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apify-ultimate-scraper

AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.

28

Quality

21%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/apify-ultimate-scraper/SKILL.md

The canonical home for this skill is apify-ultimate-scraper in sickn33/agentic-awesome-skills

SKILL.md
Quality
Evals
Security

Quality

Content

42%Scale 1-3

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The skill provides excellent actionability with concrete, executable commands and a clear workflow structure. However, it severely suffers from poor progressive disclosure and conciseness—the massive inline Actor lookup tables (which are pure reference data) dominate the file and bury the actual instructional content. Moving reference tables to separate files and integrating validation into the workflow would dramatically improve this skill.

Suggestions

Move all Actor lookup tables (Instagram, Facebook, TikTok, YouTube, Google Maps, Other, use-case mapping, multi-actor workflows) to a separate reference file like `ACTORS.md` and link to it from SKILL.md.

Add a validation checkpoint after Step 4 (e.g., check exit code, verify output file exists and is non-empty, handle the case where 0 results are returned) before proceeding to Step 5.

Integrate error handling into the workflow steps rather than listing it as a separate section—e.g., after Step 4, include 'If run fails, check error output and see Error Handling below.'

Reduce the SKILL.md to the workflow steps, prerequisites, and brief Actor selection guidance (e.g., 'See [ACTORS.md](ACTORS.md) for the full catalog of 55+ Actors by platform and use case').

DimensionReasoningScore

Conciseness

The skill is extremely verbose, with massive Actor lookup tables (55+ actors across ~100 lines of tables) that could be offloaded to a reference file. The use-case mapping tables and multi-actor workflow tables add further bulk. Most of this content is reference data, not instructional content, and bloats the SKILL.md significantly.

1 / 3

Actionability

The skill provides fully executable bash commands for every step—fetching schemas, searching actors, and running scripts with exact CLI syntax for CSV, JSON, and quick-answer modes. Commands are copy-paste ready with clear placeholder substitution.

3 / 3

Workflow Clarity

The 5-step workflow is clearly sequenced with a progress checklist, but there are no validation checkpoints between steps. After running the script (Step 4), there's no explicit error-checking or validation step before summarizing results—the error handling section is separate and not integrated into the workflow flow.

2 / 3

Progressive Disclosure

This is a monolithic wall of text with all 55+ Actor tables inline. The Actor reference tables, use-case mappings, and multi-actor workflows should be in separate reference files, with SKILL.md providing a concise overview and links. The content that matters (the workflow steps) is buried among hundreds of lines of lookup tables.

1 / 3

Total

7

/

12

Passed

Description

0%Scale 1-3

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This description is too vague and relies on insider jargon ('Actors') without explaining what it means or listing specific capabilities. It fails to provide concrete actions, natural trigger terms, or explicit guidance on when Claude should select this skill. The description would be nearly useless for skill selection among a large pool of available skills.

Suggestions

Replace vague 'data extraction from 55+ Actors across all major platforms' with specific actions and platform names, e.g., 'Scrapes and extracts data from websites including Amazon, Twitter, Google Maps, LinkedIn using Apify Actors.'

Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks to scrape a website, extract data from a platform, crawl pages, or collect structured data from the web.'

Define what 'Actors' means in context (e.g., 'Apify Actors') so the description is self-contained and doesn't rely on assumed knowledge.

DimensionReasoningScore

Specificity

The description says 'data extraction from 55+ Actors across all major platforms' but doesn't list any concrete actions beyond the vague 'data extraction.' It doesn't specify what kind of data, what platforms, or what specific operations are performed. 'Automatically selects the best Actor' is also vague.

1 / 3

Completeness

The 'what' is vaguely stated as 'data extraction' without specifics, and there is no explicit 'when' clause or trigger guidance. There is no 'Use when...' statement or equivalent to help Claude know when to select this skill.

1 / 3

Trigger Term Quality

The description uses domain-specific jargon ('Actors', '55+ Actors') that users would not naturally say. It lacks natural trigger terms like specific platform names (e.g., 'scrape', 'web scraping', 'Amazon', 'Twitter'), file types, or common user phrases. 'All major platforms' is too generic to serve as a trigger.

1 / 3

Distinctiveness Conflict Risk

'Data extraction' and 'all major platforms' are extremely generic phrases that could overlap with many other skills involving data processing, web scraping, API calls, or platform integrations. Nothing in the description carves out a clear, distinct niche.

1 / 3

Total

4

/

12

Passed

Validation

90%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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