dig

por apify

Habilidade flexível para explorar, planejar e especificar trabalhos no servidor Apify MCP. NÃO edite arquivos de origem — esta habilidade é apenas para entendimento e planejamento.

npx skills add https://github.com/apify/apify-mcp-server --skill dig

Dig

Flexible skill for exploring, planning, and speccing work on the Apify MCP server. Do NOT edit source files — this skill is for understanding and planning only.

Step 0: Parse arguments and determine intent

$ARGUMENTS contains the user's request and optional repo path overrides.

Flags (optional):

FlagDefaultPurpose
--sdk../typescript-sdkMCP SDK source repo path
--ext-apps../ext-appsMCP Apps SDK source repo path
--internal../apify-mcp-server-internalInternal server repo path

Everything not matching a flag is the user's request.

Resolution order for source repos: flag path → default sibling path → node_modules/ (compiled types only) → GitHub URL (last resort). Always verify the path exists before using it.

Determine the intent

Infer the intent from the user's natural language. There are three modes:

IntentWhat you doExamples
ExploreRead code, explain findings, answer questions"how does tool naming work", "look at the widget code", "why is this broken", "what would break if we change X"
PlanEnter plan mode, design the approach, assess impact"plan implementing resource links", "figure out how to refactor metadata", "design the simplification"
SpecPlan + create GitHub issues"write an issue for X", "create a spec for Y", "spec out resource links"

Rules:

  • Default to Explore. When in doubt, do less — the user can always ask for more.
  • Only enter plan mode for Plan and Spec.
  • Only create GitHub issues for Spec.

Step 1: Explore

Read the relevant source files and explain your findings. This is the baseline for all intents.

What to do:

  1. Read the relevant source files in this repo
  2. Check similar existing features as reference
  3. Only check the internal repo, MCP SDK/spec, or MCP Apps SDK/spec if the user's question touches those areas
  4. If you spot a related open issue, mention it casually — but don't go searching for issues unless it's relevant

Stop here if the intent is Explore.

Step 2: Plan (Plan and Spec only)

Use the EnterPlanMode tool, then design the approach — and commit to one. If the request is genuinely ambiguous (e.g. it maps to two different existing knobs), surface the fork up front and ask; otherwise pick the most defensible design and commit rather than listing options.

Investigate first:

  1. Assess internal repo impact (check ../apify-mcp-server-internal if available)
  2. Check MCP spec/SDK if the feature involves protocol behavior
  3. Check MCP Apps spec/SDK if it involves widgets or interactive UIs
  4. Use mcpc @stdio tools-call to probe current behavior if useful (requires pnpm run build)
  5. Read the repo's convention docs — follow them, don't reinvent

Conventions live in the repo, not here. AGENTS.md, CONTRIBUTING.md, and DEVELOPMENT.md hold the naming, validation, test-layout, and public/internal-separation rules — read them. Design minimally: reuse and adjust before adding.

Design output — required sections. A plan has to be implementable by someone else, so produce:

  1. Approach — the chosen design in a few sentences and the main trade-off you accepted.
  2. Files to change — each file to create or modify with a one-line note on what changes (path + change; no line numbers needed).
  3. Interfaces — the key function/type signatures you add or change (names, params, returns), so the implementer and reviewers know the contract.
  4. Test strategy (the oracle) — which unit/integration tests prove it, where they live, and what each asserts. This is how "done" is judged — never leave it implicit.
  5. Risks & impact — edge cases, and apify-mcp-server-internal impact.
  6. Data flow — only when non-trivial: entry → transforms → output.

Sections 2–4 must be concrete (real files, signatures, and tests — no "TBD", no "add error handling"). Sections 1, 5, 6 stay brief. This structure is for Plan; Explore and Spec stay lean.

Stop here if the intent is Plan. Exit plan mode with ExitPlanMode.

Step 3: Spec (Spec only)

Create GitHub issues. First exit plan mode with ExitPlanMode.

Check existing issues

Search for duplicates and related issues:

gh issue list -R apify/apify-mcp-server --search "<keywords>" --json number,title,state
gh issue list -R apify/ai-team --search "<keywords>" --json number,title,state
gh issue list -R apify/apify-mcp-server-internal --search "<keywords>" --json number,title,state

If a matching issue exists, update it with gh issue edit instead of creating a new one.

Create issues

One issue per implementation phase. A phase = one PR-sized unit of work (~50-200 lines changed). Each issue should be independently implementable.

Use the repo's feature_spec.yml template. Only the Problem and Proposed solution fields are required. Include Plan and Alternatives considered only when they add real value. No fluff, no filler — straight to the point.

## Problem
[Concrete evidence: error messages, user reports, issue links. Not "users are confused" — instead "3 users reported X in #channel".]

## Proposed solution
[Short. Reference existing code paths. List files inline if needed.]

## Plan
- [ ] Step 1
- [ ] Step 2

## Alternatives considered
[Only if you actually evaluated other approaches.]

Style (Explore explanations and Spec issues):

  • Plain language, no fluff — see CLAUDE.md § Communication style
  • Skip any section that would be empty or generic
  • 10-30 lines, not 100
  • Concrete steps > prose

(A Plan's design follows its required-sections structure above — keep each section tight, but don't truncate files / interfaces / test strategy to hit a line count.)

Self-review before presenting:

  • Is this the minimal design? Could scope be smaller?
  • Am I reusing existing patterns or reinventing?
  • Could this adjust existing code rather than add new code?
  • Does it require refactoring first? If so, that's a separate issue.

Present issue content to the user for review before creating. Use gh issue create with t-ai label.

Available resources

ResourcePath / URLUse for
Public repo. (this repo root)Main codebase — tools, widgets, tests
Internal repo../apify-mcp-server-internal (if available)Hosted server — assess impact of changes
MCP SDK (types)node_modules/@modelcontextprotocol/sdkProtocol types, server/client APIs (compiled only)
MCP SDK (source)../typescript-sdk (if available)Examples, tests, full source — faster than GitHub
MCP spechttps://modelcontextprotocol.io/specification/2025-11-25Protocol-level features
MCP Apps SDK (types)node_modules/@modelcontextprotocol/ext-appsMCP Apps types, React hooks, server helpers (compiled only)
MCP Apps SDK (source)../ext-apps (if available)Examples, tests, spec, full source — faster than GitHub
MCP Apps spechttps://github.com/modelcontextprotocol/ext-apps/blob/main/specification/2026-01-26/apps.mdxMCP Apps extension specification
Dev server (no UI)http://localhost:3001/ / tools: mcp__apify-dev__*Test tools without widgets
Dev server (UI)http://localhost:3001/?ui=true / tools: mcp__apify-dev-ui__*Test tools with widget rendering
mcpc stdiomcpc @stdio tools-call ... (requires pnpm run build)Test tools — no running server needed

Mais skills de apify

bug-triage
apify
Trie os bugs abertos no apify/apify-mcp-server. Analise, rascunhe respostas, obtenha aprovação, publique.
official
apify-influencer-brand-collabs
apify
Descubra parcerias entre marcas e criadores no Instagram encadeando Apify Actors. Use quando o usuário perguntar quem colabora com uma marca, quais marcas um criador fez conteúdo pago…
official
apify-financial-news
apify
Descubra e extraia notícias financeiras para empresas de portfólio monitoradas em 33 fontes verificadas Tier 1 (Bloomberg, Reuters, FT, WSJ, IntelliNews, ČTK, PAP, BTA,…
official
apify-actor-development
apify
Crie, depure e implante programas serverless em nuvem para web scraping, automação e processamento de dados. Suporta templates em JavaScript, TypeScript e Python com as bibliotecas integradas Crawlee, Playwright e Cheerio para crawling baseado em HTTP e navegador. Inclui testes locais via apify run com armazenamento isolado, validação de esquema para entradas/saídas e implantação na plataforma Apify via apify push. Requer autenticação da CLI Apify e metadados obrigatórios generatedBy em .actor/actor.json para IA...
official
apify-actorization
apify
Converta projetos existentes em Apify Actors serverless com integração de SDK específica para cada linguagem. Suporta JavaScript/TypeScript (com Actor.init() / Actor.exit()), Python (gerenciador de contexto assíncrono) e qualquer linguagem via wrapper CLI. Fornece fluxo de trabalho estruturado: apify init para estruturar, aplicar encapsulamento do SDK, configurar esquemas de entrada/saída, testar localmente com apify run e depois implantar com apify push. Inclui validação de esquemas de entrada e saída, conteinerização Docker e opcional pay-per-event...
official
apify-generate-output-schema
apify
Gere esquemas de saída (dataset_schema.json, output_schema.json, key_value_store_schema.json) para um Apify Actor analisando seu código-fonte. Use quando…
official
apify-ultimate-scraper
apify
Raspador web automatizado que seleciona os melhores Atores para mais de 55 plataformas, incluindo Instagram, TikTok, YouTube, Facebook, Google Maps e outras. Abrange mais de 55 Atores pré-configurados em 8 plataformas principais, com orientação de seleção específica para cada caso de uso (geração de leads, descoberta de influenciadores, monitoramento de marca, análise de concorrentes, pesquisa de tendências). Suporta três formatos de saída: exibição rápida no chat, exportação CSV ou exportação JSON com limites de resultados personalizáveis. Inclui padrões de fluxo de trabalho com múltiplos Atores para cenários complexos...
official
apify-audience-analysis
apify
Extraia dados demográficos do público, padrões de engajamento e comportamento do Facebook, Instagram, YouTube e TikTok. Suporta mais de 18 Atores especializados que abrangem dados demográficos de seguidores, métricas de engajamento, comentários e análise de perfil em todas as quatro plataformas. Oferece três formatos de saída: exibição rápida no chat, exportação CSV ou exportação JSON para análise posterior. Requer token Apify e ferramenta CLI mcpc; usa busca dinâmica de esquemas para adaptar entradas aos requisitos de cada Ator. Inclui estruturado...
official