apify-ultimate-scraper
AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.
36
21%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/apify-ultimate-scraper/SKILL.mdQuality
Discovery
0%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 vague, uses internal jargon ('Actors'), and lacks both concrete actions and explicit trigger guidance. It reads more like marketing copy than a functional skill description, making it nearly impossible for Claude to distinguish this skill from others or know when to select it.
Suggestions
Replace vague 'data extraction from 55+ Actors across all major platforms' with specific actions and platforms, e.g., 'Scrapes product data from Amazon, Google search results, social media profiles from Twitter/Instagram, and job listings from 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 web page, crawl search results, or collect social media data.'
Remove marketing language like 'AI-driven' and '55+' and instead focus on the specific capabilities and file/data types this skill handles to reduce conflict risk with other data-related skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description says 'data extraction from 55+ Actors across all major platforms' but never specifies what concrete actions are performed (e.g., scrape product listings, extract reviews, download profiles). 'AI-driven data extraction' and 'automatically selects the best Actor' are vague and buzzword-heavy. | 1 / 3 |
Completeness | The 'what' is vaguely stated as 'data extraction' without specifics, and there is no 'when' clause or explicit trigger guidance. There is no 'Use when...' or equivalent, which per the rubric should cap completeness at 2 at best, but the 'what' is also too weak to merit a 2. | 1 / 3 |
Trigger Term Quality | The description lacks natural keywords a user would say. Terms like 'Actors', '55+', and 'all major platforms' are internal jargon or marketing language. It doesn't mention specific platforms (e.g., Amazon, Google, Twitter) or user-facing terms like 'scrape', 'web scraping', 'crawl', or 'extract data from website'. | 1 / 3 |
Distinctiveness Conflict Risk | 'Data extraction' is extremely generic and could conflict with any skill involving data retrieval, web scraping, API calls, or file parsing. Without naming specific platforms or use cases, this description provides no clear niche. | 1 / 3 |
Total | 4 / 12 Passed |
Implementation
42%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 for every workflow step, but severely suffers from poor progressive disclosure and conciseness. The bulk of the content is actor lookup tables that should be in separate reference files, making the main skill file unnecessarily bloated. The workflow is well-structured but lacks integrated validation/feedback loops between steps.
Suggestions
Move all actor lookup tables (Instagram, Facebook, TikTok, YouTube, Google Maps, Other, Use Case mapping, Multi-Actor Workflows) to a separate ACTORS_REFERENCE.md file and link to it from the main skill.
Add validation checkpoints in the workflow: after Step 4, explicitly check for run success/failure before proceeding to Step 5, and integrate the error handling as a feedback loop (e.g., 'If run fails, check error output → fix input → re-run').
Reduce the main SKILL.md to the workflow steps, the mcpc commands, and a brief summary of actor categories with a pointer to the reference file for actor selection.
Consider replacing the static actor tables with a dynamic approach that primarily uses the search-actors mcpc command, keeping only a small 'common use cases' quick-reference inline.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose with massive lookup tables listing 55+ actors that could easily be in a separate reference file. The actor tables, use case matrices, and multi-actor workflow tables consume enormous token budget. Claude doesn't need all of this inline—it could be in a referenced file and looked up on demand. | 1 / 3 |
Actionability | The skill provides fully executable bash commands for every step—fetching actor schemas, searching the store, running actors with different output formats. Commands are copy-paste ready with clear placeholder substitution patterns. | 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 actor (Step 4), there's no explicit check for errors or verification of output before proceeding to summarization. The error handling section exists but is separate from the workflow rather than integrated as feedback loops. | 2 / 3 |
Progressive Disclosure | This is a monolithic wall of content with ~200+ lines of actor lookup tables inline that should be in separate reference files. The skill would benefit enormously from moving actor tables to a REFERENCE.md and keeping only the workflow and a brief actor selection strategy in the main file. | 1 / 3 |
Total | 7 / 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.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- sickn33/antigravity-awesome-skills
- Commit
1a9f5ac
- Reviewed
Table of Contents
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