Skills
100,928 packaged capabilities and playbooks for agents. Install any of them with one command.
All
Comprehensive SAP Joule CLI (formerly sapdas CLI) assistant for managing digital assistants from the command line — compiling capabilities, deploying assistants, running BDD tests, linting, and troubleshooting errors. Use this skill whenever the user mentions "joule cli", "sapdas", "joule compile", "joule deploy", "joule test", "joule login", "joule lint", digital assistant deployment, capability compilation, DAAR files, RTA artifacts, or any task involving the Joule command line interface — even if they just say something like "deploy my assistant" or "how do I log in to Joule from the terminal". Also trigger when the user asks about testing Joule capabilities with Cucumber, linking AI assistants, managing deployed assistants, or automating Joule workflows in CI/CD pipelines.
Design and create Busabase-backed App-in-Skill packages with a canonical app project, Busabase-native setup and product onboarding, review/execution workflows, a standardized responsive UI, and AirApp-first delivery. Use when a user wants a Busabase research desk, review queue, planner, action console, operating dashboard, control panel, collaboration workspace, onboarding/readiness flow, or an existing Kelly App-based skill updated. Every generated skill contains a complete app/ project, deploys that source to Busabase AirApp by default, runs pnpm dev only when local preview is explicitly requested, follows the Kelly desktop and phone UI contract, delegates AirApp runtime, SDK, security, scaffolding, and deployment constraints to busabase-app-creator, and delegates repository-level conformance testing to kelly-app-skill-creator-tests.
Work with project documentation and the central knowledge repository (marcioaltoe/secondbrain). Use when editing docs/, CONTEXT.md, specs, ADRs, or task files in any repo with a .secondbrain-project file; when deciding where a documentation change should be committed; when touching secondbrain's mirror, notes, shared or wiki areas; and when research, planning or implementation work could consult or feed the second brain (wiki + qmd search).
Securely integrate with the official LabArchives ELN REST-like API and Inventory API v1. Use for regional endpoint selection, signed-request construction, user authorization and UID flows, local LA container validation, and verified LabArchives integration workflows.
Open-source FAIR biology data framework. Version artifacts (AnnData, DataFrame, Zarr), track lineage, validate via ontologies (Bionty), query datasets. Integrates with Nextflow, Snakemake, W&B, scVI. For scRNA-seq use scanpy; for ontology lookups use bionty.
Structural restructuring and refinement of Markdown study notes: a two-stage Plan-and-Execute workflow — first diagnose and classify (produce checklist/outline/mapping/scaffold), then refine and deliver (rewrite body text, out-of-scope routing, resource tracking). Supports two scopes: the whole document or specified sections; supports either outputting only the checklist or directly outputting the organized body text.
Use this skill when the user says "/li", or when a request relates to content creation but the intent is ambiguous and it is unclear which tool to use. As the main entry point of the li toolbox, it determines intent and routes to the corresponding specialized skill. Should not trigger: when the user's request already clearly matches a specific li-* skill (e.g. "write a script" → li-writer, "refine a topic" → li-topic), trigger that skill directly instead of this entry point. DO NOT trigger for non-content-creation tasks. Use when the user says "/li" or the intent is ambiguous across multiple li-* tools.
Clean, compact, and reconcile durable project context and any configured issue-tracker mirror without changing established meaning. Always use this skill whenever a request cleans, audits, or checks consistency across two or more durable context sources, even when they are called product docs, glossary, decisions, specs, or agent instructions instead of PRODUCT.md, GLOSSARY.md, docs/decisions, docs/specs, AGENTS.md, or CLAUDE.md. Also use for confirmed shipped-spec retirement or a full consistency sweep between default-branch spec folders and their tracker issues. Do not use to define new product intent, record one newly settled term or decision, look up context, shape a feature, implement work, or revise an active spec's behavior against work that has shipped, which belongs to `babysit-specs`.
Professional malware analysis workflow for PE executables and suspicious files. Triggers on file uploads with requests like "analyze this malware", "analyze this sample", "what does this executable do", "check this file for malware", or any request to examine suspicious files. Performs static analysis, threat intelligence triage, behavioral inference, and produces analyst-grade reports with reasoned conclusions.
Manage Git branching strategies, commit conventions, and collaboration workflows. Use when choosing between trunk-based development, GitHub Flow, or GitFlow, implementing conventional commits for automated versioning, setting up Git hooks for quality gates, or organizing monorepos with clear ownership.
Implements media and file management components including file upload (drag-drop, multi-file, resumable), image galleries (lightbox, carousel, masonry), video players (custom controls, captions, adaptive streaming), audio players (waveform, playlists), document viewers (PDF, Office), and optimization strategies (compression, responsive images, lazy loading, CDN). Use when handling files, displaying media, or building rich content experiences.
Fetch and extract web content as clean Markdown when provided with URLs. Use this skill whenever a user provides a URL (http/https link) that needs to be read, analyzed, summarized, or extracted. Converts web pages to Markdown with 80% fewer tokens than raw HTML. Handles all content types including JS-heavy sites, documentation, articles, and blog posts. Supports three conversion methods (auto, AI, browser rendering). Always use this instead of web_fetch when working with URLs - it's more efficient and provides cleaner output.
Conduct structured market research for a solopreneur business. Use when sizing a market, understanding industry dynamics, mapping the competitive landscape broadly, identifying trends, or building customer personas from external data. Covers TAM/SAM/SOM estimation, free data sources, trend analysis, and persona construction. Trigger on "research this market", "how big is this market", "understand the industry", "market trends", "who are the players in this space", "market analysis".
Integrate markstream-vue into a Nuxt 3 or Nuxt 4 app. Use when Codex needs client-only boundaries, SSR-safe setup, browser-only peer gating, worker-aware initialization, renderer `mode` selection, or a safe `MarkdownRender` integration inside pages, components, or Nuxt plugins.
Via modeling: pads, antipads, ground return vias, GRV placement, and signal integrity for high-speed layer transitions. TRIGGER: user asks to model a via, design a via transition, place ground return vias, analyze via performance, or check signal integrity through layer transitions. Invoke BEFORE writing code — only viaSingleEnded exists (no viaDifferential), and the location format is non-obvious. SKIP: general signal integrity without vias (use matlab-analyze-em), transmission line design (use matlab-design-pcb-transmission-line), PDN analysis (use matlab-analyze-pcb-pdn), material/stackup setup only (use matlab-manage-pcb-material).
Export pcbComponent designs to Gerber files with RF connectors and fab service formatting for PCB manufacturing. TRIGGER: user asks to export a PCB design to Gerber, generate manufacturing files, or write out a pcbComponent for fabrication. Invoke BEFORE writing export code — gerberWrite signature (pcbComponent, connectors, filename) is non-obvious. SKIP: importing/reading PCB files (use matlab-read-pcb-layout), building PCB geometry (use matlab-assemble-pcb-layout), EM analysis (use matlab-analyze-em), material/stackup setup (use matlab-manage-pcb-material).
Build bots and mini-apps for MAX Messenger (max.ru, МАКС) — Bot API at platform-api2.max.ru, official Go client (max-bot-api-client-go), Go framework maxbot, TypeScript client @maxhub/max-bot-api, MAX Bridge (window.WebApp) and the @maxhub/max-ui React kit. Use for any code that sends/receives MAX messages, webhooks or long polling, uploads media, inline keyboards and callbacks, message formatting, chat/channel admin, comments, mini-app initData / phone verification, and TLS errors caused by the Минцифры root CA. Triggers: MAX bot, MAX Bot API, platform-api.max.ru, platform-api2.max.ru, max.ru bot, бот для МАКС, бот в MAX, мини-приложение MAX, MAX Bridge, window.WebApp in MAX, max-ui, maxbot, @maxhub. Not for MAX product/market strategy questions without code
Before fixing a slow/stale/timeout alert, measure the step yourself. Kill the theory with a stopwatch, not a code change. Measure-first ops triage for temporal alerts (stale, timeout, freshness, wedged, N hours behind) from gbrain doctor, autopilot, sync, and cron monitors — runs BEFORE any timeout raise, threshold change, or pipeline rewrite.
Assistant for creating Mermaid diagrams. Use when the user explicitly requests a Mermaid diagram, or mentions a flowchart, architecture diagram, sequence diagram, state diagram, Gantt chart, ER diagram, user journey map, or class diagram and wants a Mermaid code block directly. First determine the most suitable diagram type, then generate clear, well-structured Mermaid code that can be rendered, with a brief explanation or iteration suggestions if needed. Not for scenarios that call for high-fidelity visual design mockups.
Pick the 5 metrics that matter at a startup's current stage, with definitions, targets, actions when below target, a minimal tracking setup, and a weekly review template. Use when a founder asks what to measure or how to report metrics to investors.
Use when the user explicitly asks to "migrate a proposal", "upgrade a template", "migrate across versions", or "convert an old proposal to a new template". Intelligently migrates NSFC LaTeX proposals to a new template version, based on a five-stage workflow (analyze → map → plan → execute → verify), automatically handling structural changes, content reorganization, and reference updates; supports AI-driven semantic matching with heuristic fallback, and provides runs output isolation and a one-click migration script.
Guidelines for creating responsive email templates using MJML framework with .NET Razor syntax integration. Use when building responsive email templates in .NET applications, integrating MJML with Razor views, or creating email layouts that work across email clients.
Build reusable product-usage and engagement models — retention, stickiness, and lifecycle — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to model, define, or compute whether users come back (retention / churn), how frequently they engage (stickiness / power users / DAU-WAU-MAU ratio), or the composition of the active base (new / returning / resurrecting / dormant lifecycle). These three are one engagement family sharing a start-event/return-event vocabulary and an interval granularity; this skill treats them together and helps pick the right lens: retention for the return-rate cohort matrix, stickiness for the frequency distribution, lifecycle for growth quality. On PostHog, model them in HogQL (mirroring query-retention / query-stickiness / query-lifecycle); in dbt, build fct_retention / fct_stickiness / fct_lifecycle marts with tests. Read modeling-warehouse-foundations first; feeds the retention validation used by modeling-activation-metrics.
Build reusable revenue models — MRR, ARR, gross revenue, new/expansion/contraction/churn, ARPU, LTV, and per-customer/per-account revenue — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to model, define, or compute recurring revenue, monthly/annual recurring revenue, churn or retention of revenue, lifetime value, average revenue per user, or revenue by customer, cohort, product, or currency. On PostHog, build on the managed *_revenue_view views (revenue_item, mrr, customer, subscription, charge, product) fed by Stripe or custom revenue events — not raw Stripe tables — and normalize money with convertCurrency(). In dbt, stage the payment source and compute fct_mrr / fct_revenue_item / dim_customer marts with tests. Covers picking the right source, the subscription-config gotcha that leaves MRR empty, currency handling, and linking revenue to persons/groups. Read modeling-warehouse-foundations first for the view-vs-dbt mechanics.
Generate speech, music, and sound effects using ModelsLab's v7 Voice API. Supports text-to-speech, speech-to-text, speech-to-speech, music generation, sound effects, dubbing, song extension, and song inpainting via ElevenLabs and Inworld models.
Molecular featurization hub (100+ featurizers) for ML. SMILES to fingerprints (ECFP, MACCS, MAP4), descriptors (RDKit 2D, Mordred), pretrained embeddings (ChemBERTa, GIN, Graphormer), pharmacophores. Scikit-learn compatible with parallelization/caching. For QSAR, virtual screening, similarity, and molecular DL.
Transform lessons and insights into compelling short stories with tension, conflict, and clear takeaways. Use when turning a business lesson into a story, writing narrative social posts, or making dry insights engaging through storytelling. Includes story arc templates and micro-story formats for social content.
Build, run, and debug Nextflow data pipelines and nf-core workflows end to end. Use whenever the user mentions Nextflow, nf-core, .nf files, nextflow.config, DSL2, processes/channels/operators, samplesheets, or wants to run a community pipeline (e.g. nf-core/rnaseq, nf-core/sarek), write or test a module/subworkflow with nf-test, configure executors/containers (Docker, Singularity/Apptainer, Conda, Wave), scale a workflow to HPC/SLURM or cloud (AWS Batch, Google Batch, Azure, Kubernetes), or debug a failed/-resume run. Make sure to use this skill for any reproducible scientific/bioinformatics workflow work even if the user does not say the word "Nextflow", and for authoring nf-core-compliant pipelines, modules, configs, and linting.
Dataflow workflow engine for scalable bioinformatics pipelines. Defines processes (containerized tasks) connected by channels; runs local, HPC (SLURM/SGE), cloud (AWS/GCP/Azure), or Kubernetes via a single config change. Powers nf-core. Use Snakemake for rule-based Python workflows; use Nextflow for containerized, cloud-native, and nf-core pipelines.
Methodology for using the Nuclei vulnerability scanner. Use when scanning targets for known vulnerabilities, verifying CVEs, or running batch PoC detection. Nuclei has 9000+ community-maintained templates covering CVEs, default credentials, misconfigurations, information leaks and more. This skill should be used for any scenario involving nuclei scanning, batch CVE verification, PoC detection, or vulnerability template search. It also applies when you need to extract payloads from nuclei templates for manual exploitation
Prepares a complete O-1A extraordinary-ability petition kit in a local case folder — free evaluation from a Google Scholar profile or CV against the 8 O-1A criteria, petitioner-structure decision (U.S. employer, U.S. agent, or the beneficiary's own company), evidence checklist and citation pipeline, drafting the petitioner support letter, consultation/advisory opinion, itinerary and expert letters, I-129 field guidance, and an editable hand-off package the petitioner reviews, revises, signs and files. Use when the user mentions O-1, O-1A, O-1B, O1 visa, I-129, extraordinary ability, advisory opinion or consultation letter, agent petition, founder/startup visa, RFE response, Request for Evidence, NOID, 补件, O1签证, 杰出人才, 杰出能力签证, or an H-1B lottery miss. Also handles an RFE or NOID on an already-filed I-129, even one filed without this skill. Document preparation only, not legal advice.
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.