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CB admapix

AdMapix raw data layer for ad creatives, apps, rankings, downloads/revenue, and market metadata. Returns structured JSON from the AdMapix API; the calling agent composes and analyzes. Use for 搜广告, 找素材, 广告素材, 竞品分析, 排行榜, 下载量, 收入, 市场分析, App数据, ad creative, search ads, app intelligence, rankings, downloads, revenue, market data.

ClawHub Agent Skills author: fly0pants v1.0.30 MIT-0 11 files body ≈ 2 061 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

AdMapix raw data layer for ad creatives, apps, rankings, downloads/revenue, and market metadata.

As a process B 66/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

IntegrationAI 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
C
74/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
53
No improvement model run: 64% with skill · 48% without
Process rating
B
66/100
Nearly there
Progress reporting w 2
0
Inputs and preconditions w 11
30
Running it twice w 4
30
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: 11. 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 66/100

    • 0Progress reporting. Says nothing while it works
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 3 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 5 steps, 3 vague phrases
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2061 tokens

    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
    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +3Description length 326: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 5 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (7 of 7)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    Model run · 7 Sept 2026

    With the skill
    64%
    +16 pp vs baseline
    Without the skill (baseline)
    48%
    triggers: +100% / −100%

    No improvement Pass rate 64% is below threshold 80% · 7 cases · $0.068

    External checks

    ClawHub: suspicious
    The skill is a coherent AdMapix API client, but its install docs use an unpinned npx command that can execute mutable remote installer code.
    LLM: suspicious (medium) · VirusTotal: · 12 Sept 2026