Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Meta-gateway for experience feedback. Collects from three sources (session reviews/article scans/manual input) → distills and anonymizes → categorizes → handles in three ways (publish an issue/store locally/do not store). Trigger terms: feedback experience, save experience, submit experience, experience review, capture lessons, lessons learned
Use when driving or inspecting Roblox Studio through the Studio MCP tools — running Luau in Studio, play/stop, screenshots, reading/searching the datamodel or scripts, generating meshes/materials/models, inserting assets, moving the character, or when the MCP link is dead (no Roblox tools show up). A capability menu + gotchas + connection troubleshooting.
Autonomous browser control with Cowork-style skill recording. Use when user requests "control my screen", "record workflow", "verify Lovable", "test scrapers", "debug DOM", "autonomous testing", or any browser automation task. Uses CDP + Accessibility Tree for 10x faster, 100% reliable element targeting. NO screenshots.
AI-agent commit protocol that establishes a Shadscan baseline and reruns the audit immediately before every agent-created commit without installing dependencies or configuring Git. Use when an agent is asked to commit work in a React shadcn project, prevent UI regressions during an agent task, or run Shadscan before commits.
Universal consolidation & audit skill for Claude Code skills. Analyzes project state, detects redundancies, and safely manages skills with backup, confirmations, and rollback capabilities. Never assumes without verifying actual code and usage patterns.
The central console for Xiaohongshu content creation. Determines which stage the user is currently stuck at, routes to the corresponding specialized skill, and strings multi-step workflows together for execution. Triggers when the user says "do Xiaohongshu", "help me run Xiaohongshu", "walk through Xiaohongshu from start to finish", "I want to start doing Xiaohongshu", "the full Xiaohongshu process", "I don't know where to start", "help me with Xiaohongshu", or raises needs spanning multiple stages at once (e.g. "help me come up with topics and write them out with images ready"). For needs limited to a single stage, use the corresponding specialized skill directly rather than this skill.
Universal team coordination skill with dynamic role generation. Uses team-worker agent architecture with role-spec files. Only coordinator is built-in -- all worker roles are generated at runtime as role-specs and spawned via team-worker agent. Beat/cadence model for orchestration. Triggers on "Team Coordinate ".
Break a plan, spec, or conversation into tracer-bullet tickets assigned to autonomous or attended lanes, with pre-agreed test seams, blocking edges, and overlap-safe execution frontiers. Use when preparing work for unattended coding agents as well as human-guided implementation.
Break a plan, spec, or the current conversation into a set of tracer-bullet tickets, each declaring its blocking edges, published to the configured tracker (edges as text in one file per ticket locally, or native blocking links on a real tracker).
Cut token usage across the whole session without losing correctness or substance — concise user replies, lean tests/docs/plans/comments, tight code, disciplined context and tool use, and cheap sub-agent delegation. Load when the user wants less verbosity, lower cost, fewer tokens, or a "token diet" / "lean mode"; also triggered by /token-diet or "token diet on".
Detect whether Claude Code evolution hooks are installed/enabled, and print a copy-paste fix. Use when you expect runs/evolution artifacts but nothing is being written. Triggers: hooks, evolution, runs/evolution, settings.json, PreToolUse, PostToolUse.
Submit feedback about the TRES Claude plugin — bug reports, feature requests, skill improvements, new skill ideas, MCP issues, workflow friction, or positive feedback about what's working well. Use this skill whenever the user wants to share any kind of feedback about the plugin, its skills, or the TRES MCP. Trigger phrases include: "submit feedback", "report a bug", "request a feature", "suggest an improvement", "I have an idea", "something is broken", "the skill isn't working", "I love this skill", "this worked great", "the MCP is returning wrong data", "the flow is confusing", "can you fix the skill", "file a skill request", "give feedback", "this could be better". Also trigger proactively when the conversation appears to be wrapping up — e.g. the user says "thanks", "that's all", "looks good", "we're done", "nothing else", or any other signal that the task is complete. In that case, offer a brief feedback prompt instead of launching the full guided flow. Do NOT trigger for general questions about how a skill works — only when the user wants to submit feedback or the conversation is ending.
Use the AskUserQuestion tool (the same multiple-choice mechanism plan mode uses) to get clarity from the user. Each question is multiple-choice plus a free-format "Other" escape. Ask in small batches — up to 4 questions per call (the tool's max), more if needed across calls. Use when scoping work, resolving ambiguity, or surfacing answers a research pass already raised. Triggers: `/turn-based`, `ask me`, `clarify`, `discovery mode`, `interview me`.
Run a review-gated Twitter/X agent for normal daily growth: initialize strategy, create daily plans, present strategy/plan/task/draft reviews, inspect scheduler state, and summarize outcomes through ThreadWave. Use for Twitter agent, X agent, daily Twitter automation, account growth, content planning, tweet scheduling, draft review, or normal daily run requests.
Use when the user or agent asks to delegate work to another model/provider such as OpenCode, Claude Code, MiniMax, Gemini, or an independent reviewer; when the goal is token savings, batch or multi-file work, a task expected to run longer than 5 minutes, independent verification or security audit, model-tier routing, provider/model identity checks, OpenCode default/headless model checks, or choosing between MCP tools, an independent Claude Code session, OpenCode provider mode, and OpenCode default/internal-model mode.
Fact-check LLM-drafted technical content against primary sources — source hierarchy, programmatic existence probes for tool and plugin names, class-before-instance error triage, the truncated-negative trap, adversarial re-checking, and the verified-stamp discipline. Use when auditing a SKILL.md for factual errors, before stamping a skill verified, when a command or artifact claim needs confirming against upstream documentation, or when reviewing any drafted content whose specifics were written from model memory.
Review repository-native specs, code diffs, PRs, docs, workflow artifacts, or skill changes as a shared readiness gate. Use when deciding whether a spec can move to planning, whether a code change is safe to continue or commit, whether a workflow artifact is coherent, or whether review findings should block the next step.
Create or update compact repository-native feature specs from rough requirements, issues, notes, product discussions, prototypes, screenshots, existing drafts, or substantial skill/workflow/protocol optimization requests. Use when turning ambiguous product or workflow intent into a concise spec under the repository's feature artifact root, including repository context discovery, active clarification, shared terminology, goal, rules, acceptance evidence, and open questions before review or development planning.
Use when you need to clarify requirements and route to the right workflow (idea → executable input). Project intake + routing: help the user brainstorm and clarify intent, persist goal/context artifacts, then dispatch to the right workflow or step skill. Default route is workflow-ship-faster (Next.js 16.1.1) for idea/prototype→launch. Triggers: project kickoff, requirements clarification, brainstorm, ideas, discovery, intake.
Integrate LangChain applications with You.com tools (web search, content extraction, retrieval) in TypeScript or Python. Use when developer mentions LangChain, LangChain.js, LangChain Python, createAgent, initChatModel, DynamicStructuredTool, langchain-youdotcom, YouRetriever, YouSearchTool, YouContentsTool, or You.com integration with LangChain.
Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 9-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.
Expose an AG2 `Agent` over the Agent-to-Agent (A2A) protocol so any A2A-compliant client (or another AG2 agent) can call it across process/host boundaries. Wrap the agent with `A2AServer(agent)` and serve it via `build_jsonrpc(...)`, `build_rest(...)`, or `build_grpc(...)` — each returns a ready-to-serve ASGI/gRPC object that publishes an A2A `AgentCard` (built by `build_card`) at `/.well-known/agent-card.json`. Use when you want an AG2 agent reachable as a standard networked A2A service — interop with non-AG2 A2A clients, multi-transport (JSON-RPC / REST / gRPC) endpoints, declared auth schemes, push notifications, or multi-tenancy. To consume a remote A2A agent from AG2 instead, point an `Agent` at `A2AConfig(card_url=...)`. Covers `A2AConfig`, `A2AServer`, `build_card`, security schemes (`bearer_scheme`/`api_key_scheme`/`oauth2_scheme`/`require`), and the in-process `testing.py` helpers (`make_test_client_factory`, `make_test_rest_client_factory`).
Battle-tested engineering principles for AI coding agents. Covers plan-first workflow, subagent delegation, self-improvement loops, verification gates, elegant solutions, autonomous bug fixing, and structured task management. Use when configuring agent behavior, writing AGENTS.md files, or improving agent reliability and code quality.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.
