Skills
99,064 packaged capabilities and playbooks for agents. Install any of them with one command.
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End-to-end QA entry: "test this feature fully" orchestrates requirements, strategy, cases, review, execution, bugs, regression, and report; resumable. Single-stage tasks use their stage skill. End-to-end testing's only entry point: when asked "test this requirement/feature," orchestrates requirements → strategy → cases → review → execution → bug analysis → regression → report, persists progress for resuming; single-stage requests use the corresponding stage skill.
World-class systematic trading research - backtesting, alpha generation, factor models, statistical arbitrage. Transform hypotheses into edges. Use when "backtest, alpha, factor model, statistical arbitrage, quant research, systematic trading, mean reversion, momentum strategy, regime detection, walk forward, " mentioned.
R visual creation and ggplot2 patterns for PBIR reports. Automatically invoke when the user mentions "R visual", "ggplot2", "ggplot in Power BI", or asks to "create an R visual", "add an R chart", "write an R visual script", "inject an R script into Power BI".
RabbitMQ message broker with AMQP protocol. Covers exchanges, queues, bindings, and messaging patterns. Use for reliable message delivery and complex routing scenarios. USE WHEN: user mentions "rabbitmq", "amqp", "exchanges", "routing patterns", "topic exchange", "fanout", asks about "message routing", "work queues", "request/reply", "flexible routing" DO NOT USE FOR: high-throughput streaming - use `kafka` or `pulsar`; cloud-native - use `nats`; AWS-native - use `sqs`; JMS required - use `activemq`; simple pub/sub - use `redis-pubsub`
Convert PRDs to prd.json format for the Ralph autonomous agent system. Use when you have an existing PRD and need to convert it to Ralph's JSON format. Triggers on: convert this prd, turn this into ralph format, create prd.json from this, ralph json.
Use when writing, reviewing, or refactoring React or TanStack Router code for folder structure, naming, colocation, encapsulation, or layer separation. Triggers on React folder structure, naming rules, feature-module boundaries, or architecture decisions.
Build custom React UIs with LiveKit hooks from @livekit/components-react. Use this skill when you need low-level control over agent state, participants, tracks, chat, and data channels. For pre-built UI components, use the livekit-agents-ui skill instead.
Use when building interactive 2D or 3D graph or network visualizations in React with the reagraph library (GraphCanvas, node-link diagrams, force-directed layouts). Covers data shapes (GraphNode, GraphEdge), 16 layout algorithms, selection, lasso, camera controls, sizing strategies, and theming.
Reconstruct your recent working context from your own chat history, live state, and the shared record (user reports, prior fixes, incidents), then hand back a tight current-state brief. Use for “recall my work on X”, “catch me up”, “what have I been working on”, “where did I leave off”, before starting or resuming work.
Use this skill when the user wants to create Xiaohongshu (小红书/RedBook) posts. Trigger phrases: 小红书创作, create redbook, 小红书, 红书, 笔记创作, 帖子创作. The user will provide images, videos, or text content. This skill analyzes the content, searches for trending Xiaohongshu posts as reference, generates post title and copy, auto-edits videos (merge, subtitles, BGM, narration), attempts auto-upload to Xiaohongshu creator platform, and generates a local preview HTML file.
Reddit account cultivation for indie developers. Uses AppleScript to control real Chrome — undetectable by anti-bot systems. Checks karma, finds rising posts, drafts comments, and posts directly. Triggers on "/reddit-cultivate", "check my reddit", "reddit maintenance", "find reddit opportunities", "build reddit karma".
Run a bounded red-teaming autoresearch loop to generate LLM guardrail training data. You (the agent running the skill) are the attacker and the judge: you craft attacks and label every response. The only model the harness calls is the target under test, over any OpenAI-compatible API (OpenRouter, Moonshot/Kimi, Fireworks, Ubicloud, OpenAI, or custom), with its key in `.red-team/.env`. Every attempt (pass and fail) is written to JSONL ready for fine-tuning guardrails in Llama Guard format. Use when asked to red-team or stress-test an LLM for harmful content, jailbreaks, prompt injection, or backdoor/trigger behavior; to build a safe/unsafe dataset; or to mine novel attacks at scale. Complements the hacker skill (code/infra exploitability): this skill targets model behavior and produces data.
Comprehensive skill for 89 refactoring techniques and 22 code smells with practical examples. Use when the user asks to refactor code, detect code smells, improve code quality, reduce complexity, or clean up technical debt. Covers composing methods, moving features between objects, organizing data, simplifying conditionals and method calls, dealing with generalization, and detecting smells across bloaters, OO abusers, change preventers, dispensables, and couplers with before/after comparisons and step-by-step mechanics.
When the user wants to design, launch, or optimize a referral or affiliate program. Use when they mention 'referral program,' 'affiliate program,' 'word of mouth,' 'refer a friend,' 'incentive program,' 'customer referrals,' 'brand ambassador,' 'partner program,' 'referral link,' or 'growth through referrals.' Covers program mechanics, incentive design, and optimization — not just the idea of referrals but the actual system.
Record non-obvious decisions behind behavior others rely on — a bug fix, a requirement change, or an architecture choice — as Agent Notes bound to regression tests so later AI-driven changes cannot silently undo them. Use when fixing a bug a future change could plausibly revert, changing a requirement, reviewing such a change, verifying a notes tree, or asking when to write a postmortem. Do not use for routine fixes a test and commit message already explain, feature docs, or changelogs.
Discover and install related skills from inference.sh skill registry. Helps find complementary skills for your AI workflow. Use for: skill discovery, workflow expansion, capability exploration. Triggers: related skills, find skills, skill discovery, complementary skills, expand workflow, more capabilities, similar skills, skill suggestions
Use this skill when releasing a new version of a GitHub project, covering the full process of version number management, CHANGELOG syncing, writing release notes, creating tags, CI build monitoring, release verification, and history cleanup. Applies to any GitHub-based software project such as desktop apps, CLI tools, web apps, and libraries/SDKs. Triggers when the user mentions "发布" (release), "release", "打 tag" (tagging), "新版本" (new version), "更新版本号" (updating the version number), "写 release notes" (writing release notes), "发布失败了" (release failed), "CI 挂了" (CI is down), "Actions 配额告急" (Actions quota running low), "短时间内多次发版" (multiple releases in a short time), "monorepo", "批量打包" (batch packaging), "多 skill 发布" (multi-skill releases), "skill zip", "专家套件 zip" (expert suite zip). Also used to reject the anti-pattern of treating a release as a CI verification mechanism ("打 tag 看一下", just tag it and see). Do not use for non-GitHub projects (such as pure GitLab / Gitea projects) or manual release scenarios that require no CI.
Translate an existing Remotion (React-based) video composition into a FrameVideo HTML composition. Use ONLY when the user explicitly asks to port, convert, migrate, translate, or rewrite a Remotion composition as FrameVideo (e.g. "port my Remotion project to FrameVideo"). Do NOT use when (a) authoring a NEW FrameVideo composition (even if A/B-testing a Remotion video); (b) Remotion is mentioned in passing; (c) Remotion code is shared as reference, not for translation; (d) the user wants "the same video as my Remotion one" without explicitly asking to migrate the source — treat as a fresh FrameVideo build. When in doubt, default to the `framevideo` skill. Detects unsupported patterns (useState, useEffect side effects, async calculateMetadata, third-party React component libraries, `@remotion/lambda`) and recommends the runtime interop escape hatch instead of a lossy translation.
Runs a full forensic audit of outbound REPLIES (positive responses, objections, ghosts) for an agency client and writes the result as a structured page in an established 9-section format. Use whenever someone shares a batch of outreach replies and wants them analyzed — "analyze these replies", "audit the replies", "what's wrong with our responses", "why are leads ghosting", "analyze positive responses" — or links a Google Sheet / CSV / Notion table / mentions a LinkedIn-automation or cold-email campaign of replies. Accepts ANY source (Google Sheet, CSV, Notion table, raw pasted text, or an outreach-platform MCP). Always diagnoses ROOT CAUSE (targeting/data problem vs SDR/pitch problem), classifies every reply, extracts gold-standard reply templates from the data, and ships a 9-section audit. NOT for benchmarking a single metric ("is my reply rate good") — that's outbound-analyst.
Repo-local workflow skill for large coding tasks. Initializes .agent/tasks/TASK_ID artifacts, installs project-scoped Codex and Claude subagents, updates AGENTS.md plus the repo's Claude guide file with the workflow, and runs a spec-freeze → build → evidence → verify → fix loop with fresh-session verification.
Use when the user explicitly asks to "extract a review topic from a file/image/webpage/description", "generate a structured output of topic + keywords + core questions", or requests the old name get-review-theme skill. Supports multiple input sources such as files (PDF/Word/Markdown/Tex), folders, images, natural language descriptions, and webpage URLs; automatically identifies the input type and extracts content, producing structured output directly usable by research-literature-review and other literature review skills.
Use as the follow-up to brainstorm-beagle when a spec has an Open Questions section (or quietly carries latent gaps) that need closing before planning or implementation can begin. Triggers on: \"resolve the open questions\", \"close the gaps in this spec\", \"research the open items\", \"finalize my spec\", \"make this spec implementation-ready\", \"answer the TBDs\". Also triggers whenever the user points at a brainstorm-beagle spec and asks for research, proposals, or answers to unresolved items. Orchestrates parallel research subagents when available (falls back to inline sequential research otherwise), proposes answers one at a time for user approval, then rewrites the spec in place so it arrives at planning with no known gaps. Does NOT write code, design implementation, or create plans — it only produces a complete spec.
Generate a structured response-to-referees document from a referee report and the revised manuscript. Maps each referee comment to the specific revision, classifies coverage (addressed / partially / deferred / disagreement), and drafts polite but firm responses. Use during the R&R (revise-and-resubmit) stage of paper revision.
Guides authorized reverse engineering—static and dynamic binary analysis, disassembly and decompilation workflows, protocol and file-format reversing, defensive malware analysis (behavior, IOCs, YARA ideas), firmware RE, patch diffing, and vulnerability research documentation. Emphasizes written authorization, export-control awareness, and no assistance bypassing protections on unowned or unlicensed software. Use for reverse engineering, disassemble, decompile, Ghidra/IDA-style workflows, binary analysis, malware analysis for defense, firmware RE, patch analysis—not disk imaging forensics (digital-forensics-analyst), live incident command (incident-responder), SOC alert triage (soc-analyst), authorized pentest exploitation (penetration-tester), LLM red team (ai-redteam), or CI pipeline gates (devsecops).
Reviews PostHog's warehouse suggestions with a person and acts on them. Explains why a view should be materialized, unmaterialized, certified or deprecated using the numbers in the suggestion, changes a materialize suggestion's refresh mode, keys or interval when the person wants something else, then accepts, dismisses or assigns it. Use when the user asks what PostHog suggests for their data warehouse, whether to materialize a view, why a view should refresh incrementally or every few hours, or wants to accept, change, dismiss or assign a warehouse suggestion.
Reproduces, diagnoses, and fixes a defect in a Backstage plugin inside the rhdh-plugins or community-plugins repositories: resolve the report from a RHIDP, RHDHBUGS, RHDHPLAN or RHDHSUPP Jira key, a GitHub issue, or plain prose; locate the owning workspace; write a throwaway reproduction test that fails for the stated reason; record before and after Playwright video evidence for a UI defect; then apply the smallest fix and verify it. Use for "fix this plugin bug", "reproduce RHIDP-1234", a plugin that renders the wrong thing, or a regression that needs evidence before and after.
Transform a prioritised initiative list into a compelling strategic roadmap narrative. Use when asked to write a roadmap narrative, explain the product roadmap to non-technical stakeholders, connect roadmap items to company goals, or produce an exec-shareable roadmap story. Produces a themed narrative with strategic context, quarter progression arc, an executive summary, and a 'what's not on the roadmap' section.
Author Roblox character animations (R15 or R6) headlessly in Blender from a JSON pose spec, verify them by rendering contact sheets and GIFs and match a reference pose. Use this whenever the user wants to make, generate, or automate a Roblox animation — a punch, swing, emote, idle, walk, death, tool action — or wants an animation built to match a reference pose, or asks to animate a .blend rig without opening Blender's UI. Also trigger when a Roblox animation "comes apart", limbs detach, a rig poses correctly in script but renders wrong, limbs pop or flip between frames, or a pose looks nothing like the reference.
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.