apify-ultimate-scraper

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

Quality

53%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

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

Quality

Content

65%Weight 40%Scale 1-5

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

The body is a well-structured, highly actionable orchestration guide with concrete commands and a clear 5-step workflow, but it inlines a large actor reference catalog and lacks a validation checkpoint for its batch operations. Strong on actionability and conciseness, weaker on workflow safety and file decomposition.

Suggestions

Add an explicit validation checkpoint in Step 4/5 (e.g. verify the output file is non-empty and well-formed before summarizing, and re-run on failure) to lift workflow_clarity past the batch-operation cap.

Move the 55-actor catalog tables into a references file (e.g. references/actors.md) and keep SKILL.md as an overview with use-case selection guidance, improving progressive disclosure.

Provide one concrete JSON_INPUT example for a representative actor so the run commands are fully copy-paste ready.

Dimension	Reasoning	Score
Conciseness	The body is dense and actionable with no concept-explanation padding Claude doesn't need; only minor waste (the repeated description line, 'No need to check it upfront', 'Based on character of use case') keeps it from a 5.	4 / 5
Actionability	Provides concrete, copy-paste bash commands for quick/CSV/JSON outputs with real actor IDs and mcpc flags; the minor gap is that 'JSON_INPUT' is never concretely exemplified despite being a key placeholder.	4 / 5
Workflow Clarity	A clear 5-step sequenced checklist is present, but this is a batch data-extraction operation with no validation/verification checkpoint before summarizing results, which caps workflow_clarity at 3 per the rubric scoring notes.	3 / 5
Progressive Disclosure	Section structure is good (per-platform headers, use-case and multi-actor tables), but the full 55-actor reference catalog is inlined in SKILL.md with no in-bundle reference files to offload it, so content that could be separate stays inline.	3 / 5
	Total	14 / 20 Passed

Description

42%Weight 40%Scale 1-5

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

The description conveys a clear 'what' but relies on Apify jargon ('Actors') and generic language ('all major platforms') rather than natural user trigger phrases, and it entirely lacks a 'when to use' clause. It is functional but not distinctive or trigger-rich.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the user wants to scrape or extract data from social platforms (Instagram, Facebook, TikTok, YouTube, Google Maps) or search/review sites.'

Replace jargon/generic phrasing with natural keywords users say: name the platforms and use verbs like 'scrape', 'extract', 'export'.

Drop the 'AI-driven' filler and the redundant second sentence to tighten specificity.

Dimension	Reasoning	Score
Specificity	Names the domain ('data extraction from 55+ Actors across all major platforms') and 1-2 concrete actions ('extraction', 'selects the best Actor'), but coverage is not comprehensive and 'AI-driven' is generic fluff.	3 / 5
Completeness	The 'what' is clear (data extraction across platforms via Actors) but there is no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the rubric guidelines.	3 / 5
Trigger Term Quality	'Actors' is Apify-specific jargon and 'all major platforms' is generic; it omits the natural phrases users actually say (platform names like Instagram/TikTok, or 'scrape/extract from X'), so only one or two generic keywords are present.	2 / 5
Distinctiveness Conflict Risk	The 'Actor' terminology gives a modest Apify-niche signal, but 'all major platforms' is broad and would overlap with other scraping/data-extraction skills, so it is only somewhat distinct.	3 / 5
	Total	11 / 20 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: plugins/AI-Agents-Safe-Coding-Skills/skills/apify-ultimate-scraper/SKILL.md
Commit: 900c504

Reviewed: 6 hours ago

Table of Contents

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