Skills
100,344 packaged capabilities and playbooks for agents. Install any of them with one command.
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When the user wants to schedule production operations, optimize manufacturing schedules, balance workloads, or sequence jobs. Also use when the user mentions "job shop scheduling," "flow shop scheduling," "dispatching rules," "sequence optimization," "machine scheduling," "MRP," "finite capacity scheduling," "production planning," or "shop floor scheduling." For capacity planning, see capacity-planning. For master scheduling, see master-production-scheduling.
Load PROACTIVELY when starting work on an unfamiliar codebase or setting up a new project. Use when user says "help me understand this codebase", "onboard me", "what does this project do", "set up my environment", or "map the architecture". Covers codebase structure analysis, architecture mapping, dependency auditing, convention and pattern detection, developer environment setup, and documentation of findings for rapid productive contribution.
Expert skill for generating interactive HTML product prototypes. Use when the user needs to create product prototypes, UI interface prototypes, interactive HTML demo pages, product feature showcase pages, or mentions "原型", "prototype", "产品界面", "UI 原型", "交互原型", "演示页面" and the like.
Build chat applications with PubNub Chat SDK — direct/group conversations, typing indicators, message actions (reactions, edits, read receipts), file sharing, and threaded messages. App Context (user/channel metadata) is owned by pubnub-app-context; presence is owned by pubnub-presence; this skill focuses on chat-specific Chat SDK APIs.
Diagnoses and improves Qdrant search relevance. Use when someone reports 'search results are bad', 'wrong results', 'low precision', 'low recall', 'irrelevant matches', 'missing expected results', or asks 'how to improve search quality?', 'which embedding model?', 'should I use hybrid search?', 'should I use reranking?'. Also use when search quality degrades after quantization, model change, or data growth.
AI model security assessment and forensics (behavioral layer): extraction assessment (API black-box extraction assessment), fingerprint verification (behavioral fingerprinting and attribution verification), privacy leakage evaluation (membership inference/privacy leakage assessment), and robustness evaluation (adversarial robustness assessment). Triggers: API model theft, black-box model replication, model fingerprinting, membership inference, adversarial examples, behavioral watermarking, model security assessment, privacy leakage assessment.
ARM architecture reverse engineering (non-Android): Cortex-M/A vector tables, Thumb/ARM switching, AAPCS calling convention, position-dependent code relocation, and MMIO peripheral register cross-referencing. Trigger terms: ARM, arm32, Cortex-M, Cortex-A, Thumb, AAPCS, embedded reverse engineering, bare-metal firmware, stm32, vector table.
CTF practice gateway. Orchestration: identify challenge type → simple challenges go directly to [[re-binary-core]] → automation needed [[re-angr]]/[[re-z3]] → obfuscation [[re-deobfuscate]]. Sub-skills: [[re-angr]] [[re-z3]] [[re-pwn]], [[re-stego]]. Triggers: CTF, challenge, reverse-engineering challenge, pwn, babyre, automated solving, symbolic execution, angr, z3, solver, find flag.
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
Functional language runtime reverse engineering (Haskell/OCaml): closure/heap object models, calling conventions, and a dataflow-first strategy. Triggers: Haskell reverse engineering, OCaml reverse engineering, GHC RTS, thunk, STG, OCaml runtime, closure, functional language artifacts.
Physical-layer hardware reverse engineering: decapsulation, die analysis, probes and FIB, PCB circuit analysis, and hardware Trojan detection. Trigger terms: chip reverse engineering, decapsulation, decapping, PCB, hardware Trojan, circuit analysis, die, FIB, side channel, SPA, DPA, fault injection, clock glitch, voltage glitch
Java Card / SIM applet reverse engineering: parse CAP file components (12 components per the specification), reconstruct CAP bytecode (Java subset), analyze AID and installation parameters, and trace process(APDU) dispatch. Trigger terms: Java Card, javacard, CAP file, SIM card, USIM, applet, AID, bank card, JCVM
Malware analysis gateway. Orchestration: default sandbox → initial static analysis → behavior analysis → C2/protocol → IOC/report. Sub-skills: [[re-sandbox]] [[re-behavior]] [[re-ioc]] [[re-ransomware]] [[re-loader]] [[re-fileless]], [[re-doc-malware]], [[re-sample-acquire]]. Trigger terms: malware, sample behavior, callback, C2, IOC, malware analysis
RISC-V architecture reverse engineering: RV32/RV64, compressed instructions (RVC), gp-relative addressing, ABI and ecall system call conventions, and toolchain fingerprinting. Triggers: RISC-V, riscv, RV32, RV64, RVC, compressed instructions, ecall, ESP32-C3, GD32V.
Scalable React composition patterns. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes. Do not use for React/Next.js performance optimization (use react-best-practices instead).
Post to Reddit using AppleScript Chrome control. Use when user wants to post to Reddit, share on subreddits, or promote open source projects on Reddit. Triggers on "post to reddit", "share on reddit", "reddit post", "submit to subreddit", or any Reddit posting request.
Comprehensive UI design guidance based on Refactoring UI principles covering design personality, color theory, layout strategy, visual hierarchy, typography, spacing, mobile-first responsive design, and writing copy that doesn't read like AI wrote it. Use this skill whenever the user is creating new pages, building components, making design additions or changes, reviewing UI code, asking about design decisions (colors, fonts, spacing, layout), writing or editing interface copy, or discussing visual improvements. Trigger for any design-related conversation including "how should this look", "what color", "font size", "spacing", "layout", "responsive", "mobile", "this copy", "headline", "button text", "microcopy", or when reviewing/critiquing existing interfaces.
Refine an existing written artifact for clarity, structure, tone, and readiness while preserving intent and scope. Use when the user asks for open-ended, artifact-wide polishing or improvement. For exact edits or shortening only, follow the request directly without this skill.
A tool for deep research and integration analysis of GitHub repositories. Supports single/multiple repository research, comparative analysis with local projects, and heuristic integration suggestions. Supports a topic-driven search mode: automatically searches for related repositories, clones, analyzes, and generates reports. Clones remote repositories to the local research/ directory, performs deep code analysis, architecture evaluation, and dependency resolution, and generates structured research reports. Trigger conditions: when the user provides a GitHub URL and requests research/analysis/integration/comparison, or provides topic keywords and requests searching for and researching related repositories.
Ingest .mp4 video into Rerun chunk streams with rerun.experimental.Mp4Reader. Read when converting video into a VideoStream, choosing stream vs asset mode, transcoding (B-frames, output codec, GOP size) through FFmpeg, or aligning video PTS onto a recording's wall-clock timeline. Builds on rerun-chunk-processing (stream mechanics) and rerun-data-model (where the video belongs in the recording).
Review a Remotion video with AI vision. Starts with a cheap local check of rendered still frames, then has TwelveLabs watch the full render and score it against a JSON rubric (pacing, composition, text legibility, audio sync and more) with timestamped fixes, applies the fixes and re-reviews. The feedback loop that closes the production circle
Perform a security review of a diff, branch, or pull request, assessing what the change introduces, weakens, or exposes, with a triage-first workflow and false-positive discipline. Use when asked to security review a PR or branch, check a diff before merge or commit, or assess whether a change is safe to ship
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.