecated APIs, missing interruption handlers, threading violations, and permission anti-patterns.
Use when the user wants to triage a CORPUS of production crashes/hangs from an aggregator (Sentry, App Store Connect) — grouped, counted issues — rather than a single crash file.
Use when the user mentions flaky tests, tests that pass locally but fail in CI, race conditions in tests, or needs to diagnose WHY a specific test fails.
Use when the user mentions SwiftUI performance, janky scrolling, slow animations, or view update issues.
Use when the user mentions Swift performance audit, code optimization, or performance review.
Use when the user has a crash log (.
Use when implementing ANY Apple Intelligence or on-device AI feature. Covers Foundation Models, @Generable, LanguageModelSession, structured output, Tool protocol, iOS 26 AI integration.
Use when fixing or auditing ANY accessibility issue — VoiceOver, Dynamic Type, color contrast, touch targets, WCAG compliance, App Store accessibility review.
Help users manage their energy for sustained performance. Use when someone is feeling burned out, trying to find their zone of genius, scheduling for productivity, or identifying what drains vs energizes them.
Help users build and scale design systems. Use when someone is creating a component library, establishing design tokens, scaling brand consistency, or deciding when to invest in a design system.
Help users understand and build design engineering capabilities. Use when someone is creating a design engineering function, hiring design engineers, or bridging the gap between design and engineering teams.
Help users create compelling product visions. Use when someone is writing a vision statement, defining a long-term product direction, aligning teams on the future state, or distinguishing vision from strategy.