Skills
101,174 packaged capabilities and playbooks for agents. Install any of them with one command.
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Reason about quantitative proteomics experiment design and data analysis strategy. Use when the user asks to choose between LFQ, TMT, and DIA quantification; select an imputation method for missing values; pick a normalization strategy; interpret differential expression results from proteomics data; evaluate ratio compression; or design a proteomics study for biomarker discovery or validation. Triggers include "LFQ vs TMT", "DIA quantification", "proteomics normalization", "missing value imputation", "MNAR", "MinProb", "QRILC", "kNN imputation", "VSN normalization", "median centering", "quantile normalization", "limma proteomics", "ratio compression", "proteomics study design", "label-free quantification", "tandem mass tag", "data-independent acquisition", "DIA-NN", "Spectronaut", "MaxQuant LFQ", "proteomics differential expression", "empirical Bayes proteomics", "proteinGroups.txt", "MSFragger output", "TMT normalization code", "LFQ analysis", "proteomics pipeline R", "MaxQuant output".
Use when the user needs React-specific patterns — hooks, component composition, Server Components, error boundaries, rendering optimization, and testing strategies. Triggers: hooks design, component composition, Server vs Client component decision, error boundary placement, context optimization, rendering performance.
Complete React Native and Expo optimization guide combining Callstack profiling with Vercel code patterns. Covers FPS, TTI, bundle size, memory, lists, animations, state, UI, and React Compiler. Use for any React Native performance, debugging, or best practices task.
Senior-level review of a React feature, by theSeniorDev — structure & boundaries, state & data flow, performance & rendering, types/forms/testability/a11y, and styling architecture & motion. Recommends design patterns by name when one fits. Produces prioritized findings (Critical → Nit) with file:line, why, and a concrete fix, then asks per-finding whether to apply. Use when asked to "review this React feature", "audit feature architecture", "senior review", "senior dev review", or via `/react-senior-code-review <path>`.
Verify the model’s understanding of a user’s instruction before undertaking substantial work. Use when a request is long, bundled, high-stakes, hard to undo, or has ambiguous scope or references such as “this,” “that,” “it,” “the other one,” “whatever is cleaner,” or “whichever order makes sense.” Restate it internally, cross-check against available context, proceed silently if resolved, and surface only a genuine remaining ambiguity.
Generate a polished, professional README.md for any project — from scratch or by improving an existing one. Use this skill whenever the user mentions README, wants to improve their repo's first impression, asks about shields.io badges, wants a star history chart, contributor avatars, documentation tables, project structure trees, or mermaid architecture diagrams in their README. Also use it when someone wants their GitHub repo to "look more professional", "look legit", or make a better first impression — even if they never say the word "README". Covers badge generation, README rewrites for new versions, fork customization, and adapting README structure to different project types (libraries, apps, docs repos, monorepos). Does NOT cover: CONTRIBUTING.md, CHANGELOG, API documentation, CI/CD setup, GitHub profile READMEs, or generating docs for individual components.
Analyzes conversation transcripts to extract user corrections, patterns, and preferences, then proposes skill improvements. Use this skill when users provide corrections, express preferences about code style, or when patterns emerge from successful approaches. Can be triggered manually with /reflect or automatically at session end when enabled.
Use when the user asks to "create a campaign report", "build an executive summary", or "deliver client results"; produces audience-tailored influencer marketing reports (executive, client, internal team) with data tables, narrative, key learnings, and recommendations. Not for raw metric computation — use performance-analyzer. 达人营销报告/结案汇报
Look up current research information using the Parallel Chat API (primary) or Perplexity sonar-pro-search (academic paper searches). Automatically routes queries to the best backend. Use for finding papers, gathering research data, and verifying scientific information.
Detects common LLM coding agent artifacts across four categories (tests, dead code, abstraction, style) over the project or changed files — using parallel subagents when the agent supports them, otherwise four sequential passes. Scans files changed since main by default; use --all for full-project scan. Triggers on LLM cruft cleanup, agent-generated code review, dead code sweeps, test-quality passes, or when the user asks to scan the whole repo.
Design review for existing Motoko backends — judging whether code is well designed, not how to write it. Covers invariants encoded in types (variants over Text, correlated fields, case _ smells), stable vs transient state discipline under enhanced migration, file structure (types.mo/lib/mixins/main.mo), and severity-ranked findings (Blocker/Warning/Note). Use when reviewing, auditing, critiquing, or refactoring existing .mo files, or when asked for code-review feedback on a Motoko canister. Do NOT use for writing new Motoko code or fixing compiler errors (writing-motoko), authoring migration files (migrating-motoko-actors), or deployment.
Use Ref and WidgetRef to read, watch, listen, invalidate, and refresh providers; onDispose and onCancel lifecycle; ref.read vs ref.watch vs ref.listen, ref.invalidate and ref.refresh. Use when interacting with Riverpod providers from widgets or other providers, when to use watch vs read, or when resetting provider state. Use this skill whenever the user asks about ref.watch, ref.read, ref.listen, ref.invalidate, or Riverpod lifecycle.
Use when making Roblox avatars/characters look accurate or varied — sourcing and applying clothing (shirts/kits), hair, and faces from the Marketplace catalog, validating them, rendering to hand-pick good ones, and doing nation/identity-weighted skin & hair. Covers the WebFetch catalog-API pipeline that the Studio search_asset tool can't do.
Use when you need to test a Roblox game under conditions a solo Studio playtest can't reach — mobile/tablet/phone (device emulation, TouchEnabled), multiplayer features (parties, co-op, trading, anything needing a 2nd player), or any client state that normally requires another person. Covers driving Studio's Test menu via AppleScript and simulating multiplayer by firing real remotes from the Server VM. Load this INSTEAD of telling the user "I can't test that myself".
Core skill for Sa-Token, a lightweight Java authorization and authentication framework. Covers login authentication, permission/role authentication, annotation-based authorization, route-interceptor authorization, Session sessions, kicking users offline, Token validity strategies, framework configuration, Token styles and submission prefixes, frontend-backend separation, remember-me mode, mutually exclusive login on the same client type, and handling NotLoginException scenario values. StpUtil is the core facade utility class, providing the full authorization API: login/checkLogin/logout/isLogin/getLoginId/getTokenValue. Use when the user needs to integrate authorization into a Java Web project, use StpUtil for login/permission checks, or configure route interceptors or annotation-based authorization. Does not cover advanced features such as two-factor authentication/bans/multiple accounts/SSO/OAuth2/microservices/API security/JWT/Redis — use the corresponding specialized skill for those.
Apollo.io platform help — config, integrations, CRM sync, API, analytics, dialer, Chrome extension, credit management, admin. Use when Apollo settings aren't configured right, CRM sync is breaking or duplicating records, running out of credits too fast, API calls returning errors, or something in Apollo isn't working as expected. Do NOT use for building prospect lists (use /sales-prospect-list), enriching contacts (use /sales-enrich), interpreting buying signals (use /sales-intent), or designing outbound sequences (use /sales-cadence).
Sales coaching and training — onboarding new reps, skill development, role-play practice, coaching programs, certifications, manager coaching cadences, and measuring coaching impact. Use when new reps ramping too slowly, managers not sure how to coach effectively, role-plays feel awkward or unproductive, no structured way to certify reps, coaching efforts not moving pipeline metrics, or struggling to build a coaching culture. Do NOT use for reviewing specific sales call recordings (use /sales-call-review), Seismic-specific platform help (use /sales-seismic), Allego-specific platform help (use /sales-allego), or sales content creation (use /sales-content).
Inspects deal health, map stakeholders, identify risks, and recommend next actions. Use when a deal feels stuck or at risk, you're not sure who the real decision-maker is, champion seems weak or disengaged, MEDDPICC gaps are unclear, a deal review is coming up and you need to prep, or you can't tell if a deal will actually close. Do NOT use for portfolio-level pipeline management or revenue forecasting (use /sales-forecast), or reviewing a specific sales call (use /sales-call-review).
Step 1–2 of building a Salla App Function: pick the trigger, confirm its exact `payload.data` shape, and choose a synchronous action (runs before the operation, can block/modify, 5 s total) vs an asynchronous event (runs after, fire-and-forget, 30 s). Use before writing the handler. Routed from salla-app-functions; next is salla-app-functions-handler.
Step 3 of building a Salla App Function: write the handler body from the trigger's `template`. Use when coding the logic — reading typed `context` (payload/merchant/settings), returning the response contract via the `Resp` (or entity `Shipment`) builder, staying inside the V8-isolate sandbox (web-standard APIs, `fetch` for HTTP), and bounding each `fetch` to the sync/async timeout. Routed from salla-app-functions; validate next with salla-app-functions-validate. Token-handling → salla-app-auth; API details → salla-api-core.
Run a saved Salla App Function on a demo store with `salla_functions action=preview` (app_id, trigger, store_id, and the trigger's form fields as REAL ids). Use after save to verify behavior before publishing; poll `salla_functions action=deploy_status` while it is still deploying. Routed from salla-app-functions; uses salla-api-core to fetch real ids.
REQUIRED before saving a Salla App Function: keep the template's wrapper exactly — its first AND last line — with all code inside the wrapper (no hoisted const/helper), and type-check the handler with the TypeScript compiler against the trigger's `types` (.d.ts URLs) at the editor's bar — non-strict, ignoring unused (TS6133) — plus validate any documented Admin API call against its OpenAPI schema (salla-api-core loop). `salla_functions action=save` validates ONLY those wrapper lines. Routed from salla-app-functions; release with salla-app-functions-release.
Check design-system health for a React+Vite app — raw hex, undefined/unused tokens, scale gaps, DESIGN.md drift, and (opt-in) property→token-family misuse. Use for 'is my design system healthy', 'find raw colors', 'check tokens', or a mixed design system.
Set up Storybook on a React+Vite app that has none — defer to Storybook's own agent setup (`npm create storybook@latest`, `storybook skills setup`), then align viteFinal/providers/MCP and ask where stories live. Use for 'set up Storybook', 'install Storybook', or NO_STORYBOOK.
Map inclusive usage scenarios across ability spectrums for a product or feature. Chains: ability-spectrum-mapping, situational-impairment-mapping, assistive-technology-scenarios. Use when planning test coverage, identifying accessibility gaps, or understanding how a feature performs across diverse users.
Systematic strategies for searching scientific literature across PubMed, arXiv, Google Scholar, and AI-assisted tools. Covers PICO framework for clinical questions, three-tiered search (database-specific, AI-assisted, content extraction), PubMed field tags and MeSH, boolean query construction, and full-text extraction. Use when planning a literature search or choosing a search tier.
Geostatistical analysis with scikit-learn style API. Compute variograms, kriging interpolation, and spatial correlation analysis. Use when the agent needs to: (1) Compute experimental variograms from spatial data, (2) Fit variogram models (spherical, exponential, gaussian, matern), (3) Perform Ordinary or Universal Kriging interpolation, (4) Assess spatial anisotropy with directional variograms, (5) Cross-validate spatial models, (6) Analyze spatio-temporal data, (7) Export variogram parameters for other geostatistical software.
Builds, evaluates, and audits right-censored survival analysis workflows with scikit-survival (sksurv): Cox PH, Coxnet, IPC ridge, survival trees, forests, boosting, and SVMs, plus nonparametric cumulative incidence for competing risks. Covers leakage-safe scikit-learn pipelines, nested CV, and censoring-aware metrics. Use when fitting survival models on time-to-event data, building structured outcome arrays, computing IPCW concordance, dynamic AUC, or Brier scores, estimating cause-specific cumulative incidence, or tuning models without leakage. Not for Fine-Gray regression, which scikit-survival does not provide.
When the user wants a technical site audit, crawl data analysis, location page quality checks, duplicate content detection, schema validation at scale, or internal linking analysis. Trigger on "Screaming Frog," "site crawl," "technical audit," "crawl data," "broken links," "duplicate content," "location page audit," or when analyzing exported crawl CSV/Excel files.
Scanpy-based single-cell RNA-seq analysis pipeline covering loading (10X, h5ad), QC, normalization, HVG selection, PCA/UMAP, Leiden clustering, differential expression, and batch correction (Harmony, scVI). Use when the user mentions single-cell, scRNA-seq, Scanpy, AnnData, UMAP, clustering, 10X, h5ad, leiden, highly variable genes, Harmony, scVI, cluster cells, find marker genes, filter low-quality cells, gene expression matrix, doublet removal, normalize counts, dimensionality reduction, cell clustering, QC single-cell, filtered_feature_bc_matrix, cellranger output, 10X h5, scrublet, cell ranger, count matrix, find cell types, batch effect, integration, neighborhood graph, or differential expression genes.
Search and inspect local macOS 26 SDK documentation and Swift interfaces, especially SwiftUI, SwiftUICore, and AppKit. Use when Codex needs current macOS 26 APIs, Liquid Glass components, window, scene, menu, toolbar, AppKit bridging, availability, signatures, deprecations, symbol docs, or SDK-only facts and should not rely on memory or public web docs being available.
Secures Helm chart deployments by verifying chart signatures and provenance, rendering and linting templates for misconfiguration, enforcing pod security contexts through values.yaml, moving secrets into an external store instead of Helm values, and scoping RBAC for Helm operations in CI/CD. Use when deploying charts to Kubernetes or reviewing chart provenance, templates, or release RBAC. Keywords: Helm, provenance file, helm verify, helm lint, values.yaml, Tiller-less, release RBAC, external secrets. Do not use for scanning the rendered manifests themselves - use scanning-kubernetes-manifests-with-kubesec.
Use when goal, constraints, and success criteria are already settled and the user wants them frozen into a formal spec an agent can execute and verify: 'turn this into a spec', 'write it up as a Seed', or right after an interview. Returns an immutable Seed with acceptance criteria. Not for vague requests (use interview first).
Two stages: first a single-page sheet of numbered panels from an image model, then that sheet fed to Seedance as the reference. The sheet owns order, framing and timing, and the video prompt only has to connect the panels. Use when the user wants a Seedance 2.5 / 2.0 video prompt for this kind of clip (Storyboard grid to video; 中文触发词: 分镜网格转视频, 分镜图, 九宫格, 故事板), or asks how published cases of this type were prompted. Read references/cases.md before drafting.
Guidance for designing and operating Microsoft Sentinel, the cloud-native SIEM and SOAR delivered through the Defender portal. Covers workspace design, data connectors, ingestion tiers (Analytics/Basic/Auxiliary/ADX), analytics rules, hunting, watchlists, automation playbooks, and cost/commitment-tier optimisation. WHEN: deploy Microsoft Sentinel, design SIEM, onboard data connectors, write analytics rule, KQL detection, Sentinel playbook, SOAR automation, Sentinel cost optimization, log ingestion tiers, commitment tier, Sentinel workspace design, how do I set up a SIEM, collect logs from third-party tools, ingest firewall or Linux syslog into Azure, write a detection rule, automate incident response, how much does Sentinel cost, Auxiliary logs, Basic logs, ADX archive, codeless connector. DO NOT USE when the goal is correlating Microsoft 365 XDR alerts into incidents (use defender-xdr) or onboarding Sentinel into the unified Defender portal (use unified-secops-platform).
This skill should be used when implementing or debugging Sentry error tracking, instrumentation, distributed tracing, performance issues, or capture-errors flows. It provides a portable workflow for SDK setup, middleware wiring, expected-error classification, and verification.
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.