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ai-web-scraping-scrapegraph

AI-powered web scraping - extract data using natural language prompts

49

Quality

53%

Does it follow best practices?

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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 ./skills/research-tools/capabilities/ai-web-scraping-scrapegraph/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%

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

The skill is a well-structured API reference with mostly executable curl examples, but it suffers from two malformed example payloads, redundant parameter prose, and an implicit async workflow. Splitting the endpoint reference into a separate file and fixing the broken JSON would improve it.

Suggestions

Fix the malformed JSON in the SmartScraper and SmartCrawler curl examples so the body parameters are inside the request body object.

Add an explicit async workflow (start request → poll Get Status with suggested interval → retrieve results) instead of leaving the sequence implicit.

Deduplicate the repeated mock/stealth parameter explanations and move the full per-endpoint parameter reference into a separate REFERENCE.md to reduce inline bulk.

DimensionReasoningScore

Conciseness

The body is mostly actionable API reference, but repeats identical explanations (mock mode, stealth, +4 credits) across endpoints and includes padding like "Useful when you already have HTML content cached", so it could be tightened.

2 / 3

Actionability

Most curl examples are concrete and executable, but the SmartScraper (lines 60-66) and SmartCrawler (lines 120-127) examples have malformed/incomplete JSON with parameters outside the request body, a real defect.

2 / 3

Workflow Clarity

The async start-then-poll-status pattern is implied by paired "Start X" and "Get X Status" sections, but there is no explicit sequence, polling guidance, or validation checkpoints for the request_id workflow.

2 / 3

Progressive Disclosure

Content is organized into Setup/Capabilities/Usage/Use Cases sections, but ~230 lines of per-endpoint parameter reference live inline in SKILL.md with no separate reference files; the only "Discover More" entry is an API call, not a navigable file.

2 / 3

Total

8

/

12

Passed

Description

57%

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 is concise and names a clear niche, but it only implies when to use the skill and lists a single action. Adding a "Use when..." clause and more concrete action verbs would lift specificity and completeness.

Suggestions

Add an explicit trigger clause, e.g. "Use when the user wants to scrape a website, extract data from a page, or convert web content using natural language prompts."

List more concrete actions (e.g. crawl sites, generate sitemaps, convert pages to markdown) to raise specificity.

Include natural trigger variations like "scrape", "crawl", and "web data" to broaden trigger term coverage.

DimensionReasoningScore

Specificity

Quotes "extract data using natural language prompts" — names the web-scraping domain and one concrete action, but does not list multiple specific actions, matching the score-2 anchor.

2 / 3

Completeness

It states what the skill does (extract data via natural language prompts) but has no "Use when..." trigger clause; per the guidelines a missing explicit trigger caps completeness at 2.

2 / 3

Trigger Term Quality

"web scraping" and "extract data" are natural terms a user would say, but common variations like "scrape", "crawl", or "spider" are absent, so coverage is partial.

2 / 3

Distinctiveness Conflict Risk

"AI-powered web scraping" with "natural language prompts" is a distinct niche unlikely to trigger for unrelated skills, matching the clear-niche anchor.

3 / 3

Total

9

/

12

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
gooseworks-ai/goose-skills
Reviewed

Table of Contents

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