dig

作者: apify

用于在Apify MCP服务器上探索、规划和指定工作的灵活技能。请勿编辑源文件——此技能仅用于理解和规划。

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

来自 apify 的更多技能

bug-triage
apify
对 apify/apify-mcp-server 上的开放 bug 问题进行分类。分析、草拟回复、获取批准、发布。
official
apify-influencer-brand-collabs
apify
通过串联Apify Actors发现Instagram品牌与创作者的合作关系。当用户询问某品牌与谁合作、某创作者曾与哪些品牌进行付费合作时使用…
official
apify-financial-news
apify
发现并提取追踪的投资组合公司在33个经过验证的一级来源(彭博社、路透社、金融时报、华尔街日报、IntelliNews、捷克通讯社、波兰通讯社、保加利亚通讯社……)中的财经新闻。
official
apify-actor-development
apify
创建、调试和部署用于网页抓取、自动化及数据处理的无服务器云程序。支持JavaScript、TypeScript和Python模板,集成Crawlee、Playwright和Cheerio库,用于HTTP和基于浏览器的爬取。包含通过apify run进行的本地测试(使用隔离存储)、输入/输出的模式验证,以及通过apify push部署到Apify平台。需要Apify CLI认证,并在.actor/actor.json中强制包含generatedBy元数据以用于AI...
official
apify-actorization
apify
将现有项目转换为无服务器Apify Actors,支持语言特定的SDK集成。支持JavaScript/TypeScript(使用Actor.init() / Actor.exit())、Python(异步上下文管理器)以及通过CLI包装器的任何语言。提供结构化工作流:使用apify init搭建脚手架,应用SDK封装,配置输入/输出模式,通过apify run进行本地测试,然后使用apify push进行部署。包含输入和输出模式验证、Docker容器化以及可选的按事件付费...
official
apify-generate-output-schema
apify
通过分析Apify Actor的源代码生成输出模式(dataset_schema.json、output_schema.json、key_value_store_schema.json)。在以下情况下使用…
official
apify-ultimate-scraper
apify
自动化网页抓取工具,为55多个平台(包括Instagram、TikTok、YouTube、Facebook、Google Maps等)选择最优Actor。涵盖8大主流平台的55多个预配置Actor,并提供针对特定用例的选择指导(潜在客户生成、网红发现、品牌监控、竞争对手分析、趋势研究)。支持三种输出格式:快速聊天显示、CSV导出或JSON导出,可自定义结果数量限制。包含多Actor工作流模式,适用于复杂...
official
apify-audience-analysis
apify
从Facebook、Instagram、YouTube和TikTok提取受众人口统计、参与模式和行为数据。支持18+个专业Actor,涵盖所有四个平台的粉丝人口统计、参与指标、评论和资料分析。提供三种输出格式:快速聊天显示、CSV导出或JSON导出,用于下游分析。需要Apify令牌和mcpc CLI工具;使用动态模式获取来调整输入以适应每个Actor的要求。包括结构化...
official