SKILLEMALL.ai
GitHub ↗ RU Sign in

BB apify-ads-intelligence

Research, spy on, and analyze ads across Meta (Facebook & Instagram), Google (Ads Transparency Center + paid search results), TikTok (Ads Library + Creative Center), LinkedIn Ad Library, and X (Twitter — promoted tweets, best-effort) using Apify Actors. Use when user asks about competitor ads, ad library research, winning creatives, ad copy analysis, landing page audits from ads, cross-platform ad audits, brand transparency checks, or any task involving paid ad creatives, advertiser data, or ad targeting from public ad libraries.

apify/awesome-skills Agent Skills author: apify 1 file body ≈ 3 991 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Research, spy on, and analyze ads across Meta (Facebook & Instagram), Google (Ads Transparency Center + paid search results), TikTok (Ads Library + Creative…

As a process B 67/100 · Nearly there — weak spots: when it triggers, running it twice, progress reporting

AnalyzerMarketingtype 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
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
B
67/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
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: 1. 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 67/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 4 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 34 steps, 1 vague phrases
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3991 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low The response is described with custom markup (6 tags): a typed call is more reliable

    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 535: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 34 items
    • +3Output format is stated explicitly
    • +4Has examples (6 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.