OpenClaw

apify

Run any Apify Actor to scrape web data (Instagram, TikTok, Reddit, Twitter, etc). Handles Actor discovery, quality filtering, probe testing, batched execution, and result collection. Use when user asks to scrape/crawl/extract data from websites or social media platforms, or mentions Apify directly.

duxj45200 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install duxj4520/apify-runner

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

Apify Skill

Run any Apify Actor through a standardized workflow: search → validate → execute → collect results.

Prerequisites

APIFY_TOKEN env var, or a config.json with tokens (copy config.json.example)
Python 3 with requests installed

Workflow

Step 1: Parse User Intent

Extract from the user's request:

Platform/target (Instagram, TikTok, Reddit, etc.)
What to scrape (posts, profiles, hashtags, comments, etc.)
Targets (URLs, usernames, keywords)
Quantity/filters (how many, time range, min likes, etc.)

Step 2: Select Token

If user specifies a token name or the task maps to a specific account, use that. Otherwise use default.

Token can be provided via:

--token flag (highest priority)
config.json tokens map (by --token-name)
APIFY_TOKEN env var (fallback)

Step 3: Search & Select Actor

Run the search script:

python3 scripts/search_actor.py "instagram scraper" --top 3

Output: ranked candidates with score, success rate, rating, pricing model.

Quality filters (built into script):

notice = NONE (not deprecated)
30-day success rate ≥ 95%
30-day runs ≥ 1,000
User rating ≥ 4.0

Pick the top-ranked candidate. If user has a preference or prior experience with a specific Actor, skip search.

Step 4: Get Actor Schema & Build run_input

Fetch the Actor's documentation:

web_fetch https://apify.com/{actor_id}.md

Read the input schema section. Construct run_input JSON based on:

The Actor's required/optional fields
The user's targets and filters
Sensible defaults from the documentation

Do NOT ask the user to write JSON. Build it from their natural language request.

Step 5: Probe Test (Top 1 → Top 2 → Top 3 fallback)

Test with minimal input before committing to full run:

python3 scripts/apify_runner.py {actor_id} \
  --input '{...}' \
  --token {token} \
  --probe-only \
  --list-key {key}

The probe automatically uses the fi...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/duxj4520/apify-runner
Version: —
Commit: 90a144cfcc75e8eed8c9d1d685c3031c3ec5a1f3
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# Apify Skill

Run any Apify Actor through a standardized workflow: search → validate → execute → collect results.

## Prerequisites

- `APIFY_TOKEN` env var, or a `config.json` with tokens (copy `config.json.example`)
- Python 3 with `requests` installed

## Workflow

### Step 1: Parse User Intent

Extract from the user's request:
- **Platform/target** (Instagram, TikTok, Reddit, etc.)
- **What to scrape** (posts, profiles, hashtags, comments, etc.)
- **Targets** (URLs, usernames, keywords)
- **Quantity/filters** (how many, time range, min likes, etc.)

### Step 2: Select Token

If user specifies a token name or the task maps to a specific account, use that. Otherwise use `default`.

Token can be provided via:
1. `--token` flag (highest priority)
2. `config.json` tokens map (by `--token-name`)
3. `APIFY_TOKEN` env var (fallback)

### Step 3: Search & Select Actor

Run the search script:

```bash
python3 scripts/search_actor.py "instagram scraper" --top 3
```

Output: ranked candidates with score, success rate, rating, pricing model.

**Quality filters (built into script):**
- `notice` = NONE (not deprecated)
- 30-day success rate ≥ 95%
- 30-day runs ≥ 1,000
- User rating ≥ 4.0

Pick the top-ranked candidate. If user has a preference or prior experience with a specific Actor, skip search.

### Step 4: Get Actor Schema & Build run_input

Fetch the Actor's documentation:

```bash
web_fetch https://apify.com/{actor_id}.md
```

Read the input schema section. Construct `run_input` JSON based on:
- The Actor's required/optional fields
- The user's targets and filters
- Sensible defaults from the documentation

**Do NOT ask the user to write JSON.** Build it from their natural language request.

### Step 5: Probe Test (Top 1 → Top 2 → Top 3 fallback)

Test with minimal input before committing to full run:

```bash
python3 scripts/apify_runner.py {actor_id} \
  --input '{...}' \
  --token {token} \
  --probe-only \
  --list-key {key}
```

The probe automatically uses the fi...

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