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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.

50

Quality

55%

Does it follow best practices?

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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-claude/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

57%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 well-structured and actionable with concrete commands and a clear 5-step workflow, but it inlines a large actor catalog that belongs in a reference file and lacks an explicit validation checkpoint for its batch scraping operations.

Suggestions

Move the 55-actor catalog (and possibly the use-case/multi-actor tables) into a references/ file (e.g., ACTORS.md) and keep SKILL.md as an overview pointing to it, improving both conciseness and progressive disclosure.

Add an explicit validation checkpoint in the workflow between running the scraper and summarizing results (e.g., verify result count and inspect sample rows before reporting) to lift workflow_clarity above the batch-operation cap of 3.

Confirm the run_actor.js script is present in the bundle (references/scripts/) and reference it via a clearly signaled local path so the progressive-disclosure structure is real rather than pointing only at ${CLAUDE_PLUGIN_ROOT}.

DimensionReasoningScore

Conciseness

The content is mostly efficient with dense, non-padded tables and no irrelevant concept explanations, but the full 55-actor catalog is inlined in SKILL.md and could be tightened or moved to a reference file. Not a 4 because the inlined catalog is a notable chunk of tokens that does not all earn its place in the overview.

3 / 5

Actionability

Provides concrete, executable bash and node commands with real flags (e.g., `mcpc --json ... tools-call fetch-actor-details`, `node --env-file=.env .../run_actor.js --actor ... --input ... --format csv`). Not a 5 because commands rely on placeholders (ACTOR_ID, JSON_INPUT, SEARCH_KEYWORDS) and an external ${CLAUDE_PLUGIN_ROOT} script path rather than being fully copy-paste ready.

4 / 5

Workflow Clarity

A clear 5-step sequenced workflow with a copyable checklist is present, but this is a batch data-extraction skill and there is no explicit validation/verification checkpoint (e.g., inspect output before reporting) in the flow, which caps workflow_clarity at 3 per the rubric scoring notes. Not a 4 because the missing validation checkpoint is a required gating element for batch operations.

3 / 5

Progressive Disclosure

Good section structure with clear headers and use-case tables, but the 55-actor catalog that clearly belongs in a separate reference file is inlined, and the referenced run_actor.js lives outside this bundle (no local references/scripts/assets exist), so references are not clearly signaled as local files. Not a 4 because content that should be split out remains inline and local references are absent.

3 / 5

Total

13

/

20

Passed

Description

53%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 clearly states what the skill does but omits any explicit "when to use" trigger guidance, capping completeness. Trigger terms lean on Apify jargon ("Actors") rather than natural user phrasing.

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 or supplement jargon ("Actors") with natural user terms like "scrape", "extract data", and name the key platforms so trigger matching works.

List 2-3 more concrete capabilities (e.g., profile data, posts, comments, reviews) to lift specificity above 3.

DimensionReasoningScore

Specificity

Names the domain ("data extraction from 55+ Actors across all major platforms") and 1-2 concrete actions ("data extraction", "automatically selects the best Actor"), but the actions are not comprehensive and "data extraction" is generic. Not a 4 because it does not list several specific concrete actions with only minor gaps.

3 / 5

Completeness

Has a clear "what" (AI-driven data extraction across 55+ Actors with automatic Actor selection) but no "Use when..." clause or equivalent explicit trigger guidance, which caps completeness at 3 per the rubric guidelines. Not a 4 because the "when" is entirely absent rather than merely imprecise.

3 / 5

Trigger Term Quality

"data extraction" is a relevant keyword, but "Actors" is Apify jargon rather than natural user language, and common synonyms (e.g., "scrape") and platform names users would actually say are missing. Not a 4 because keyword coverage lacks natural variations.

3 / 5

Distinctiveness Conflict Risk

The "55+ Actors across all major platforms" framing carves a clear Apify-scraping niche with distinct triggers and only minor overlap risk against other scraper skills. Not a 5 because "data extraction across all major platforms" is still broad enough to overlap with general scraper skills.

4 / 5

Total

13

/

20

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

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
Reviewed

Table of Contents

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