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AB dataify-api-best-practices

Write, review, or debug Dataify SERP, Web Unlocker, Builder, MCP, or SDK integration code using correct authentication, task lifecycle, retry, error, and output patterns. Do not use for ordinary end-user searches, scraping, research, or account setup.

ClawHub Agent Skills author: dataify-server v1.1.1 MIT-0 20 files body ≈ 1 009 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

Write, review, or debug Dataify SERP, Web Unlocker, Builder, MCP, or SDK integration code using correct authentication, task lifecycle, retry, error, and…

As a process B 65/100 · Nearly there — weak spots: result and completion

IntegrationSoftware developmentInfrastructureAI and agentstype 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
95/100
safety, quality, tests
Safety 60%
99
Quality 40%
89
Run on models
none yet
Process rating
B
65/100
Nearly there
Result and completion w 14
0
When it triggers w 12
50
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-password-literal scripts/task_runtime.py:38
      Hard-coded password / key literal (may be an example)
      api_key = api_key[7:].strip()

    Files scanned: 20. 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 65/100

    • 0Result and completion. Does not say what the result is
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 19 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1009 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • -36 of 7 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 251: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 19 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (10 of 10)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: suspicious
    The skill is mostly a Dataify integration helper, but it includes under-disclosed live scraping and business-intelligence workflows plus insecure token handling that should be reviewed before installation.
    LLM: suspicious (high) · 7 Sept 2026