Skills
100,696 packaged capabilities and playbooks for agents. Install any of them with one command.
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Compresses and clarifies an existing LLM prompt through concept elevation, verifying that no original instruction is lost. Use when the user says "improve this prompt", or when the user supplies a prompt or prompt file and asks to improve, tighten, optimize, or refine it. Not for writing a new prompt; use writing-system-prompts.
Raw mechanical interfaces fusing Swiss typographic print with military terminal aesthetics. Rigid grids, extreme type scale contrast, utilitarian color, analog degradation effects. For data-heavy dashboards, portfolios, or editorial sites that need to feel like declassified blueprints.
Build upstream/downstream industry-chain mappings and track business-cycle signals (available proxies such as price, capacity, orders, inventory). Use when the user asks about industry-chain analysis, upstream/downstream linkage, cycle tracking, or needs an industry-chain map.
Frictionless setup. Detects missing daemon, installs it, configures local memory, and verifies the full plugin → MCP → daemon round-trip. Run after `/plugin install origin@7xuanlu`, or any time the user says "set up origin", "is origin working", "fix origin".
A skill for user-need insight and product-opportunity analysis. Applies to market analysis before new product development, marketing planning, or category entry. Use this skill when you need to quickly understand target users, uncover emotional motivations, and turn insights into actionable product opportunities.
Cheat sheet + workflow for launching interactive coding-agent CLIs (Claude Code, Gemini CLI, Codex CLI, Cursor CLI, and pi itself) via the interactive_shell overlay or headless dispatch. Use for TUI agents and long-running processes that need supervision, fire-and-forget delegation, or headless background execution. Regular bash commands should use the bash tool instead.
Interview prep coach that helps job seekers systematically prepare for interviews through three modes: knowledge review, Q&A practice, and full mock interviews. Covers technical interviews (algorithms, system design, coding) and behavioral interviews (STAR, culture fit). Trigger words: "帮我准备面试", "面试准备", "考考我", "来一轮模拟面试", "模拟面试", "帮我梳理考点", "明天面试", "面试模式", "准备面试". Also trigger proactively when the user mentions preparing for an interview at a specific company. Not for: pure knowledge Q&A, resume editing, or career-planning consulting.
Generate images using InvokeAI's local API. Use when asked to generate, create, or make images with InvokeAI, FLUX.2 Klein, Z-Image Turbo, FLUX, or SDXL models. Supports text-to-image generation, automatic model detection, image download, and parameter selection based on model architecture.
Route a Jam (a jam.dev screen recording or screenshot with the console, network and user actions attached) to the right Jam skill. Use when the user shares a jam.dev link with no clear ask ("look at this jam", a bare link), or types /jam. For a clear ask (fix, test, ticket, record, annotate), use that skill directly.
At the end of a batch of agent work (several fixes, a test run, a set of tickets), return one table that ties each claim to the Jam (a jam.dev screen recording with the console and network attached) that shows it, with its status and link, and file those Jams so they can be found later. Use when the user asks "give me the jams", "which jam shows this", "what was the jam I gave you", "show proof for each", or when a run that recorded Jams finishes and the user wants the proof listed.
Turn a narrated Jam (a jam.dev screen recording with the speaker's transcript, console, network and user actions attached) into a numbered list of every change the person asked for, each tied to the moment, the frame and the words it came from, then work through the list. Use when the user pastes a jam.dev link and says "do what I said in this", "consume this jam", "make it look like this", or shares a feedback, walkthrough or reference recording rather than a single bug.
File a Jam (a jam.dev screen recording with the console, network and user actions attached) as an issue in the team's tracker, such as Linear, GitHub Issues or Jira, with the link, repro steps, the key frame and the errors, after checking it was not filed already. Use when the user says "make a ticket for this jam", "file this jam as a bug", "put this jam in Linear", "open an issue for this recording", or asks whether a Jam was ever ticketed.
Autonomous job application agent. Searches jobs, tailors resumes per JD, generates cover letters, fills ATS forms, and submits applications across 10+ platforms — fully hands-free. Triggers on: "apply", "job application", "auto apply", or job URLs.
Use when the user wants to persist a single insight from the current chat as a structured, project-local doc — debugging breakthroughs, hard-won config, workflow steps, or post-mortem lessons. Triggers: 'save this', 'document this', 'we figured it out', '记录下来', '总结一下'. Writes to <project>/.{trae,claude,cursor,windsurf}/knowledges/. For global compounding knowledge (architecture, patterns, APIs), use llm-wiki instead.
Landscape evolution and surface process modelling in Python. Build 2D numerical models for erosion, hydrology, soil transport, and geomorphology. Use when the agent needs to: (1) Model landscape evolution over time, (2) Simulate river/stream erosion, (3) Route water flow across terrain, (4) Model hillslope diffusion processes, (5) Simulate weathering and soil production, (6) Analyze drainage networks, (7) Combine multiple geomorphic processes, (8) Load/save DEM data for modeling.
Codifies the latest correction or mistake in the conversation as a short imperative rule in the project or global CLAUDE.md or AGENTS.md, after the user approves it. Use when the user says "learn from this mistake", or when the user asks to learn from a mistake, remember a lesson for next time, add a rule to CLAUDE.md, or not repeat a behavior. Not for a whole session; use running-retros.
Estimate and compare what a workload costs on Lightning AI - fetch live per-hour GPU/CPU machine prices from the accelerator catalog for every cloud (Lightning Cloud, AWS, GCP, Lambda Labs, Nebius, Voltage Park), size hardware for a model, then quote a training run, fine-tune, serving deployment or data-prep job over hours to months, including spot rates, multi-node fan-out and Drive storage. Use when the user asks "how much would it cost to train/serve X", "what's the price of an H100/B200 on lightning", "how much for 32 8xH100 nodes for 2 weeks", "which cloud is cheapest for this", "what will my monthly bill be", or wants hardware sized for a model before pricing it.
Help Litium partner developers build, configure, and deploy e-commerce sites and backoffice UI extensions. Use for React Accelerator (Next.js, GraphQL, blocks, cart, checkout, wishlist), MVC Accelerator (.NET Razor views, controllers, view models, client builds), local setup, Litium Cloud deployment, data modelling and product fields, Litium Admin, Storefront GraphQL, Connect, and Extension Management APIs, or forum.litium.com solutions. Covers @litiumab/platform-extension-sdk extensions in React, Vue, Angular, or Vanilla: scaffolding, extension.manifest.json, window.litiumExtension, sub-path routing, IIFE/Vite builds, testing, installation, and deployment. Triggers: build Litium site, create Litium project, product field, block, accelerator, Litium API, wishlist, cart, placeOrder, custom field, backoffice extension, platform-extension-sdk, UI extension, panel, or settings page extension.
Use PUA talk to motivate OpenClaw (lobster) to work more efficiently and with better quality. Triggered when you need to push an AI to complete tasks more efficiently and with higher quality, applicable to: (1) low task execution efficiency (2) output quality not good enough (3) needing the AI to focus on the work (4) tasks that need to be done in one go without dawdling
Add memorable color systems, adaptive layouts, scenario boards, and colorway prompts to existing logos, generated brand-system boards, mascot logos, base logos, app icons, wordmark lockups, seals, and identity sheets without changing the underlying symbol. Use when the user asks to add colors, recolor, create palettes, generate colorways, make color applications, increase first-glance impact, improve broad audience appeal, change the colorway board background/layout, or enhance a previously generated logo with brand colors.
Generate professional slide deck images from content (Markdown, text, URLs). Creates outlines with style instructions, then generates individual slide images. Supports 16 visual styles, CJK/Latin mixed text, branding overlays, and PPTX/PDF export. Use when the user asks to "create slides", "make a presentation", "generate deck", "slide deck", "PPT", "做PPT", "生成幻灯片", "制作演示文稿", or wants to turn content into a visual slide deck.
Guide incident response from detection to post-mortem using SRE principles, severity classification, on-call management, blameless culture, and communication protocols. Use when setting up incident processes, designing escalation policies, or conducting post-mortems.
This skill should be used when the user asks to "use marimo", "create a marimo notebook", "debug a marimo notebook", "inspect cells", "understand reactive execution", "fix marimo errors", "convert from jupyter to marimo", or works with marimo reactive Python notebooks.
Generate mascot-inspired logo creative directions, image-generation prompts, and optionally direct logo images by extracting the most recognizable feature from an animal, character, object, celestial body, fictional figure, or mascot-like source. Use when the user asks for animal feature abstraction, mascot logo design, paw/head/antler/tusk/alien/crescent-style symbol ideas, mascot-derived brand marks, direct mascot logo image generation, or revisions to an existing mascot-symbol concept.
Look up current API signatures from installed Mastra packages in node_modules/@mastra/*/dist/docs/. Use this skill to verify exact parameters, constructor arguments, and function signatures that match the installed version. Mastra APIs evolve rapidly - embedded docs ensure accuracy. Covers: Agent constructor parameters, Workflow API, Tool API, Memory configuration, Storage options, and all @mastra/* package exports. Returns actual TypeScript definitions and JSDoc from installed packages. Always use for API verification questions to prevent outdated answers.
Create and simulate UAV scenarios with terrain, buildings, platforms, and sensors using uavScenario. Use when building a UAV simulation, UAV simulator, or UAV scenario in MATLAB. Covers addMesh for terrain/building import, uavPlatform with updateMesh, uavSensor adaptor pattern for GPS/IMU, and the setup/advance simulation loop. Triggers on: uavScenario, UAV simulation, UAV simulator, multirotor simulation, quadrotor scenario, terrain import, building import, GPS sensor simulation.
Generate standard-compliant IEEE 802.11 waveforms using MATLAB WLAN Toolbox. Use when creating WLAN waveforms, PPDU packets, or the transmit side of a link-level simulation. Covers all formats: Non-HT (802.11a/g), HT (802.11n), VHT (802.11ac), HE-SU/HE-MU/HE-TB (802.11ax), EHT-MU/EHT-TB (802.11be), UHR-MU/UHR-TB/UHR-ELR (802.11bn). Handles single-user, MU-MIMO, OFDMA, trigger-based uplink, extended range, preamble puncturing, UEQM, and DRU. Use when asked to generate test waveforms, create packets with MAC frames, configure OFDMA resource units, build trigger-based uplink transmissions, target a specific transmit duration, or build multi-packet waveforms.
Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.
Creates publication-quality matplotlib/seaborn charts in Python with an opinionated, despined, annotation-rich style (whitegrid, DejaVu Sans, ColorBrewer and cubehelix palettes, dimgrey text) and saves a reproducible script plus PDF/PNG. Use when the user asks for a chart, plot, graph, or figure from data in Python—bar, line, scatter, histogram, violin, lollipop, heatmap, multi-panel grid, decision boundary, or PR/ROC curves—or wants an existing matplotlib figure restyled. Also use for terse requests that only name a chart and a data file (e.g. “PR and ROC curves from results.npz”): make the chart
Comprehensive guide for MDAnalysis - the Python library for analyzing molecular dynamics trajectories. Use for trajectory loading, RMSD/RMSF calculations, distance/angle/dihedral analysis, atom selections, hydrogen bonds, solvent accessible surface area, protein structure analysis, membrane analysis, and integration with Biopython. Essential for MD simulation analysis.
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.