Skills
100,344 packaged capabilities and playbooks for agents. Install any of them with one command.
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Usare quando l'utente chiede di documentare un repository, scrivere o rifare il README o il documento di un servizio, spiegare come si avvia in locale o come va in produzione, descrivere variabili d'ambiente, deploy e troubleshooting, allineare documentazione obsoleta al codice attuale, o segnala che nessuno sa più come far partire il progetto. Non per documentazione di prodotto, API reference generabile dal codice o testi commerciali.
Analyze dividend investing opportunities and assess dividend safety, growth potential, and yield. Use when the user asks about dividends, dividend payouts, income investing, or dividend yield. Supports quick screening, deep analysis, and portfolio optimization.
Diagnose and improve slow Django database-backed endpoints with evidence-first profiling, query-plan review, index selection, ORM loading fixes, batching, database-side computation, materialized views, and pagination choices. Use when a Django view, API, job, report, queryset, or database query is slow and the right optimization path is not obvious.
Process large Django querysets and write-heavy jobs with memory-safe reads, values/values_list, iterator chunking, set-based update/delete, bulk_create, bulk_update, F expressions, Func expressions, and batch sizing. Use when a Django command, task, migration, report, or loop reads or writes many rows and is slow, memory-heavy, or query-heavy.
Django security best practices, authentication, authorization, CSRF protection, SQL injection prevention, XSS prevention, and secure deployment configurations. Use when reviewing Django authentication, authorization, input handling, or deployment settings.
Design faster, clearer Django test data and test structure with factories, setUpTestData, SimpleTestCase/TestCase choices, fixture-file cleanup, query optimization, and unit-vs-integration boundaries. Use when Django tests create too much data, rely on slow fixtures, overuse TransactionTestCase, duplicate setup, or need refactoring for speed without losing coverage.
Create Behavior-Driven Development (BDD) scenarios - Layer 4 of the SDD flow, translating EARS requirements into executable Given-When-Then acceptance scenarios with req-to-SPEC trace links. Use after EARS, before ADR. Single-document authoring primitive; for end-to-end or batch generation the autopilot (`doc-bdd-autopilot`) drives this skill.
Review a Kibana PR for OpenAPI Spec (OAS) and API docs compliance against the Elastic API docs checklist, core guidelines, and Kibana quickstart. Checks availability, summaries, descriptions, parameters, examples, tags, links, defaults, enums, deprecation, and generated YAML correctness. Use when reviewing a Kibana PR that touches API routes or OAS output files, or when asked to check OAS, availability, x-state, or API docs compliance on a PR.
An intelligent tool that helps you quickly read documents, materials, images, and books and break them down, extract, and summarize them into mind maps, developed by ProcessOn. It can one-click break down, extract, and summarize natural language, Markdown, long text, PDF/Word documents, web content, image text, study materials, meeting minutes, work reports, and books and literature into clear, well-structured, editable ProcessOn professional mind maps. It is suited to scenarios such as summarizing documents, organizing materials, extracting key points, breaking down long texts, generating mind maps, turning articles into mind maps, organizing PDFs into mind maps, summarizing meeting minutes, extracting work reports, sorting out papers and literature, making reading notes, organizing study materials, generating learning paths, breaking down proposal outlines, and planning project tasks. It mainly addresses the problems of "content too long to finish reading, materials too scattered to organize, key points too unclear to extract, structure too messy to reuse". It supports outputting knowledge-organization graphics such as mind maps, logic diagrams, tree diagrams, fishbone diagrams, timelines, and table diagrams, which can be edited online, collaboratively modified, and efficiently reused in ProcessOn after generation. Note: This skill is not used to generate flowcharts, swimlane diagrams, sequence diagrams, system architecture diagrams, ER diagrams, Mermaid diagrams, or other process or technical charts. To automatically draw flowcharts, business processes, system architectures, or database relationship diagrams, please use ProcessOn's chart generation skills.
Use when needing to scrape documentation websites into markdown for AI context. Triggers on "scrape docs", "download documentation", "get docs for [library]", or creating local copies of online documentation. CRITICAL - always analyze sitemap first before scraping.
Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, foreign key constraints, OCC retry patterns, ORM migration (Django/EF Core/Hibernate/Rails/SQLAlchemy), DDL operations, query plan explainability, system diagnostics via CloudWatch AAS, SQL compatibility validation, and bulk data loading. Load before answering DSQL limit or error-code questions; it has limits docs omit. Triggers on phrases like: DSQL, Aurora DSQL, distributed SQL database, serverless PostgreSQL-compatible database, migrate to DSQL, DSQL query plan, DSQL EXPLAIN ANALYZE, DSQL ENUM, DSQL foreign key, DSQL OCC retry, DSQL multi-region, DSQL JSONB, DSQL GIN index, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader, DSQL slow, DSQL performance, DSQL wait events, DSQL AAS.
Generate D2 database diagrams from Entity Framework Core models. USE FOR: EF Core database diagram, Entity Framework Core ERD, DbContext diagram, C# entity relationship diagram, PostgreSQL schema visualization, generate .d2 file from EF Core entities, Fluent API mapping diagram, migrations-based database diagram, table relationships, owned types, many-to-many join tables, indexes and constraints. DO NOT USE FOR: runtime debugging, database migration execution, schema deployment, SQL performance tuning, or draw.io diagrams.
Build, review, debug, plan, or migrate Effect 4.x (stable LTS, [email protected]) TypeScript applications. Use for Effect services, Schema, layers, Config, concurrency, streams, HTTP/SQL/AI/CLI integrations (effect/http, effect/http-api, effect/sql, effect/ai), @effect/vitest, v3-to-v4 migrations, and v4 RC-to-4.0 upgrades (effect/unstable/* path removal). Enforce consistent Effect usage across application logic, with thin adapters for non-Effect frameworks and libraries.
Penetration testing efficiency rules. Prevent inefficient behaviors such as blind enumeration and tool blocking, and ensure the maximum number of vulnerabilities is found within a limited time. Use when starting a penetration test, when a scanning tool is taking too long, when stuck in a blind brute-force loop, or when you need to decide the next attack direction. Recommended reading before each round of penetration testing
Exploits privileged pods, host mounts, runC CVEs, and exposed Docker sockets to break out of a container and reach the underlying host during an authorized container-security assessment. Use when executing an approved breakout test, demonstrating the real impact of a privileged or hostPath workload, or validating that escape mitigations actually hold. Keywords: container breakout, privileged, hostPath, docker.sock, runC CVE-2019-5736, CVE-2024-21626, release_agent, nsenter. Do not use for defensive detection of these techniques - use detecting-container-escape-attempts.
This skill should be used when setting up code quality tooling with ESLint v9 flat config, Prettier formatting, Husky git hooks, lint-staged pre-commit checks, and GitHub Actions CI lint workflow. Apply when initializing linting, adding code formatting, configuring pre-commit hooks, setting up quality gates, or establishing lint CI checks for Next.js or React projects.
Gather the smallest set of repository, environment, documentation, and runtime evidence needed before planning or changing code. Use when load-bearing facts are unknown, tracing execution paths, inspecting unfamiliar packages, or resolving codebase contradictions — even if the user does not explicitly say "fable-discover" (e.g. "explore the codebase", "how does this work", "where is this implemented", "find where this route is handled"). Do NOT use when the bounded edit is already known (use fable-execute), for external API/version research (use fable-research), or for running existing test suites (use fable-verify).
Implement one accepted, bounded work card with immediate local verification, invariant preservation, and zero scope drift. Use when executing a planned work card, applying a well-defined code change, implementing an isolated function, or performing targeted single-scope edits — even if the user does not explicitly say "fable-execute" (e.g. "implement this card", "write the code for this step", "apply the agreed changes", "build this component"). Do NOT use when new architectural decisions are required (use fable-plan) or when tests are repeatedly failing (use fable-recover).
Convert discovery evidence into bounded, testable work cards with explicit acceptance criteria and architectural invariants. Use when designing multi-file features, planning complex refactors, decomposing architectural migrations, or structuring multi-step implementation tasks — even if the user does not explicitly say "fable-plan" (e.g. "plan this feature", "design the architecture", "break this task down", "create an implementation plan"). Do NOT use when load-bearing facts remain unknown (use fable-discover first) or for trivial single-scope bug fixes (use fable-tdd or fable-execute).
Diagnose repeated command failures, stale build caches, branch drift, or contradictory evidence before attempting further code edits. Use when commands fail repeatedly, tests stay red after attempted fixes, build output contradicts source code, or the execution path is confused — even if the user does not explicitly say "fable-recover" (e.g. "still failing", "why did this fail again", "stuck in a failure loop", "diagnose this error"). Do NOT use for routine first-time test failures in fresh TDD (use fable-tdd).
Falsify software implementations and gather fresh, machine-checked acceptance proof across tests, builds, typechecks, and runtime smoke checks before completion. Use when running test suites, checking type correctness, validating acceptance criteria, or verifying post-mutation code integrity — even if the user does not explicitly say "fable-verify" (e.g. "verify my changes", "run all tests and typecheck", "check if everything passes", "prove this works"). Do NOT use for planning (use fable-plan) or diff code review (use fable-review).
Retrieves a Jira ticket or a GitHub issue into docs/<KEY>.md as a read-only, validated Markdown snapshot for downstream workflow phases. Use when a Jira URL, a GitHub issue URL, or owner/repo/number coordinates need the Phase 1 fetch-work-item step. Detects the platform from the input and loads the matching playbook just-in-time.
Translates Figma designs into production-ready application code with 1:1 visual fidelity. Use when implementing UI code from Figma files, when user mentions "implement design", "generate code", "implement component", provides Figma URLs, or asks to build components matching Figma specs. For Figma canvas writes via `use_figma`, use `figma-use`.
Extract language learning content from web pages and create structured flashcards in markdown format for spaced repetition study (Anki decks). Works with ANY language (Spanish, Japanese, Arabic, Chinese, etc.). Use when the user asks to create flashcards, flash cards, study cards, Anki decks, or mentions spaced repetition from online content. Supports grammar lessons, vocabulary, and exercises. Converts to Anki-importable format.
Mac fleet inventory and upkeep with full/worker profiles: collect installed apps and packages, compare desired versus observed state, audit local-account escrow references, update Homebrew/global packages, safely sync repos and Xcode, and report disk, service, backup, update, and security health.
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.