Skills
101,174 packaged capabilities and playbooks for agents. Install any of them with one command.
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Plan a codebase restructuring through read-only architecture mapping, DDD and Screaming Architecture analysis, reference quarantine, bounded review repair, and a persisted decision report. Use when a user wants an evidence-backed restructuring plan without implementing file moves or refactors.
Plan, implement, and debug frontend tests: unit/integration/E2E/visual/a11y. Use for Playwright MCP browser automation, Vitest/Jest/RTL, flaky test triage, CI stabilization, and canvas/WebGL games (Phaser) needing deterministic input plus screenshot/state assertions. Trigger: "test", "E2E", "flaky", "visual regression", "Playwright", "game testing".
Complete Polygon agent toolkit for on-chain operations on Polygon. Use this skill whenever helping an agent set up a wallet, check balances, send or swap tokens, bridge assets, deposit or withdraw from yield (Aave aTokens, ERC-4626 vaults), register on-chain identity, submit or query reputation/feedback, or make x402 micropayments. Covers the full lifecycle: Sequence smart contract wallets, Trails DeFi actions, ERC-8004 identity + reputation, x402 payments. Single CLI entry point (`polygon-agent`), AES-256-GCM encrypted storage.
Create clear, polished before-and-after screenshots from the actual running product for a GitHub pull request. Use when a UI change needs visual proof: capture matching product states, crop to the relevant UI, stitch and caption one comparison image, attach it natively to the PR, and keep the image out of the repository.
When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' 'monetization,' 'how much should I charge,' 'my pricing is wrong,' 'pricing page,' 'annual vs monthly,' 'per seat pricing,' or 'should I offer a free plan.' Use this whenever someone is figuring out what to charge or how to structure their plans. For in-app upgrade screens, see paywall-upgrade-cro.
Use when a B2B SaaS product needs upstream positioning or messaging clarity — defining who it's for, the category, competitive alternatives, differentiation, value props, strategic narrative, or a messaging hierarchy. Triggers include "my messaging isn't working," "we sound like everyone else," repositioning, or auditing an existing positioning doc. Use before homepage, deck, or sales-narrative work. Not for writing the final copy (use copywriter-skill) or running the sales process (use sales-skill).
When the user wants to create SEO-driven pages at scale using templates and data. Also use when the user mentions "programmatic SEO," "template pages," "pages at scale," "directory pages," "location pages," "[keyword] + [city] pages," "comparison pages," "integration pages," "building many pages for SEO," "pSEO," "generate 100 pages," "data-driven pages," or "templated landing pages." Use this whenever someone wants to create many similar pages targeting different keywords or locations. For auditing existing SEO issues, see seo-audit. For content strategy planning, see content-strategy.
Create or refine a Promptfoo redteam config and generate probes from target behavior, code, or OpenAPI evidence. Use for purpose, trust boundaries, plugins, strategies, and grading guidance. Use promptfoo-provider-setup for connection work and promptfoo-redteam-run for an existing scan.
Apply the testing pyramid to a feature — divide test scenarios across levels (unit, integration, e2e), assign each scenario to the cheapest sufficient level, and produce a coverage split per level. Use when asked "unit, integration or e2e?", "what level should this test be?", "how should we test this feature?", "what test coverage do we need?", or to apply the test pyramid to a feature or project.
Analyzes test coverage data (Istanbul, c8, lcov) to identify uncovered lines, branches, and functions with risk-weighted gap detection. Use when analyzing coverage reports, identifying coverage gaps, comparing coverage between branches, or prioritizing which untested code to cover first.
This skill should be used to search the local Obsidian vault / markdown knowledge base by meaning, not just keywords, using the on-device qmd engine (BM25 + vector + LLM rerank). Trigger when the user asks to "search my vault/notes", "find notes about X", "what do my notes say about Y", "do I have anything on Z", "semantic search my knowledge base", or wants concept/cross-lingual retrieval over markdown. Fully local — nothing leaves the machine.
Always invoke for classical analysis, classification, validation, or comparability of satellite, aerial, or drone imagery. This skill owns sensor, product, processing-level and processing-baseline harmonization, including multi-date inputs; add change-detection only after comparable observations exist. Two scenes of the same product level are not automatically comparable: Sentinel-2 L2A crossed a reflectance offset at Processing Baseline 04.00 in January 2022, so any pair spanning that date starts here. Covers spectral indices, masking, compositing, SAR, land cover, and accuracy assessment. Route neural methods to geo-deep-learning and planetary server-side execution to google-earth-engine.
Foundation reference for the Salla Admin (Merchant) API — base URL, Bearer-token auth, request headers, merchant identity, common resource endpoints, pagination, rate-limit tiers/headers, HTTP status codes with slugs, structured error shapes, and the App Settings read-modify-write pattern. Use whenever writing, reviewing, or debugging any Salla Admin API call — auth setup, pagination, error handling, app-settings read/write, or rate limits. Every other Salla skill builds on this; merchant token handling → salla-app-auth.
Systematic framework for evaluating scholarly and research work based on the ScholarEval methodology. This skill should be used when assessing research papers, evaluating literature reviews, scoring research methodologies, analyzing scientific writing quality, or applying structured evaluation criteria to academic work. Provides comprehensive assessment across multiple dimensions including problem formulation, literature review, methodology, data collection, analysis, results interpretation, and scholarly writing quality.
Reviews scientific documents for logical clarity, argument soundness, and rigor by auditing hypothesis-data alignment, claim-evidence chains, quantitative precision, hedging calibration, and terminology consistency across any document type. Use when reviewing scientific argumentation, checking claims vs evidence, auditing terminology, or when user mentions check clarity, review logic, scientific soundness, hypothesis-data alignment, or claims vs evidence.
"Audits Move contracts for security vulnerabilities before deployment using 7-category checklist. Triggers on: 'audit contract', 'security check', 'review security', 'check for vulnerabilities', 'security audit', 'is this secure', 'find security issues'."
Model and analyze Semantic Spacetime (SST) graphs, distances, trajectories, drift, and model files. Do not use this skill for promise-theory vocabulary and fundamentals without SST modeling; use `promise-theory` for the substrate concepts.
Audit how well a page can be operated by AI agents and agentic browsers — semantic interactive controls (<button>/<a href> instead of div/span click handlers), named buttons and links, labeled form controls, no javascript: hrefs, primary content outside iframes, and WebMCP detection — following Google's generative-AI guidance and the web.dev agent-friendly websites guide. Module M22 "Agent-readiness". Feeds the AI Visibility score (weight 4, all directional).
Generate competitive SEO content briefs with per-section word counts, competitor scoring, keyword density guidance, and page-type templates. Supports both new page briefs and improve-existing-page briefs. Use when user says “content brief”, “write a brief”, “content outline”, “blog brief”, “service page brief”, “brief for”, “writing brief”, “content plan”, or “outline for”.
AI image generation for SEO assets: OG/social preview images, blog hero images, schema images, product photography, infographics. Powered by Gemini via nanobanana-mcp. Requires the banana extension installed. Use when user says "generate image", "OG image", "social preview", "hero image", "blog image", "product photo", "infographic", "seo image", "create visual", "image-gen", "favicon", "schema image", "pinterest pin", "generate visual", "banner", or "thumbnail".
Image optimization analysis for SEO and performance. Checks alt text, file sizes, formats, responsive images, lazy loading, CLS prevention, image SERP rankings (via DataForSEO), and image file optimization (WebP/AVIF conversion, IPTC/XMP metadata injection). Use when user says "image optimization", "alt text", "image SEO", "image size", "image audit", "optimize images", "image metadata", "image SERP", "convert to webp", or "image file optimize".
Comprehensive SEO analysis for any website or business type. Full site audits, single-page analysis, technical SEO (crawlability, indexability, Core Web Vitals with INP), schema markup, content quality (E-E-A-T), image optimization, sitemap analysis, and GEO for AI Overviews/ChatGPT/Perplexity. Industry detection for SaaS, e-commerce, local, publishers, agencies. Triggers on: SEO, audit, schema, Core Web Vitals, sitemap, E-E-A-T, AI Overviews, GEO, technical SEO, content quality, page speed, structured data.
Build a content cluster plan from seed keywords — intent-grouped clusters, pillar+spokes architecture with H1/H2 suggestions per spoke, prioritised build order, and an internal-linking map. Plans a content tier across many articles (vs `seo-content-brief` which produces a single article from a topic; vs `seo-page` which audits one existing URL). Use when the user asks for keyword clustering, topical map, pillar content strategy, content cluster plan, or content calendar from a keyword list.
Strategic SEO planning for new or existing websites. Industry-specific templates, competitive analysis, content strategy, and implementation roadmap. Use when user says "SEO plan", "SEO strategy", "SEO planning", "content strategy", "keyword strategy", "content calendar", "site architecture", or "SEO roadmap".
Search Experience Optimization: reads Google SERPs backwards to detect page-type mismatches, derives user stories from search intent signals, and scores pages from multiple persona perspectives. Identifies why well-optimized pages fail to rank by analyzing what Google rewards for each keyword. Use when user says "SXO", "search experience", "page type mismatch", "SERP analysis", "user story", "persona scoring", "why isn't my page ranking", "intent mismatch", or "wireframe".
Configure build-time JavaScript sourcemap uploads to Datadog using the Datadog build plugin (@datadog/esbuild-plugin, @datadog/rollup-plugin, @datadog/rspack-plugin, @datadog/vite-plugin, @datadog/webpack-plugin), so RUM and Error Tracking show un-minified stack traces with git metadata attached. Use when Datadog errors show minified stack traces, someone asks to “upload sourcemaps”, “set up sourcemaps”, “unminify errors”, or “configure the Datadog build plugin”, or a project has a bundler config and @datadog/browser-rum but no sourcemap upload.
Take one feature request from prompt to reviewed, audited delivery in a SINGLE autonomous pass — one runnable Workflow that plans, reviews the plan, builds task by task, cross-reviews the build, and optionally runs a launch audit, then RETURNS the verified findings rather than looping (the user decides any fix round; a Workflow can't ask mid-run, and an autonomous fix-loop is what caused multi-hour grinds). It chains the existing skills — plan-to-task-list-with-dag (a specialist agent per task) → plan-founder-review → a specialist engineer/reviewer build across the DAG → a full claude ∥ codex/kiro cross-review → go-live-audit — and FOLLOWS the graph strictly: every task declares its dependencies, layers must be a topological order, plan review blocks a mis-ordered graph, and a task NEVER builds before its dependencies integrate (no building on a broken base). Per-task review is SLICE-SCOPED (judges only that task's own change against its acceptance criteria, attributing whole-codebase end-state gaps to the task that owns them) so the build isn't drowned in false blocks, and impl review is the end-state gate. Gates FAIL CLOSED — a reviewer that didn't actually run is never counted clean, and a died codex/kiro reviewer falls back to a native review, never a fabricated block. Durable and resumable: each run writes a live status file (overall + per-phase + per-task) with a journal reader for status/stop/resume, and resume rebuilds integration state from GIT ground truth so an interrupted run never loses or re-loses already-landed tasks. Code writing and each review independently pick native / codex / kiro (reviews can skip; writer and reviewer can differ), so the user controls quality vs token cost.
Use this skill when the user asks to create or refactor a reusable Codex skill folder, including SKILL.md trigger wording, instruction contracts, rules, references, scripts, assets, and validation checks. Do not use for generic documentation that is not a skill.
Eight-stage PDCA workflow for systematically solving bugs, refactors, and feature-development tasks: clarify → analyze → explore solutions → review → plan → execute → verify → retrospect. Manual mode (default) pauses for user confirmation at each stage exit; auto mode runs end-to-end; ai-proxy overlay (thin freeze then occupy) is available independently. Triggers — 「明确问题」「分析问题」「探索方案」「审查方案」「制定计划」「执行计划」「检查验证」「复盘改进」(alias「回顾总结」)；「继续分析」「深入分析」「修改方案」「完善方案」「优化方案」「更新计划」「修订计划」「修改计划」；「自动模式」「自动分析」「自动解决」；「ai-proxy 模式」「AI 代理模式」「切换 ai-proxy」 / clarify problem, analyze problem, explore solutions, review solution, make plan, execute plan, verify, retrospective, auto mode, ai-proxy mode, switch to ai-proxy.
Specify plant models for closed-loop simulation: system specs, architecture, build plans, validation plans. Use when creating, updating, or reviewing plant model specifications, planning plant model architecture, or planning plant model validation.
Provides comprehensive guidance for Spring Security including authentication, authorization, OAuth2, JWT, and security best practices. Use when the user asks about Spring Security, needs to implement security in Spring applications, configure authentication, or work with security features.
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.