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AB research-brightdata

This skill should be used when the user asks to "research web data", "scrape websites", "extract web data", "perform market research", "analyze competitors", "monitor prices", "collect product information", "search and analyze web content", or mentions Bright Data MCP, web scraping, web data extraction, or automated research. Provides comprehensive web research workflows using Bright Data MCP tools including search, scraping, extraction, and browser automation capabilities.

ClawHub Agent Skills author: liangdabiao v1.0.0 10 files body ≈ 2 535 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process B 68/100 · Nearly there — weak spots: inputs and preconditions, consistency

ProcedureAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
B
68/100
Nearly there
Inputs and preconditions w 11
0
Consistency w 8
40
Failures and branches w 10
50
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: 10. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 68/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 40Consistency. Frontmatter name (research-brightdata) differs from the folder (bright-data-claude-skill-deep-research)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 128 steps, 3 vague phrases
    • 100Tools and files. No external tools needed
    • 100Execution cost. Instruction body is 2535 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • low 11 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 8 example trigger phrases
    • +3Description length 478: enough signal without eating the budget
    • +4Structure: 46 headings
    • +3Step-by-step instructions: 128 items
    • +3Output format is stated explicitly
    • +4Has examples (6 code blocks)
    • +4Reference files are cited in the instructions (5 of 5)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.

    External checks

    ClawHub: suspicious
    This is a coherent Bright Data web-scraping skill, but it needs review because it can automate logged-in browsing, clicking, and persistent sessions without clear user controls.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026