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BB apify-creator-emails

Find creators and their published contact emails in one run. Routes "find YouTube channels about <niche> with emails", "get the email for these TikTok / Instagram handles", "build a micro-influencer outreach list", "creator email finder", "influencer contact list for a sponsorship campaign" and "alert me when new creators appear in <niche>" to a single Actor that discovers YouTube channels by keyword, optionally hops to the Instagram and TikTok profiles those channels link, enriches user-supplied TikTok and Instagram handles, crawls each creator's linked site for an email, MX-verifies it and scores every row 0-100 in one 51-column schema. No login, no API key. Use when the user asks to scrape creator or influencer emails, find YouTube creators by topic, enrich a list of TikTok or Instagram usernames with contact details, or set up a weekly new-creator watch. Does not search TikTok or Instagram by keyword or hashtag.

apify/awesome-skills Agent Skills author: apify 1 file body ≈ 3 504 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Find creators and their published contact emails in one run.

As a process B 76/100 · Nearly there — weak spots: result and completion, running it twice

ProcedureYouTubeSlackMarketingtype 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%
85
Run on models
none yet
Process rating
B
76/100
Nearly there
Result and completion w 14
0
Running it twice w 4
30
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: 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 76/100

    • 0Result and completion. Does not say what the result is
    • 30Running it twice. 9 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 33 steps
    • 100When it triggers. States when to use and when not to
    • 100Inputs and preconditions. Inputs and preconditions are listed
    • 100Failures and branches. 3 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3504 tokens
    • 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 929: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 33 items
    • +4Has examples (4 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.