Skills
98,639 packaged capabilities and playbooks for agents. Install any of them with one command.
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Look up MCU peripheral registers, bit fields, reset values, and addresses (based on CMSIS SVD), with sources and evidence labels for every finding. Supports address reverse lookup; register value decoding and write-value calculation; bit-field quick reference and bitmaps; interrupt number and NVIC enable hints; CMSIS-style macro generation; cross-chip and peripheral-level comparisons; address range queries; SVD quality checks; local chip inventory and environment checks; Markdown table output; and parsed-result caching. Use when the user asks about a register, bit field, reset value, peripheral base address, or interrupt number; wants to decode or compute a register value to write, generate register macros, compare register definitions across devices, or find or verify SVD files on the machine. Keywords: 寄存器、位域、复位值、SVD、外设地址、寄存器值、掩码、位图、中断号、IRQn、NVIC、宏定义、对比、整体对比、质量检查、SVD 位置、芯片清单、读改写、USART1->CR1、register、bitfield、mask、reset value、decode、interrupt. Not for: pin multiplexing, manual chapter search, errata lookup, or verifying register writes in code (use embedded-register-verify).
Use when the user asks to fix, patch or upgrade a PHP vulnerability (e.g. 'Please fix my PHP vulnerability'), PHP2Shell, CVE-2026-109618 or CVE-2026-109619, or to find vulnerable PHP versions in Dockerfiles, distro packages, source builds, setup-php CI, Helm/IaC or Ansible/Chef/Puppet. Read-only inventory, approval gate, regression tests, focused PRs.
Align the current project's PRODUCT.md, DESIGN.md, GLOSSARY.md, and ARCHITECTURE.md after meaningful changes, and write the project Flow block on request. Use when the user says "update the docs", "sync the context", "keep the docs aligned", or "set up Flow here".
Install, wire up, and tune glimm—WebGL color-sweep page transitions—in a web project, with correct setup and candid guidance on where it fits. Use whenever the user wants page transitions, route-change animations, more lively or polished navigation, mentions glimm by name, or asks for “view transitions with some color” in a Next.js or React site, even if they never name a library. Also use when customizing an existing glimm install—changing palettes, matching brand colors, adjusting sweep timing or direction, or excluding specific links. Also use when a single in-app moment should get a sweep, such as a plan upgrade, a payment landing, a mode change, or a generated result.
Use when a human wants to create, hire, activate, or set up a new AI employee from a plain-English description, e.g. wanting something that writes a weekly recap or replies to guest messages. Drives the Hiring Dozal's multi-turn setup wizard (the hiring chat), relays its button questions (show me first or just send it) to the human, and hires the finished Dozal. Use it for any new-employee request. Load operate-ai-employees first for auth and base-URL setup.
Build from a written issue or task, collaborating with the user: read the issue and related open work, ground it in the code, clarify requirements and design with the user, then implement after they confirm the plan. Use when the user says let's build, build together, or asks to build from an issue or task description.
Use when a human wants to see, list, pause, reactivate, rename or inspect their existing AI employees (config, status active/paused/draft), or manage connected apps, schedules, handoffs, members and invitations, usage and bills, e.g. what employees do I have, pause the recap bot, how did last Friday go. Not for running one now (run-ai-employee), a task's output, approval or logs (monitor-and-review-ai-employee-work), or creating one (hire-ai-employee). Load operate-ai-employees first.
Makes a 3D map animation for a reel. A clean white city with parks and a river, a coloured route that draws itself from start to destination while the camera follows, a red pin that drops with a bounce, an ETA card that counts down and a destination card. Use when the user runs /map-route or asks for an animated route, a "from A to B" map, a journey or a commute animation.
Use when a human asks whether an AI employee task is done, wants to see its output, or wants to approve or reject its work: is the recap ready, show me what it wrote, approve it, reject and shorten it, why did that run go wrong, show its logs, watch it live. Reads task status via phase_category, drives the approve/reject decision, and reads run logs, activity and live status. Load operate-ai-employees first for auth setup.
Use when a human asks you to operate their AI Employee platform account: hiring an employee, running one, checking on a task, approving or rejecting work, listing or pausing employees, or managing connections, schedules, handoffs, members, usage, bills, activity and run logs. Load this first to route between the four job skills. The umbrella skill: dze_ API key auth and its level, the production base URL, tenant resolution, the async model, and person-only steps.
Use the public `orca` CLI to operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, worktree comments, and the browser embedded inside the Orca app. Use when the user says "$orca-cli", "use orca cli", "Orca worktree", "child worktree", "cardStatus", "spawn codex/claude in a worktree", "read/wait/send Orca terminal", "terminal send", "full handoff", "handover", "give this to another agent", "another worktree", "Orca browser", "orca artifacts", "share HTML/Markdown", "public artifact link", or "control the browser inside Orca". Prefer this over raw `git worktree`, ad hoc PTYs, Playwright, or Computer Use when the task touches Orca-managed state. Use Computer Use for browser windows, webviews, or desktop UI outside Orca's embedded browser.
Reviews a git diff through reviewer subagents on each lens (big picture, detail), then checks and triages the findings and fixes clear problems. Use when the user asks for a code review, PR review, sanity check on changes, or reviews with specific or multiple models.
Find where an agent pipeline can swap frontier models for cheaper ones without losing quality. Drive the installed rightmodeler TypeScript CLI through its resumable pipeline, interpret its machine protocol, present per-family verdicts, and open a reviewed model-swap pull request when the evidence supports one. Use when the user wants to cut model spend, right-size models per task, benchmark cheaper model substitutions, or analyze an agentic pipeline for cost optimization.
Use when a human wants to run, trigger, start, or test-run an existing AI employee, e.g. run the recap bot now, kick off the guest responder, or run it once per topic from a list of inputs. Triggers the employee, can dry-run to list required inputs, and hands back task handles without blocking. Load operate-ai-employees first for auth; use monitor-and-review-ai-employee-work to check the result.
Companion CLIs for Runpod workflows — HuggingFace, GitHub, Docker, and AWS. Use runpod-build-template for complete template project authoring, and the ComfyUI model-repair guide in runpod-templates for imported workflows lacking model download metadata
Initialize a new project from flow-next or flow-mono with verified setup, renamed identity, and an independent initial Git commit, while explore shapes the product. Use when the user says "start a Next.js project", "start a new project", or "start a monorepo".
Sets stat budget curves per tier, zone, or level with per-stat costs and multipliers (rank, rarity, slot), checks every item and enemy against its expected budget, drafts new items and enemies from archetypes that split the budget across stats, and suggests stat costs from the combat formula. Use when designing tier or zone progression, adding items or enemies so they fit a curve, finding over- or under-budget entries, or deciding how much one stat point is worth, in any genre and engine.
Sync a repository's documentation with what its code actually says: versions, dependencies, commands, project structure and feature claims in README.md, CLAUDE.md, AGENTS.md and docs/, each verified against the manifest, lockfile, scripts and files before it is written, with human prose, tone and section order left as they are. Use when: (1) a dependency, framework or runtime version changed and the docs still name the old one, (2) scripts, Makefile targets or the directory layout changed, (3) a feature was added, renamed or removed and the docs may no longer match, (4) an agent instruction file tells agents to run a command that may not exist, (5) the user mentions: sync docs, update the README, docs are stale, docs out of date, outdated version in README, fix CLAUDE.md, AGENTS.md, package.json, lockfile, pyproject.toml, Cargo.toml, go.mod, "npm run lint" missing script, "docs: sync documentation with codebase state". Carries the ground-truth table per ecosystem, the rule for declared range against resolved version, the flag-don't-guess rule for unverifiable feature claims, and a three-part report of changed, verified and flagged. Works on any project type, Node, Rust, Python, Go or a repository with no manifest at all; the repository's own manifests and files are the seam. Not a changelog or version bump tool, not a doc writer for missing files, and not an audit of the code against the docs
Wire Redis Agent Memory into an existing agent with redis-iris so it remembers users across sessions. Asks five questions (what to remember, what to share, what never to keep, how long to keep it, whether users can be forgotten), writes the answers to service.yaml, provisions the service from the terminal with `iris agent-memory service create`, then wires recall and write-back into the agent's turn in any framework. Use when the agent forgets users, uses InMemoryStore or a checkpointer only, keeps memory in a dict or local DB, or the user asks for long-term memory, recall, or right-to-erasure.
Add Redis Iris Agent Playbooks to an existing agent. Use when approved answers or repeatable procedures live in prompts, document retrieval, or a semantic response cache. Connect or provision a Playbook, publish FAQs and Skills, and verify the first application turn with optional review proposals.
Put the MCP servers an existing agent uses behind one Redis Iris Context Gateway with redis-iris, so the agent connects to one MCP endpoint with one agent key instead of configuring each server and its secret. Asks whether the Gateway is in Redis Cloud or self-managed, then five questions (which MCP servers and secrets the agent has today, how many tools it sees, which read-only tools may be cached, which tools return large results, whether the framework speaks MCP itself). Self-managed, it provisions the Gateway and its Upstreams from the terminal with `iris context-gateway` and sets cache and compression policies. In Redis Cloud the developer creates them in the console. Then it points the agent at the Gateway in any framework and verifies it with `tools list` and `tools call`. Use when the agent configures several MCP servers one by one, carries one secret per server, loads too many tool schemas into the prompt, or the user asks for Context Gateway, one MCP endpoint, tool caching, or result handles.
Replace an existing agent's hand-written routing (keyword if/else chains, regex, or an LLM call that only picks a route) with a Redis Iris Intent Router through redis-iris, so a message maps to a defined intent and its dispatch target by example phrasings and a score threshold. Asks five questions (what the routes are today, a few real phrasings per route, what each route dispatches to, what happens when nothing matches, which embedding model), writes the answers to intents.yaml, provisions the Router from the terminal with `iris intent-router service create`, loads the intents with `iris intent-router intents add`, then wires `router.match()` where the agent decides today. Use when the agent routes with keywords or an extra LLM call, or the user asks for intent detection, intent routing, or semantic routing.
Add Redis Iris Lens tracing to an existing AI agent through redis-iris, so every agent turn is one trace in the Lens UI with its LLM calls, tokens, tool calls, memory and document retrievals, and which of them entered the prompt. Detects how the agent runs (LangGraph or ADK attach, workflows.turn, another framework, or a plain loop), installs redis-iris[llm-tracing], calls lens.init(agent_id=...), opens one lens.turn() per turn where Iris does not open it already, and covers coexistence with an OpenTelemetry setup or LLM instrumentation the app already has. Use when the user asks to add Lens, tracing, observability, or per-turn traces to their agent, or asks why their Lens traces are missing.
Add Redis Iris to an existing AI agent in any framework (LangGraph, LangChain, OpenAI Agents SDK, Google ADK, Pydantic AI, CrewAI, or a plain loop). Use when the user asks to add Iris, Redis Agent Memory, Agent Playbooks, Context Gateway, Intent Router, or Lens to their agent, or asks which Iris product their agent needs. Detects the framework and routes to the product skill.
Make videos in pure code (mp4, 16:9 and 9:16): narrated explainers with captions and music, and promo / launch videos for websites, apps and software with beat-synced music and sound effects. The user picks only the style: whiteboard, risograph, ink-wash (水墨), launch motion, origami, pop art, op art, Matisse cut-outs, ASCII art, shadow puppets (皮影戏), Bauhaus, pixel art, chat log, toy 3D, old newsreel, film-room breakdown, suspense mystery, isometric miniature world, movie trailer, news clippings, manga (日漫), animated map. Use for science / concept explainers, problem walkthroughs, vocabulary, history, places and maps, how-tos, news / data, and product promos from a URL. Use when the user asks for an "explainer video", "whiteboard animation", "a promo / launch video for this website / app", or in Chinese "做个科普视频" "把这道题做成讲解视频" "白板动画" "讲个历史故事的视频" "给这个网站 / App 做个宣传片" "产品发布视频", or names one of the styles (孔版印刷, 水墨, 折纸, 波普, 欧普, 剪纸, 字符画, 皮影戏, 包豪斯, 像素, 聊天记录, 玩具三维, 老新闻片, 录像分析, 悬疑探秘, 等距微缩世界, 电影预告片, 剪报解说, 日漫, 地图动画).
100 questions about Binance Agentic Wallet. Uses Chinese Q&A to guide an agent in understanding Binance on-chain wallets, choosing official baw routes, checking transaction approvals and risk boundaries, and designing constrained automation extensions. Covers meme coin risks and honeypot detection, rug pull prevention, and verifying smart-money signals. For product questions, troubleshooting, trading strategy constraints, meme coin risk control, and wallet workflows; does not replace the official wallet skill and is not for placing Binance exchange futures orders.
Runs a local (LAN) and instant public host (Zero-Config Tunneling via Cloudflare/Localtunnel) for simultaneous multiplayer educational quizzes and board games in a classroom, without router configuration or a paid domain. Includes an automatic gradebook (real-time grade ledger), instant QR Codes for student smartphones, and export of grade summaries to Excel/JSON.
Assess, set up, and verify how an AI development environment uses an organization's actual design system. Use when someone wants to keep AI on their system's rails, connect system knowledge and tools to coding agents, establish reuse and gap policies, or check whether those guardrails work. Focuses on the workflow and its evidence; ordinary page building or a full system inspection alone does not require this skill.
This skill should be used when the user is building electrobun features for the milady-ai/milady project, submitting PRs to milady that will be reviewed by the agent-review system, asks about "electrobun milady", "milady electrobun PR", trust tiers, Biome compliance, or the release-electrobun.yml workflow.
Researches evidence unavailable locally: external documentation, public repositories at specific refs, archives, and published dependency artifacts (JARs, source JARs, POMs, dependency graphs), reusing a persistent cache of Git clones and Maven artifacts. Use for library docs, upstream source and history, dependency source or metadata, or any question needing external evidence
Use when work arrives at the software factory at any lifecycle stage — a new request, an informational question, a CI failure on a delivered PR, review findings, a merge notification, or a reply to a factory question — and must be routed to the right factory skill, human gate, or follow-up context.
Use when the user asks to fix, patch or upgrade a self-managed GitLab vulnerability (e.g. 'Please fix my GitLab vulnerability'), CVE-2026-110264 or the GitLab 19.5.1 critical patch release, across Omnibus, Docker, Helm chart and IaC deployments. Read-only inventory, approval gate, post-deploy checklist, focused PRs.
Humanize prose or draft it from supplied facts when explicitly asked to "humanize", "de-AI", "remove AI-sounding language", or write in a human voice. Simplify by cutting filler, stale content, and needless complexity. Confirm scope each run and offer four voice presets.
Lays out each level as an ordered list of encounters (rooms, waves, packs, time spans), scores every encounter's threat from the same enemy stats used by combat and stat budgets, compares it with a threat budget curve per stage and role, shows single-target kill time against stage length, and cross-checks placed enemies against combat stages and kill counts against the economy. Also lays out pacing: enemy-less stretches, encounter times against economy stage lengths and purchases, accumulated load between rest points and rewards, and runs of strong encounters. Use when composing levels, waves or rooms, sizing encounters, placing rests and rewards, or checking that a level agrees with combat and economy assumptions, in any genre and engine.
Use when the user asks about MLX serving, mlx_lm.server, oMLX, Apple Silicon LLM serving, or running a local LLM on a Mac, and when troubleshooting a model that fails to load, OOM during load or inference, a server that hangs or crashes at batch>1, tool calls returned as plaintext content, a throughput regression, or the choice between mlx-lm and oMLX. Also covers oMLX feature-flag tuning (turboquant_kv, dflash, MTP, specprefill, thinking_budget, max-concurrent-requests, force_sampling), the OptiQ proxy for models exceeding RAM, Llama-4 ChunkedKVCache batch handling, Llama-3 tool-call JSON format, and bench-driven validation of serving configs. Apple Silicon M-series only, not cloud LLM hosting, non-MLX backends such as llama.cpp, Ollama and vLLM, or model training.
Richtet alles ein, um mit Claude Code (oder Codex) Motion-Graphics-Videos per Code zu bauen, und hilft danach, einen guten Video-Prompt zu schreiben. Fragt, ob HyperFrames, Remotion, Tesseract oder nichts installiert werden soll, verbindet auf Wunsch Higgsfield für Bilder, Videos, Stimmen und Musik und führt dann durch ein kurzes Interview (Länge, Format, Ziel, Branding, Referenz, Erfolgskriterium). Nutze diesen Skill, sobald jemand „Motion Fabrik“ sagt, den Link zu diesem Repo schickt, mit Claude Videos, Erklärvideos, Werbespots, Shorts oder Motion Graphics machen will oder HyperFrames, Remotion, Tesseract oder Higgsfield einrichten möchte. Also use it in English when someone wants to set up AI video creation with code.
Build NHCX (India's National Health Claims Exchange) payer communication into an existing HMIS: receiving payer queries, notifications and notes on a pre-authorisation or claim, replying with text and documents, and acknowledging notifications, with the embedded NHCX gateway it needs. Use when a hospital system must answer insurer or PMJAY queries over NHCX.
Build NHCX (India's National Health Claims Exchange) payment notices into an existing HMIS: receiving the payer's payment notice and reconciliation breakdown, matching it to the claim, and acknowledging it, with the embedded NHCX gateway it needs. Use when a hospital system must record insurer or PMJAY payments arriving over NHCX.
Build NHCX (India's National Health Claims Exchange) claim reprocess and balance release into an existing HMIS: asking the payer to look again at a decided claim, asking for the unpaid balance of a partly paid claim, and applying the payer's answers, with the embedded NHCX gateway it needs. Use when a hospital system must contest a claim decision or recover a withheld balance over NHCX.
Write the brains of an app's onboarding or web2app funnel — every question, its answers, the order, the branching, the statements and explainer screens between them, image descriptions and the meaning of each transition — so the flow primes the user to finish and to buy, and collects the answers that predict revenue (from ARPU data across 411 apps). Starts from an App Store URL (and the website, if there is one), researches the app, and hands over a spec that ships fastest as a Superwall surface (the superwall framework: one page per screen, answers sent as user attributes). Design, UI and code agnostic; no paywall, pricing or checkout. Use when the user asks \"what should we ask in onboarding\", \"write our onboarding questions\", \"design a quiz/survey funnel\", \"which questions predict who pays\", \"make our onboarding convert\", or wants inspiration for onboarding screens.
Operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and Orca's embedded browser through the `orca` CLI. Use when the user says "$orca-cli", "Orca worktree", "child worktree", "spawn codex/claude in a worktree", "read/wait/send Orca terminal", "handoff" / "handover" / "give this to another agent", "Orca browser", "orca artifacts", or "share skills". Prefer it over raw git worktree, ad hoc PTYs, or Computer Use when Orca state is involved. Use Computer Use only when a visible window needs GUI control that a CLI, filesystem, or API cannot do.
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.