Apify Ultimate Scraper Agent Skill

Universal web scraper

AI-driven data extraction from ~100 Actors across 15+ platforms via the Apify CLI.

Rule: Pass --json and redirect stderr with 2>/dev/null on data-returning commands (actors call, actors start, actors info, actors search, datasets get-items, runs info). JSON output is stable across CLI versions. stderr contains progress messages and version warnings that break JSON parsers if not redirected.

This rule does not apply to status/auth commands (apify info, apify --version, apify login). For those, use 2>&1 so authentication and version errors are visible.

Exception: if --input returns no data, re-run with 2>&1 to confirm whether the cause is a missing schema vs. a network/auth error.

Prerequisites

• Apify CLI v1.4.0+ (npm install -g apify-cli)
• Authenticated session (see below)

Authentication

If a CLI command fails with an auth error, authenticate using one of these methods:

• OAuth (interactive): apify login (opens browser)
• Environment variable: export APIFY_TOKEN=your_token_here
• From .env file: source .env (if the file contains APIFY_TOKEN=...)

Generate token: https://console.apify.com/settings/integrations

Workflow

Step 0: Verify CLI readiness before doing anything else

Before using the Apify CLI, always verify the local environment:

• Check that the CLI is installed:

    apify --help

If this fails, install the CLI first:

       npm install -g apify-cli

• Check that the CLI is authenticated:

    # Auth check — do NOT pipe to /dev/null, you need to see errors
    apify info 2>&1

If this shows the user is not logged in, instruct them to authenticate with a token:

    apify login --token TOKEN

• Run Apify CLI commands with all permissions when needed by the agent sandbox.

• Assume many Apify commands block with zero output until completion. For blocking runs, set block_until_ms to at least 60000.

• For long or unknown-duration runs, prefer the async pattern:

    apify actors start "ACTOR_ID" -i 'JSON_INPUT' --json 2>/dev/null

Then poll the run status:

    apify info actor-runs/RUN_ID --json

Check .status for SUCCEEDED or FAILED.

Step 1: Understand goal and select Actor

Identify the target platform and use case. Read references/actor-index.md to find the right Actor.

Prefer apify-tier actors; use community-tier only when no apify actor covers the task.

For input schemas, fetch dynamically: apify actors info "ACTOR_ID" --input --json 2>/dev/null

If the output is empty, re-run without the redirect (2>&1) to surface auth or network errors before proceeding.

If the task involves a multi-step pipeline, also read the matching workflow guide:

| Task involves... | Read |

|-----------------|------|

| leads, contacts, emails, B2B | references/workflows/lead-generation.md |

| competitor, ads, pricing | references/workflows/competitive-intel.md |

| influencer, creator | references/workflows/influencer-vetting.md |

| brand, mentions, sentiment | references/workflows/brand-monitoring.md |

| reviews, ratings, reputation | references/workflows/review-analysis.md |

| SEO, SERP, crawl, content, RAG | references/workflows/content-and-seo.md |

| analytics, engagement, performance | references/workflows/social-media-analytics.md |

| trends, keywords, hashtags | references/workflows/trend-research.md |

| jobs, recruiting, candidates | references/workflows/job-market-and-recruitment.md |

| real estate, listings, hotels | references/workflows/real-estate-and-hospitality.md |

| price monitoring, e-commerce, products | references/workflows/ecommerce-price-monitoring.md |

| contact enrichment, email extraction | references/workflows/contact-enrichment.md |

| knowledge base, RAG, LLM data feed | references/workflows/knowledge-base-and-rag.md |

| company research, due diligence | references/workflows/company-research.md |

If no Actor matches in the index, search dynamically:

apify actors search "KEYWORDS" --json --limit 10 2>/dev/null

From results: items[].username/items[].name (Actor ID), items[].title, items[].stats.totalUsers30Days, items[].currentPricingInfo.pricingModel.

Step 2: Fetch Actor schema and check gotchas

Some Actors don't register an input schema with the platform (their schema lives in code). Try schema sources in this order — fall through on empty/error:

• Input schema (human-readable):

    apify actors info "ACTOR_ID" --input 2>/dev/null

If output is Error: No input schema found for this Actor, skip to source 2.

• Input schema (JSON keys only):

    apify actors info "ACTOR_ID" --input --json 2>/dev/null | jq '.input.schema.properties // empty | keys'

Empty result means no registered schema — fall through to source 3. To drill into a specific field:

    apify actors info "ACTOR_ID" --input --json 2>/dev/null | jq '.input.schema.properties.FIELD_NAME'

• README fallback (always works, contains usage examples):

    apify actors info "ACTOR_ID" --readme 2>/dev/null

Grep the README for an "Input" / "Example input" section to copy the JSON shape.

• Last resort — call with minimal known input (e.g. {"startUrls":[{"url":"..."}]} for crawlers) and let the Actor surface validation errors that reveal required fields. See references/gotchas.md for known-good minimal inputs for common Actors.

Also read references/gotchas.md to check for common pitfalls and cost guardrails for the selected Actor.

Step 3: Configure and run

Skip user preferences for simple lookups (e.g., "Nike's follower count"). Go straight to running with quick answer mode.

For larger tasks, confirm output format (quick answer / CSV / JSON) and result count.

Before starting the run, double-check whether the task is short enough for a blocking call or should use the async pattern from Step 0.

Standard run (blocking):

    apify actors call "ACTOR_ID" -i 'JSON_INPUT' --json 2>/dev/null

From output: .id (run ID), .status, .defaultDatasetId, .stats.durationMillis

Fetch results:

    apify datasets get-items DATASET_ID --format json

For CSV: apify datasets get-items DATASET_ID --format csv

Quick answer mode: Fetch results as JSON, pick top 5, present formatted in chat.

Save to file: Fetch results, use Write tool to save as YYYY-MM-DD_descriptive-name.csv or .json.

Large/long-running scrapes:

    apify actors start "ACTOR_ID" -i 'JSON_INPUT' --json 2>/dev/null

Poll: apify info actor-runs/RUN_ID --json (check .status for SUCCEEDED or FAILED).

Step 4: Deliver results

Report: result count, file location (if saved), key data fields, and links:

• Dataset: https://console.apify.com/storage/datasets/DATASET_ID
• Run: https://console.apify.com/actors/runs/RUN_ID

For multi-step workflows: suggest the next pipeline step from the workflow guide.

Troubleshooting

Common errors and pitfalls are documented in references/gotchas.md. Read it before running PPE (pay-per-event) Actors.