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AB apify-yandex-search-api

Get structured Yandex search results without an official API key using the Apify Yandex Search Scraper Actor (johnvc/Scrape-Yandex). One query returns the full SERP as JSON, with organic results (position, title, link, displayed_link, snippet), and optional ads, knowledge graph, inline images, and inline videos, targeted by Yandex domain, language, and any of 123,000 plus lr region IDs. Use when the user wants a yandex search api, a yandex serp api, Yandex search results as JSON or CSV, to scrape Yandex search, to check what ranks on yandex.ru for a query or region, or SEO research for Russian-speaking markets. Pay-per-page billing, MCP-ready for Claude and other AI agents.

apify/awesome-skills Agent Skills author: apify 3 files body ≈ 1 799 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Get structured Yandex search results without an official API key using the Apify Yandex Search Scraper Actor (johnvc/Scrape-Yandex).

As a process B 71/100 · Nearly there — weak spots: result and completion, progress reporting

IntegrationAI and agentsData and analyticstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
B
71/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "author_url"

    Process rating: all ten parameters 71/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 30 steps, 1 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1799 tokens
    • 100Running it twice. No mutating operations
    • low 12 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 682: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 30 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.